The Impact of Various Land Use Strategies on Suburban Mobility
Click HERE for graphic.
Click HERE for graphic.
THE IMPACT OF VARIOUS LAND USE
STRATEGIES ON SUBURBAN MOBILITY
Final Report
Click HERE for graphic.
Prepared by:
MIDDLESEX SOMERSET MERCER REGIONAL COUNCIL
621 Alexander Road
Princeton, NJ 08450
and
HOWARD/STEIN-HUDSON ASSOCIATES, INC.
38 Chauncy Street
Boston, MA 02111
in association with:
ACUTECH, INC.
DOUGLAS AND DOUGLAS, INC.
MOORE-HEDER ARCHITECTS, INC.
NOTICE:
This document is disseminated under the sponsorship of the U. S.
Department of Transportation in the interest of information exchange.
The United States Government assumes no liability for its contents or
use thereof.
The United States Government does not endorse manufacturers or
products. Trade names appear in the document only because they are
essential to the content of the report.
Middlesex Somerset Mercer Regional Council (MSM)
621 Alexander Road, Princeton, NJ, 08540. (609) 452-1717
MSM is an independent, non-profit civic planning and research
organization. Established in 1968, MSM concentrates on land use,
transportation, housing, environmental conservation, and related
issues in the 500-square-mile central New Jersey region situated
between the Delaware and Raritan Rivers, MSM's research and advocacy
programs are supported primarily by individual and corporate members
who share a concern for the future of their region. MSM receives
funding from foundations and the state and federal governments to
carry out special projects.
ACKNOWLEDGMENTS
The preparation of this report has been financed by the Urban Mass
Transportation Administration's Office of Technical Assistance and
Safety, the New Jersey Department of Transportation, the Fund for New
Jersey, and the Hyde and Watson Foundation, and the members of
Middlesex Somerset Mercer Regional Council.
The contents of this report reflect the views of the MSM staff and
their consultants, who are responsible for the facts and accuracy of
the information presented herein. The contents do not necessarily
reflect the views of any of the above funding sources.
The MSM staff and their consultants would like to acknowledge the
assistance and guidance given them by the study's Steering Committee
and Peer Review Panel. In addition, MSM would like to give special
recognition to Edward Thomas, Director of Technical Assistance and
Safety of the Urban Mass Transportation Administration in Washington,
DC, for his vision and support for this project.
The Steering Committee included the following individuals:
Chairman: David J. Goldberg, Esq., Cohen, Shapiro, Polisher,
Sheikman & Cohen, and Chairman, NJ Turnpike Authority
William S. Beetle, New Jersey Department of Transportation
Ronald Berman, Esq., DKM Properties, and MSM Board of Directors
Martin Bierbaurn, Esq., New Jersey Office of State Planning
Hon. Carolyn Bronson, Freeholder, Mercer County
Jack Claffey, Delaware Valley Regional Planning Commission
Hon. David B. Crabiel, Freeholder, Middlesex County
Robert Dunphy, Urban Land Institute
Carl Hintz, AICP, ASLA, Hintz Associates, Inc.
David Knights, Princeton Forrestal Center
Jack Lowenstein, FMC Corporation
William Swain, MSM Board of Directors
Joel S. Weiner, North Jersey Transportation Coordinating Council
Jeffrey Zupan, Transportation Consultant, Regional Plan Association
The Peer Review Panel included the following:
Frederick Ducca, Federal Highway Administration
Robert Dunphy, Urban Land Institute
Kevin Hooper, JHK Associates
Patrick Kane, Architect
Richard Pratt, Consultant
Richard Tustian, Lincoln Institute of Land Policy
Jeffrey Zupan, Transportation Consultant, Regional Plan Association
Staff of MSM who worked on this project included:
Administrative Project Manager:
Dianne Brake, President, MSM
Technical Project Manager:
Melvin R. Lehr, P.E., Principal, M. R. Lehr & Associates, and
Secretary, MSM Board of Directors
Senior Research Director:
Donna Bender, AICP/PP, Vice-President, MSM
Consultant Team included:
Howard/Stein-Hudson Associates, Inc.
Jane Howard, Principal
Arnold J. Bloch, Ph.D., Project Manager
Alfred R. Howard, P.E., Sr. Project Engineer
Douglas & Douglas, Inc.
G. Bruce Douglas, Ph.D., P.E., Principal
Barry Zimmer, Transportation Planner
Acutech, Inc.
Ruby Siegel, President
Heder Architects, Inc.
LaJos Heder, Principal
Staff members of the Bureau of Local Transportation Planning of the
New Jersey Department of Transportation also made important
contributions to this project. MSM gratefully acknowledges the
technical and administrative assistance of:
William S. Beetle, Manager, Bureau of Local Transportation Planning
Helene K. Rubin, PP/AICP, Principal Planner, Bureau of Local
Transportation Planning
James B. Lewis, PP, Supervising Planner
James Pivovar, Manager, Bureau of Transportation and Corridor Analysis
THE IMPACT OF FUTURE LAND USE SCENARIOS
ON SUBURBAN MOBILITY
EXECUTIVE SUMMARY
MSM Regional Council and its team of technical consultants have
completed an 18-month study on the interaction between suburban land
use trends and regional traffic conditions. The results of the study
verify what had previously been only a theoretical viewpoint: that
concentrating new suburban development into higher density, mixed-use
centers will slow the growth of regional vehicular use.
The study tested the traffic impact of locating the region's new
employees in Trenton and New Brunswick, as well as in tightly
clustered suburban employment centers. Under scenarios proposed in
the study, new residents would work and shop closer to their homes.
Their living environment would be conducive to walking and reduced
auto use. Those who still commute longer distances would have transit
and ridesharing opportunities available to them, and a significant
number would take advantage of these choices because of incentives
provided by regional demand management policies. The study
demonstrated that this approach to land use would create a significant
reduction in the growth in traffic.
Background:
MSM began this study in the summer of 1989 by reviewing the
published data on the relationship between suburban development and
transportation, as well as by evaluating various analytic tools for
the study. A consultant team joined MSM in February 1990, and a
steering committee and peer review panel comprised of transportation
and land use professionals (listed in Acknowledgments) provided
oversight for the project.
Constructs of Higher Density, Mixed-Use Centers:
The study team developed and tested three models -- or "constructs"
-- of higher density, mixed-use centers designed to fit within the
suburban setting of the MSM region. These constructs incorporated
residential and employment growth expected in the region by 2010 -- a
30 percent increase in population (187,905 new residents) and a
dramatic 54 percent increase in employment (182,581 new jobs) -- but
reshaped that growth into different land use configurations. The new
growth was located in the cities and in a small number of newly
created suburban centers instead of in low density developments spread
throughout the region.
Three construct types were used: a Transit Construct, a dense
development that could house a minimum of 12,000 people and employ
over 13,000, while maximizing transit, ridesharing and walking access;
a Short Drive Construct, a somewhat less dense area of at least 6,700
residents and 9,500 employees, with ridesharing and walking as the
main travel alternatives to the single occupant vehicle (SOV); and a
Walking Construct, a dense, pedestrian-oriented residential village of
about 4,500 persons with only minimal service and retail employment
opportunities.
Developing a Transportation Modeling Procedure:
A transportation modeling package called TransCAD was used for its
capacity to incorporate important land use elements in a Geographical
Information System (GIS). This allowed the project team to utilize
transportation models similar to those used in prior regional studies
(e.g., Route 1 Corridor Study, NJDOT, 1986) in combination with land
use/demographic data bases and models that will have long-range
applications for MSM, the counties, and the municipalities.
A key part of the modeling process was to determine quantitatively
how much less auto travel could be expected from the constructs.
Using case study data, the study team determined that Transit
Constructs would create 28 percent fewer vehicle trips than the same
amount of development dispersed in less dense, single-use
configurations. For Short Drive and Walking Constructs, the
corresponding numbers were 24 percent and 18 percent fewer vehicle
trips, respectively.
Scenarios and Results
Two scenarios were developed. Scenario 1 assumed that all new
regional development between the year 1988 and 2010 would be
distributed in two ways. First, much of it would be absorbed into
suburban constructs located throughout the region. Second, a major
resurgence of growth would, occur in Trenton and New Brunswick. In
Scenario 2, no major resurgence of the region's cities was assumed.
Instead, all growth would be absorbed into the suburban constructs,
making them larger than those in Scenario 1.
The results for two key criteria are described and displayed in the
discussion below.
Vehicle Trips
The figure on the right examines the growth rate of vehicle trips
occurring in the suburban portion of the MSM region between 1988 and
2010. Under "non-construct," trend conditions, new daily vehicle
trips in the suburban area would be expected to grow by nearly 1.8
million. In Scenario 1, the combination of constructs and strong urban
growth reduces that suburban growth to under 700,000 daily trips.
In Scenario 2, where there is no significant new urban growth, new
suburban vehicle tripmaking still declines to about 1.2 million daily
trips.
When adding the large number of existing trips to these varying
levels of new trip growth, the results for 2010 are as follows:
- There would be 18 percent fewer total daily suburban vehicle
trips in Scenario 1, compared to the trend;
- and 10 percent fewer total daily suburban vehicle trips in
Scenario 2, compared to trend.
Click HERE for graphic.
Vehicle Miles Traveled
As seen at right, the growth of new vehicle miles traveled (VMT) on
the suburban regional highway network declines in the alternative
scenarios. Under trend conditions, VMT grows by about 300,000 miles
during the morning peak hour trip to work. Under Scenario 1, the
growth of AM peak hour VMT is under 170,000 miles. In Scenario 2, the
growth is slightly more than 200,000 miles.
When the existing VMT are added to these varying levels of new VMT
growth, the results are as follows:
- In the year 2010, there would be 12 percent less total VMT
in the morning peak under Scenario 1, compared to the trend;
- and 9 percent less total VMT in Scenario 2, compared to the
trend.
Click HERE for Graphic
Conclusions
Four basic conclusions can be drawn from the analyses performed in
this study:
1. Mixed-use centers can produce significant regional
transportation benefits.
2. Mixed-use centers are a viable concept for suburban settings.
3. Mixed-use centers, through design and function, can have
tangible transportation benefits at the site.
4. Promoting strong urban growth along with suburban mixed-use
centers gives the best regional transportation results.
Note: These dramatic results are based on the assumption that all
new development locates in cities or in higher density, mixed-use
constructs. Only to the extent that we can change our current land
use patterns, will we approach these results. Success within the
next twenty years is unlikely because of the number of new
developments in the region that already have planning permits for
traditional, low density, single-use patterns. Success in the
future will be achieved by carefully planning uncommitted lands and
by redeveloping existing sites over a much longer period of time.
Next Steps:
In this study, the project team has worked to see whether higher
density, mixed-use suburban development can achieve traffic impact
reduction on a regional level. The conclusion is that indeed it can.
During the next phase of our Land Use/Transportation Study, once again
funded by the Urban Mass Transportation Administration, MSM will
present this evidence to local officials, employers, developers, and
residents and relate it to their efforts to achieve the goals and
objectives of the New Jersey State Development and Redevelopment Plan
and the federal Clean Air Act. Phase Two is expected to be completed
by December, 1992.
Financial and time constraints on the first phase of the study
forced the project team to ignore several key technical issues. Our
regionwide trip generating formulas concentrated on suburban practices
and do not provide a good reflection of urban tripmaking conditions.
During the next phase of study, in order to understand better the full
regional and subregional consequences of constructs and strong urban
growth, new formulas will be developed and urban area vehicle trip
reduction factors devised. In addition, a more detailed network and
zone structure for the urban areas will be built to better distribute
tripmaking within and around the periphery of the cities.
THE IMPACT OF FUTURE LAND USE
SCENARIOS ON SUBURBAN MOBILITY
TABLE OF CONTENTS
Page
I. INTRODUCTION
A. Impetus for the Study 1
B. The Study Area 2
C. Goals and Objectives of the Study 2
D. Methodology 2
1. Study Participants 2
2. Study Process 4
II. BUILDING BASIC CONSTRUCTS OF MIXED-USE CENTERS 7
A. Suburban Development Trends and Alternatives 7
1. The Constructs as Alternatives to Present Development Trends 7
2. The Problems with Existing Development Trends 7
3. The Princeton Forrestal Center Area: An Attempt to Achieve
Mixed-Use Center Objectives 7
4. Why Propose Alternative Development Patterns? 8
B. Defining Alternative Development Patterns: The Construct
Approach 11
1. Three Basic Construct Types 11
2. Urban Design Components of the Three Constructs 16
3. Key Characteristics of the Constructs 22
C. The Role of Constructs in Reducing Vehicle Traffic: Local Level
Analysis 24
1. Assumptions 24
2. Methodology 25
3. Construct Land Use and Transportation Relationships 25
4. Determination of Vehicle Trip Reduction Factors 26
5. Producing a Vehicle Trip Reduction Factor: An Example 29
III. DEVELOPING THE REGIONAL TRANSPORTATION MODEL 32
A. Basic Components of the Regional Transportation Model 32
1. Building the MSM Network with Reliance on Previous Efforts 32
2. Using the GIS-Based TransCAD Package 32
3. Accounting for the Traffic Reduction Effects of Construct
Development in the Regional Model 33
B. Building the MSM Network with Reliance on Previous Efforts 33
1. Building the 1988 Network 33
2. Building the 2010 Network 35
3. Building Traffic Zones 36
4. External Trips 36
C. Using the GIS-Based TransCAD Package 36
D. Accounting for the Traffic Reduction Effects of Construct
Development in the Regional Model 37
IV. FORECASTING DEVELOPMENT SCENARIOS 39
A. Developing 1988 Baseline Conditions 39
B. Year 2010 Trend Conditions 39
C. Alternative Development Scenarios 41
1. Scenario 1: Constructs and Major Urban Growth 41
2. Scenario 2: Constructs With Only Trend Urban Growth 43
V. ANALYZING THE TRANSPORTATION IMPACTS OF CONSTRUCT SCENARIOS 49
A. Defining the Study Area 49
B. Regional Impacts of the Scenarios 49
1. Total Vehicle Trips on the Regional Network 49
2. Total Vehicle Miles on the Regional Network 51
3. Travel Speeds 55
4. Travel Time 56
VI. CONCLUSIONS AND NEXT STEPS 58
A. Conclusions 58
1. Mixed-Use Centers Can Produce Significant Regional
Transportation Benefits 58
2. Mixed-Use Centers are a Viable Concept for Suburban
Centers 59
3. Mixed-Use Centers, Through Design and Function, Can Have
Tangible Transportation Benefits at the Site 60
4. Promoting Strong Urban Growth Along with Suburban Mixed-Use
Centers Gives the Best Regional Results 60
B. Next Steps 60
1. Technical Improvements to the MSM Model and the Regional
Network 61
2. Quantifying the Public and Private Costs and Benefits of the
Study Findings 61
3. Seeking Public Support for Changing Regional Development
Patterns 62
REFERENCES
APPENDICES:
Appendix A. Calculation of Vehicle Trip Reduction Factors for Walking,
Transit, and Short Drive Constructs
Appendix B. MSM Region Traffic Zones and 1988 Calibration Network
Appendix C. TransCAD Package Steps and Trip Generation Equations
Appendix D. Development of Land Use Data for Municipalities and Zones
Appendix E. MSM Employment and Housing Projections, Vehicle Trip
Productions and Attractions, Daily Trip Ends, and
Jobs/Housing Ratios: 1988, 2010 Trend, Scenario 1,
Scenario 2
Appendix F. Vehicle Trips, Speeds, and Vehicle Miles of Travel for
Study Area Municipalities: 1988, 2010 Trend, Scenario 1,
Scenario 2
Appendix G. Suburban Mixed-Use Centers and Transportation: Current
Research and Issues
MSM Regional Council Report, June 1990
LIST OF TABLES
Page
Table 1: MSM Land Use Construct Comparison 23
Table 2: Summary of Trip Reduction Factors 30
Table 3: 1988 Baseline Conditions for MSM Region Municipalities 40
Table 4: 2010 Trend Conditions for MSM Region Municipalities 42
Table 5: Location of Constructs in Both Scenarios 1 and 2 45
Table 6: Current and Projected Employment Under Different Scenarios 47
Table 7: Current and Projected Households Under Different Scenarios 48
Table 8: Vehicle Trips in the MSM Construct Study Area 52
Table 9: A.M. Peak Hour Vehicle Miles Travelled (VMT) in the MSM
Construct Study Area 54
Table 10: A.M. Peak Hour Vehicle Speeds in the MSM Construct Study
Area 55
Table 11: A.M. Peak Hour Vehicle Travel Minutes in the MSM Construct
Study Area 56
LIST OF FIGURES
Page
Figure 1: The MSM Region 3
Figure 2: Representative Recent Development - Forrestal Center 9
Figure 3: Forrestal Village - Existing Development 10
Figure 4: Transit Construct City, Diagram 13
Figure 5: Short Drive Construct City Diagram 14
Figure 6: Walking Construct Village Diagram 16
Figure 7: Transportation Components of Constructs - Generalized 17
Figure 8: Diagrammatic Cross Section - Town Center 18
Figure 9: Diagrammatic Cross Section at Railroad Station
and Main Street 19
Figure 10: Diagrammatic Cross Section - Highway Edge 20
Figure 11: Ratio of Construct Total Trips Compared to Same Construct
with Trend Rate 31
Figure 12: Chart of Study Process 36
Figure 13: Location of Constructs in the MSM Region 44
Figure 14: Employment and Household Projections for Trenton and New
Brunswick for the Year 2010 under Scenarios 1 and 2 46
Figure 15: Growth in Daily Trip Ends 1988 to 2010 - MSM Construct
Study Area: Trend Versus Alternative Development
Scenarios 50
Figure 16: Growth in AM Peak Hour Vehicle Miles of Travel 1988-2010 -
MSM Construct Study Area: Trend Versus Alternative
Development Scenarios 53
Figure 17: Growth in Travel Time 1988-2010 (Vehicle Minutes of
Travel) - MSM Construct Study Area: Trend Versus
Alternative Development Scenarios 58
CHAPTER 1:
INTRODUCTION
A. Impetus for the Study
The continued growth of the nation's suburban areas as residential
and employment centers places a strain on the transportation
infrastructure and services available in these areas. As the 1989
report by the Institute of Transportation Engineers entitled A Toolbox
for Alleviating Traffic Congestion pointed out, the growing trend of
suburban congestion is due to 1) more people traveling in metropolitan
areas (with most of that growth occurring in suburban settings); 2)
more people traveling by car (and, overwhelmingly, in single occupant
vehicles); 3) more people traveling to dispersed locations; and 4)
more people traveling where necessary highway capacity has not been
provided.
The strain that this creates is manifested by added energy use and
regional air pollution, added congestion and delay; and the increasing
conflict between preserving suburban/rural lifestyles and the need for
more highway capacity and traffic controls.
Suburban growth represents a 40-year trend, and there is no
expectation of any significant reversal leading to reconcentrated
urban areas. In that light, the focus among planners has turned to
determining how to redistribute and redesign suburban development to
conserve open lands, preserve the unique local character of villages
and towns, and reduce growth in traffic congestion, while continuing
to serve the diverse needs of residents and employees.
In its April, 1989 report to the Urban Land Institute entitled
Suburban Mobility and Growth Management: Initiatives in Central New
Jersey, Middlesex Somerset Mercer (MSM) Regional Council concluded
that "concentrating growth in higher density, mixed-use centers" would
be "expected to reduce the growth in vehicular traffic" in this
suburban New Jersey setting. The report pointed out that
concentrating growth would create other related advantages:
- The reduction of highway congestion by internalizing trips
within mixed-use areas;
- making transit or paratransit more feasible; and
- reducing the length of necessary trips.
The report acknowledged that "the real impact of these centers on
traffic reduction has yet to be tested." The MSM Land
Use/Transportation Project provides the evidence to document the
transportation advantages of centers.
1
B. The Study Area
The MSM region served as the study area for the Land
Use/Transportation Project. It is a 523-square mile area, consisting
of 32 municipalities covering all of Mercer County and the southern
portions of Middlesex and Somerset Counties in central New Jersey (see
Figure 1 on page 3). Virtually halfway between New York City and
Philadelphia, the MSM region is largely suburban, although its
northeast and southwest borders are anchored by the cities of New
Brunswick (about 40,000 people) and Trenton (about 90,000 people),
respectively. The Borough of Princeton (about 12,000 people) is at
the center of the MSM region.
The MSM region is bisected -- northeast to southwest -- by Route 1,
a four-lane regional commuter highway characterized by some strip
development, stop lights, shopping centers and office parks. New
Jersey's Department of Transportation has a long-term plan to improve
Route 1 to six lanes and to replace most of the lights with grade-
separated intersections. The Northeast Corridor Rail Line, used both
by New Jersey Transit commuter trains and AMTRAK intercity lines,
parallels Route 1.
In 1988 - the year used in this report as the base year because of
data availability - it was estimated that the region included more
than 617,000 residents and nearly 338,000 jobs (source: New Jersey
Department of Labor). Growth by the year 2010, as projected in the
1989 New Jersey Preliminary State Development and Redevelopment Plan,
is dramatic -- 187,905 new residents (a 30% increase), and 182,581 new
jobs (a 54% increase).
C. Goals and Objectives of the Study
The goal of the MSM Land Use/Transportation Study was to rigorously
test the concept of higher density, mixed-use centers in the suburban
setting, in order to assess the type and level of transportation
benefits that might occur.
The specific questions that this study addressed are as follows:
- Can higher density, mixed-use centers produce noticeable,
beneficial effects on the regional highway network, when
compared to the effects of typical single purpose suburban
development as characterized by current trends?
- What intensities of development and mixes of land use patterns
can realistically be developed that reduce vehicular trips made
to, from and within the centers?
- Can higher density, mixed-use centers be located realistically
in the MSM region, given expected growth in employment and
population levels?
D. Methodology
1. Study Participants
The study was conducted in a collaborative effort by MSM Regional
Council, its consultant team, and staff members of the Bureau of Local
Transportation Planning of the New Jersey Department of Transportation
(see Acknowledgments).
A steering committee was created early in the study and was
convened four times during the course of the study (November 27, 1990;
June 13, 1990; January 23, 1991; and April 10, 1991). The committee
had the opportunity to review and comment on interim products, as well
as to ask questions of and make comments to the project team at the
committee's meetings.
2
Click HERE for graphic.
3
In addition, a peer review process was built into the study at two
important junctures of the project. First, on May 14-15, 1990, a
meeting was held between the project team and a peer review panel. At
this meeting, the overall methodological direction of the study was
discussed, highlighting the following key issues (discussed in detail
later in this report):
- The TransCAD software used for modeling transportation impacts;
- The "constructs" and "scenario" approach for testing land use
patterns;
- Site planning to reduce vehicular use; and
- Travel demand management policies and effectiveness.
At the second juncture - during November and December of 1990 -- a
key interim document describing the capabilities of constructs to
reduce single occupant auto tripmaking was circulated for comment
among peer reviewers (Appendix A).
The comments of the peer review panel, as well as steering
committee members, were a valuable resource to the project team during
the course of the study.
2. Study Process
The study consisted of five major tasks, which are briefly
described below and described in more detail later in this report.
a. Suburban Mixed-Use Centers and Transportation: Current
Research. (Appendix G)
To test the hypothesis that concentrating growth in mixed-use
centers would yield regional transportation benefits, the project team
began by exploring published research for evidence of interaction
between land use and transportation in general, and more specifically,
the travel behavior associated with different facets of existing
suburban mixed-use centers. Documented parameters for mixed-use
centers, such as proper density, scale, design and mix of activities,
were gathered as an empirical foundation for the analysis.
In addition, effective demand management techniques were examined
to determine the extent to which the benefits of changing land use
might be enhanced by implementing transportation management programs
(a reciprocal enhancement was expected).
Although the literature search did not uncover any hard and fast
rules, a number of case studies emerged which served as the basis for
crafting the prototype mixed-use centers.
b. Building Basic Constructs of Mixed-Use Centers. (Chapter II)
The theoretical concept of a higher density, mixed-use center was
formalized into a set of land use models, or "constructs." These
constructs were meant to be ambitious, yet realistic representations
of suburban centers which include good planning and design features,
especially a pedestrian environment while meeting the region's needs
for residential and employment growth.
Three types of constructs were formulated:
4
- The Transit Construct: A high density, mixed-use center with a
high concentration of employment. It is designed to maximize
the use of transit services and provide significant pedestrian
amenities.
- The Short-Drive Construct: A high density, mixed-use center,
somewhat lower in density than the transit construct, but also
with a high concentration of employment. Although there are
minimal transit services, there are significant pedestrian
amenities in this construct as well.
- The Walking Construct: A tightly clustered, mixed-use village or
town, with a high level of residential development and only
minimal employment opportunities.
c. Modeling the New Land Use/Transportation Relationships
(Chapter III)
A regional transportation model was developed for the purpose of
testing the effects of the constructs on travel in the MSM region.
The typical modeling system has four steps: 1.) trip generation: uses
formulas to generate total trips; 2) distribution: distributes trips
throughout the region; 3) mode split: defines the proportion of trips
using different forms of transportation; and 4) assignment: it assigns
vehicle trips to appropriate routes for traveling from place to place.
The modeling system used in this study is the TransCAD software
package which combines a geographic information system (GIS) with a
traditional four-step transportation planning model. This GIS
capability has a number of benefits. It provides numerous procedures
for processing land use data, constructing and subdividing traffic
zones, calculating the precise location and adjustment of
transportation network links, and summarizing traffic characteristics
by geographic area. It is also capable of storing present and future
land use and demographic data at the parcel, census block and
municipality level, a feature which is attractive to the long-term
planning efforts of MSM.
The modeling system was further adjusted by consideration of some
key tripmaking characteristics of the constructs, as distinct from the
other subareas of the region. For the region as a whole, auto trip
generation rates were developed using formulas developed by previous
NJDOT studies in and around the MSM region. But these rates were
adjusted for the different construct types -- based on case studies
and the team's planning judgment to develop "trip reduction factors" -
- to reflect the enhancing effect of density, demand management, mixed
uses and transit services on reducing regional auto use to and from
these constructs.
d. Forecasting Development Scenarios (Chapter IV)
A 1988 baseline of employment and population conditions in the MSM
region was established. A forecast year of 2010 was selected for
evaluation and a "2010 Trend Scenario" was developed, projecting
conditions similar to those in the base year to the year 2010. These
forecasts represent the trend of what is likely to occur in land use
and transportation conditions without any change in policy direction.
In addition, two alternative land use scenarios were developed for
the year 2010 to compare with the trend:
Scenario 1: a combination of suburban development in constructs and
increased employment and population growth in the rep-ion's major
cities;
5
Scenario 2: the replacement of all trend suburban development with
development in suburban constructs, and only trend growth in the
cities.
The two scenarios differ by the amount of growth which is allocated
to urban vs. suburban areas.
e. Analyzing the Transportation Impacts of Construct Scenarios
(Chapter V)
The impact of construct vs. trend development was analyzed,
focusing on four key indices of transportation conditions at the
regional and subregional level:
- The number of vehicle trips;
- The level of vehicle miles traveled (VMT);
- The level of delay experienced; and
- The average speed.
These measures were then assessed in aggregate terms -- what
happens in the suburban portion of the region overall -- and in
disaggregate terms, for their effects on suburban municipalities.
6
CHAPTER II:
BUILDING BASIC CONSTRUCTS OF MIXED-USE CENTERS
A. Suburban Development Trends and Alternatives
1. The Constructs as Alternatives to Present Development Trends
The constructs were devised as a means for exploring and
illustrating alternative development patterns for central New Jersey.
The dominant features of recent growth are large, single-use private
developments: office parks, shopping centers and subdivisions. These
developments are planned only within their property boundaries, and
are related to each other only by existing road connections, and are
almost entirely limited to automobile access.
The basic premise of the study is that integrated, multi-use and
better planned development can significantly reduce auto travel needs.
Underlying this premise are basic convictions that more integrated
land use planning and design is both desirable in terms of aesthetic,
social and environmental goals, and marketable to developers and
consumers.
2. The Problems With Existing Development Patterns
The rapid growth of the 1980's tended to create large-size
single purpose developments on assembled tracts of previously rural
land. These suburban developments - office parks over 6 million
square feet, shopping centers approaching 1 million square feet,
residential complexes over 3,000 units -- are much larger in scale
than the existing fabric of small towns in the area. They lack an
effective integration of uses and have no community framework to
support them.
This land use pattern forces total dependence on automobile travel.
By maximizing the need for cars and parking spaces at each
destination, this pattern causes each facility to be surrounded and
isolated by roads and parking lots, thereby reducing accessibility by
walking, transit or bicycle. These single function private
developments, although the size of small towns, lack a town's public
institutions such as schools and government facilities. The resulting
absence of public spaces and foot traffic not only aggravates
transportation problems, but prevents the evolution of community life.
3. The Princeton Forrestal Center Area: An Attempt to Achieve
Mixed-Use Center Objectives
The Princeton Forrestal Center is a major multi-use center owned
by Princeton University that, in 1975, set the standard for
development along the Route 1 Corridor. The center was selected by
the project team to illustrate some key design issues for this study.
The center is known for its ecologically sensitive site planning, as
well as its excellent examples of architectural design. It contains
all three of the major land use functions -- office, retail and
residential -- and has the potential for creating a more integrated
community environment such as that presented in the constructs.
7
Forrestal Village, a retail and office development within
Forrestal Center, offers a concrete illustration of how the
comparative advantages of mixed-use constructs can be evaluated
against the best efforts of single function development. In addition,
Forrestal Village represents a movement toward a mixed-use and town
center type environment, and, although it does not fully incorporate
the concepts of integrated land use proposed in the constructs, it can
provide some useful lessons.
The plan of the Forrestal Center area contains three basic
elements (as shown in Figure 2):
- The Forrestal Center office park, with 4.9 million square feet
of space already completed, and an eventual 8.6 million square
feet it build-out;
- Princeton Landing and several other residential clusters (the
latter not part of the development, but physically proximate)
totaling about 1,200 dwelling units;
- Forrestal Village, a regional shopping center with upper-floor
offices and a hotel, totaling about 1.5 million square feet of
which 822,000 square feet has been built, with the remainder
designated as office space.
The office buildings are driving oriented. The housing
complexes are exclusively residential, with only minimal community
recreation facilities, and are only accessible at a minimal number of
points. Even though the distances among the various facilities are
not great (many under a mile), there are no local connections other
than a very few regional roads.
Forrestal Village embraces some of the ideas of mixed-use
developments and traditional pedestrian- oriented town centers. It
contains a "Main Street," a 'Village Square" and a "Market Plaza." Its
environment fairly convincingly recreates the environment of
traditional town centers. In appearance the town center and main
street in one of the constructs might be very similar.
An aerial view of Forrestal Village (Figure 3) reveals a very
different place. It is isolated in a sea of parking lots and,
although it is located on a huge overpass of Route 1, it is virtually
inaccessible from anywhere else. The "Main Street" and "Village
Boulevard" terminate in parking lots within a block of the center.
Although an attempt was made to provide walkways and bikepaths, it is
inconvenient to walk or bike to the office park or the residential
neighborhood. There is no school or city hall nearby. Forrestal
Village is revealed from this view as simply a regional shopping
center with the marketing theme of a "village" without the urban
design and land use connections to make it real.
4. Why Propose Alternative Development Patterns?
The causes of the development trend favoring large single
function compounds are easy to trace. Land use regulations, created a
century ago to protect residential property from noxious industry,
generally favor single purpose zoning. In addition, developers and
the financing institutions they depend upon tend to develop their
business expertise in one functional area (i.e., housing, office parks
or shopping centers) and for the most part do not welcome the
complexities of mixed-use town development.
New regulatory measures have been enacted in towns in the region
to reduce the impact of these large developments on their environment
and infrastructure. But there has been little effort to change the
underlying zoning to encourage new developments to enhance the
existing community or to become a complete community in their own
right.
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As shown through the analyses and reports produced for the
emerging New Jersey State Development and Redevelopment Plan, and the
1987 MSM REGIONAL FORUM, such large, single function development
patterns consume enormous amounts of land, tax the transportation
infrastructure through their auto dependence, force up the cost of
housing, and degrade the environment and community character of the
region. Both planning documents call for a regional approach to
growth management and the creation of regional mixed-use centers as an
alternative development pattern.
The professional planning community is now promoting many of
these changes under the banner of "neo-traditional" planning
techniques. However, the federal Clean Air Act, with its powerful
mandate to reduce vehicle miles traveled (VMT), as well as auto
emissions, will force New Jersey regulators - under threat of losing
major federal funding - to use land use plans to help achieve these
targets. Demand management techniques, largely an effort to mitigate
the damage that auto-dependent land use patterns have created, will
not be successful enough on their own. The underlying land use
patterns must change as well.
B. Defining Alternative Development Patterns: The Construct Approach
1. Three Basic Construct Types
In this study, the construct approach was adopted to show that,
as an alternative to current land use trends, reasonable models of
higher density, mixed-use centers could fit within the geographical
and socioeconomic settings of the suburban MSM region. The constructs
take into account that there is a continuing demand for residential
and employment opportunities within the region, albeit at a slower
pace than in the 1980's. They also take into account some basic
transportation assumptions of the region, namely:
- The automobile will remain the dominant mode of travel for
employees and residents.
- Because of the proximity of the NJ Transit/AMTRAK rail line and
the relative proximity of New York City and Philadelphia,
employees and residents have some receptivity to transit
services.
- There is a basic familiarity with ridesharing, particularly for
commuting purposes.
- Polls have demonstrated that people like the pedestrian
amenities and opportunities that "small town' aesthetics offer.
These attributes were accepted by both the steering committee and
the peer review panel.
Three basic construct types were identified to represent three
transportation environments: the Transit Construct, the Short Drive
Construct, and the Walking Construct. These are further defined
below.
a. The Transit Construct
This construct represents the largest, densest and most complex of
the three construct types. It is anchored between a transit hub
(e.g., a rail station or convenient bus route) and a major highway.
(See Figure 4.) Commercial and residential land uses are mixed to
provide a jobs/dwelling unit ratio of at least 2.18.
11
The Transit Construct shows a high density concentration of
employment and transportation services near a rail station, and a
second high density of employment and retail activity at the highway
connection point. The Main Street of the Transit Construct and its
access roads connect the two transportation nodes and create a
pedestrian and transit focused spine. Transit facilities may include
shuttles along Main Street and regional and local collector bus
service providing service from the residential areas to the employment
facilities and the Transit Hub.
The focal point of the Transit Construct is the Town Square, which
is near the construct's geographic center and houses its primary local
institutions and civic facilities.
As found in the other two constructs, the Transit Construct, as do
the other two constructs, has strong public and private sector demand
management policies in place. It has restricted, preferential parking
and a transportation coordinator on site.
b. The Short Drive Construct
The Short Drive Construct has a structure similar to the Transit
Construct, but is somewhat less dense and lacks direct access to a
transit hub as a second transportation anchor (see Figure 5). Main
Street still acts as an important spine, but now it is shorter and
only connects the Regional Shopping and Market Square area of the Town
Center.
Since the Short Drive Construct is not well served by convenient
public transit, the denser residential areas are clustered near enough
to the center to permit access on foot. The less dense parts are
spread somewhat further and require a short drive to shopping and
employment opportunities either by private auto or shuttle buses. The
jobs/housing ratio here is 3.39.
In comparison to an ordinary office park, a reduction in trips in
the Short Drive Construct is produced by having more housing and
retail services near the employment site and by the use of strong
demand management policies. There is restricted and preferential
parking, and a transportation coordinator on site.
c. The Walking Construct
The Walking Construct is basically a higher density residential
village, with minimal employment opportunities, located off the main-
highway network. It is sufficiently compact to permit access on foot
to the center from most of the residential areas (see Figure 6). The
cluster pattern of the neighborhoods facilitates vanpools and
ridesharing to regional employment centers.
The Town Square is the focus of this more limited mixed-use area
and is almost completely locally oriented. If the Primary Connecting
Road is not overwhelmed by high speed traffic and can bring some
additional clientele from surrounding communities, the Town Square may
develop into a kind of Main Street. Many of the existing village
centers could evolve into this pattern. While there is some
commercial employment within the walking construct, its jobs/housing
ratio is only 0.14.
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2. Urban Design Components of the Three Constructs
The Transit Construct, the Short Drive Construct and the Walking
Construct show basic differences of size, scale, organization, focus
and pattern. On the other hand, all three represent a major departure
from prevailing patterns of development and are made up of similar
components of successful urban design for viable towns with a full
complement of community functions. These components are in most ways
traditional prototypes drawn from successful cities and towns of the
past, updated to accommodate today's functional requirements.
The visual imagery of these components can vary. The key to
success is that the basic density and functional layout requirements
needed for a sound transportation and land use plan are accompanied by
massing, zoning, and street environment concepts that support a
pedestrian environment and the community life of the town. Thus, we
illustrate general scale, proximity and massing relationships on the
plan and cross section diagrams (Figures 7-10), but avoid advocating
particular architectural vocabularies.
The following are some of the key design components. Refer to the
plan and cross section diagrams for their illustration.
- Streets: To function properly, streets must be committed to
full-time civic use. By contrast, malls, drives, cul-de-sacs,
and other contemporary devices tend to serve single, semi-
private purposes and restrict the public life of a town. The
best streets allow for some mix of livable and interesting uses,
such as cars, pedestrians, service vehicles, bicycles, baby
carriages, etc.
The use of the street and adjacent relationships of private
properties should be regulated by public code. Grids of streets
serve multiple functions and civic purposes by creating an open-
ended, continuously connected system with enough redundancy to
be adaptable and flexible.
The actual shape of the open grid can vary with topography,
density, and design intent, but its basic integrity should be
consistently maintained. Older, traditional towns have many
examples of successful streets.
- Main Streets: The traditional center of American cities and
towns is "Main Street," characterized by a mix of uses and
transportation modes and a high level of pedestrian activity and
interaction. Dense, mid-rise buildings (3-5 story) with retail
uses on the ground floor, and small offices, workshops and
apartments on the upper floors usually create the right mix.
The scale and density of the "Main Street" at Forrestal
Village would be quite appropriate for the constructs. However,
unlike the one at Forrestal Village, Main Street needs to be
connected to and become the focal point of the street grid in
order to attract pedestrians from surrounding neighborhoods.
Vehicles should be allowed on Main Streets, but their volume and
speed controlled to maintain a pedestrian orientation.
Main Street should connect to the principal squares of the
town and should be within walking distance from most residential
blocks. In the Transit Construct, shuttle transit should run
along the length of Main Street.
- Squares: Squares are special spaces in the street network where
functional, civic, recreational, and ceremonial activities of
the city or town can be focused. In the larger constructs, the
functions can be split -- i.e., one square devoted primarily to
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institutions, another to markets, a third to transportation -
but these definitely need to be in close relationship to each
other. Pedestrian emphasis and connection among the squares is
essential.
- Major Connector and Service Roads: The size and density of
settlements considered for the constructs creates a great deal
of auto traffic bound for highly concentrated employment, retail
and transit centers. For this reason, roads should be
designated in the grid to handle primary traffic and give access
to the main parking garage concentrations., In the two larger
constructs, these connector roads should be separate from Main
Street and not have major pedestrian or retail concentrations at
street level.
Generally, at the scale of these settlements, traffic signal
timing and other management techniques, rather than grade
separation, should be used to insure adequate flow along these
roads. The plan diagrams and Town Center cross section
illustrate the relationship of these roads to the other elements
of the grid, land uses, and parking areas.
- Parking Design: The large amount of area required for parking in
these towns where employment and retail are concentrated
(roughly a 1:1 ration of space devoted to parking and all other
uses), necessitates a very careful design approach to parking.
It is assumed for the constructs that in order to create the
density and continuity required for mixed-use centers, most of
the parking for employment and Main Street related activity will
be in multi-level structures. This will be an economic burden
for the developers, but in recent developments--such as
Forrestal Village, Carnegie Center in West Windsor and the
proposed Metroplex office park in South Brunswick--have set the
precedent by including multi-level parking garages.
The key design principle is to make these parking structures
easily accessible from the main connector and service road, but
to prevent them from dominating the streetscape of Main Street,
the Squares, or the residential streets. Ideally, garages
should be located at the center of commercial blocks, faced with
stores at the ground level and other uses above.
Parking for the residential areas should generally be
absorbed in driveways, garages, or carports on a small scale
directly adjacent to the units, as shown in the site diagrams
and Town Center cross section. But controlled street parking
should not be prohibited.
- Residential Neighborhoods and Streets: Neighborhoods need a
greater level of privacy and protection from heavy traffic than
other, more public uses. Residential streets can be designed to
enhance, but not dominate the neighborhood, and still remain
connected to the public street grid that ties the town or city
together.
Traffic Management should insure that these streets carry
primarily local traffic at low speeds. Front doors and parking
and front doors should generally occur at or near the street to
keep an active community character. Density, proposed in the 10
to 15 dwelling units per acre range (on average), should be
highest near Main Street and diminish toward the edges. These
densities are equivalent to traditional single-family
neighborhoods, and recent townhouse and apartment complexes in
the region.
21
- Institutions: Government buildings, schools, colleges, day care
centers, and public recreation facilities need to be provided in
prominent public locations, easily accessible on foot and by all
other modes of transport. Schools and recreation facilities
need to be directly connected to the city's open space system.
- Open Space Networks: Streets, provided with sidewalks that are
scaled to the amount of pedestrian activity, are the most used
part of the public open space network, and should be landscaped
with trees and enhanced with other planting on the adjacent
private properties.
Walkways other than sidewalks are needed primarily in the
densest commercial areas, where arcades and through block
passages are a welcome and valuable enrichment and in the
undeveloped periphery, where public walkways should give access
to natural attractions.
3. Key Characteristics of the Constructs
Specific characteristics of the three constructs were developed by
the project team and were reviewed and revised by the initial peer
group and the steering committee. The density and size of the Transit
Construct were designed to maximize the use of transit and paratransit
services while maintaining the suburban fabric of the development.
However, for the Short Drive and Walking Constructs, the
characteristics were based on standards put forth for "regional
centers" and "towns and neighborhoods" as defined by MSM's REGIONAL
FORUM in 1985-87 and followed by the Preliminary State Development and
Redevelopment Plan.
The FORUM convened regional public and private sector leaders, as
well as interested citizens, to address ways to better manage growth
in the region. This consensus-building effort developed a set of
recommendations for efficiently concentrating growth into mixed-use
centers. (See An Action Agenda for Managing Growth, Final Report of
the MSM Regional Forum, 1987.)
Table 1 shows the key characteristics used in the Land
Use/Transportation Study for all three constructs. These are
presented as minimum thresholds rather than absolute dimensions of the
constructs. (The estimates in Table 1 were used for Scenario 1.
Scenario 2's estimates were larger in order to accommodate more
suburban growth.) A summary of major points follows:
a. Population
The number of residents ranges from 12,000 in the Transit Construct
to 6,700 in the Short Drive Construct, to 4,500 in the Walking
Construct. Residential density ranges from 15 dwelling units per net
residential acre (average) for the Transit Construct, to 10 dwelling
units per net residential acre (average) for both the Short Drive
Construct and the Walking Construct.
b. Employment
Employment opportunities are significant in the Transit Construct
(13,100 jobs) and the Short Drive Construct (9,500 jobs), but
negligible for the Walking Construct (230 jobs). The commercial land
use floor area ratio is 2.0 in the Transit Construct, 1.1 in the Short
Drive Construct and 0.4 in the Walking Construct.
Both the Transit Construct and the Short Drive Construct have
regional retail anchors, while the retail component of the Walking
Construct is assumed to be a neighborhood center.
22
Table 1
MSM LAND USE CONSTRUCT COMPARISON
Transit Short Drive Walking
Characteristic Construct Construct Construct
"TC" "SD" "W"
COMMERCIAL COMPONENTS:
Comm. Floor Area(SF) 4,000,000 3,000,000 10,000
Comm. Employment 12,000 9,000 30
Commercial FAR 2.0 1.1 0.4
Comm.Net Acres 45.9 62.6 0.6
RETAIL COMPONENTS:
Retail Floor Area(SF) 550,000 250,000 50,000
Retail Employment 1,100 500 200
Retail FAR 1.00 0.40 0.23
Retail Net Acres 12.6 14.3 5.0
NON-RESIDENTIAL TOTALS:
Total Employment 13,100 9,500 230
Total Net Non-Res. Acres 58.5 77.0 5.6
RESIDENTIAL COMPONENTS:
Population 12,000 6,700 4,500
People per D.U. 2.0 2.4 2.8
Dwelling Units 6,000 2,800 1,600
D. U. per Net Res. Acre 15 10 10
Net Residential Acres 400.0 280.0 160.0
TOTAL CONSTRUCT FACTORS:
Jobs per D.U. 2.18 3.39 0.14
Workers per D.U. 1.0 1.5 1.5
RESERVE AREAS:
Open Space 15% 15% 15%
Roads/Utilities 25% 28% 28%
Public Buildings, etc. 10% 10% 10%
GROSS DIMENSIONS:
Area in Acres 917 759 352
Area in Sq. Mi. 1.43 1.19 0.55
Radius if Circular (FT.) 3,566 3,245 2,210
23
c. Jobs to Dwelling Units Ratio
This ratio reflects the mixed-use elements of the Transit Construct
(2.18) and the Short Drive Construct (3.39), while indicating that the
Walking Construct (0.14) is simply a residential center.
d. Gross Dimensions
In order to accentuate the potential for walking trips among land
uses, an attempt was made to concentrate each construct into a
relatively compact area. As a result, the Transit Construct
represents an area of over 900 acres, the Short Drive Construct
represents an area of over 750 acres, and the Walking Construct
represents an area of over 350 acres. This includes not only the
residential, commercial and retail land uses, but open space, roads,
utilities and public buildings as well.
C. The Role of Constructs in Reducing Vehicle Traffic: Local Level
Analysis
Each of the three constructs was designed to reflect a "package" of
land use mix, density, transportation, and demand management
attributes which in combination reduce automobile usage. In this step
of the study, the effects of each construct on reducing auto travel
were quantified by the type of development in each construct for peak
hours, off-peak hours and daily trips. The analysis was designed both
to identify the specific traffic reduction benefits of constructs at
the local level, and to show the overall effects on the regional
network. The regional analysis discussed in Chapters III & IV was
conducted only for the more general measure of daily travel.
1. Assumptions
The analysis was based on a number of assumptions about the trip
types considered and their trip rates, and the effects of the
different constructs on tripmaking, as follows:
- As the target of the study was the reduction of automobile
trips, the trip generation dealt with vehicle trips. The effect
of changes in modal shifts to transit, carpools or walking was
thus expressed as an estimated change in vehicle trips.
- The product of the trip generation was vehicle trips with an
origin or destination external to the construct, as intra-
construct trips do not impact the area roadways to any
significant extent. Traffic zones in the model were not smaller
than a construct.
- Tripmaking generated by each construct was accounted for in
three categories:
- commercial (represented mainly by office rates),
- retail; and
- residential uses.
- The time periods considered were:
- AM Peak Hour
- PM Peak Hour
- Off-Peak periods
- Average Weekday (ADT)
24
Travel behavior description and analyses for the constructs
required inclusion of all such periods. For determination of off-peak
period trip rates, twice the sum of the AM and PM peak hour rates (to
determine the peak period) was subtracted from the ADT rate.
2. Methodology
Construct-level analysis was based on the premise that the
constructs chosen reduce (external) vehicle trips. These vehicle trip
reduction factors were developed for each construct, trip type and
time period compared to basic trip rates. Comparison among the
constructs is possible by looking at the differences in construct-to-
trend ratios. (See Appendix A.)
3. Construct Land Use and Transportation Relationships
A review of the literature in land use/transportation
relationships, transportation demand management and of case, studies
of suburban activity centers indicated that the general effects in
terms of land use and travel relationships can be summarized in five
areas, as follows:
a. Internal Vehicle Trips increased by:
- Greater employment opportunities for residents. (Jobs/Housing
ratio more balanced within zone.)
- More retail/services for residents or employees. (Mixed-use
enhanced.)
b. Internal Walking Trips increased by:
- Combination of jobs, retail/services and residences in close
proximity with one another. (Density and mixed-used
enhanced.)
- Pedestrian oriented site planning and design.
c. Internal Transit Trips increased by:
- Presence of local transit service, i.e., shuttle/feeder
buses. (Density enhanced.)
- Greater variety of trip purposes served. (Mixed-use
enhanced.)
- Transit oriented site planning and design.
d. External Trip Shift to Transit increased by:
- Good transit available to serve remote residents working in
construct and construct residents working in remote job
centers. (Density and mixed-use enhanced.)
- Transit incentives, such as transit pass subsidy by
employers, etc. (Demand management enhanced.)
25
e. External Trip Shift to Carpools increased by:
- Greater carpool matching potential i.e., convenience of
association at both ends of trip. (Density and mixed-use
enhanced.)
- Carpooling incentive through parking management and pricing
at destination. (Demand management enhanced.)
Of course, each one of the features listed above has a varying
influence on the reduction of vehicle trip making, and, in most cases,
the features' interaction with each other complicate estimating. In
addition, similar end results can be caused by the varying interaction
of different factors in different constructs.
4. Determination of Vehicle Trip Reduction Factors
Once basic land use and transit relationships were established,
specific vehicle trip reduction factors for each construct were
determined through the steps below. The project team developed the
factors and had them reviewed by the peer group. All land use based
reduction factors were applied to Institute of Transportation
Engineers average vehicle trip rates for the AM peak hour, PM peak
hour, off-peak period and the average daffy traffic (ADT) conditions,
while the values for the regional analysis were limited to daily (ADT)
vehicle trips.
a. The Factors Influencing Trip Reduction
The vehicle trip reductions from the constructs result from a
combination of factors:
- overall office/retail/housing mix;
- jobs/housing ratio;
- total employment;
- design integration;
- proximity to rail transit;
- presence of radial bus service;
- presence of internal bus service;
- constrained, and in the case of the Transit Construct, priced
parking supply for commercial uses; and increased residential
density.
b. How the Factors Operate on Travel Behavior
As discussed above, these factors in various combinations can bring
about varying degrees of reduction of single occupant vehicles, due
to:
- internalization of vehicle trips, whether by vehicle, transit,
or walking; and/or
- reduction of external vehicle trips by shifts to transit or
rideshare modes.
c. Using NCHRP #323
In looking for case study data to use in measuring the vehicle trip
reduction effects of these characteristics, one of the best sources,
containing the largest, most recent and most consistent data set is
NCHRP #323, Travel Characteristics at Large-Scale Suburban Activity
26
Centers (October, 1989) by Kevin Hooper1. As shown in his report and
in other studies such as Cervero's2, existing "suburban activity
centers" or "suburban employment centers" typically exhibit some of
the above characteristics, but not all. Existing centers exhibit some
land use mixing (particularly office/retail), but generally, with the
possible exception of Bellevue, Washington, do not have the level of
residential development, the parking restraints, the clustering, rail
service, internal transit service, or pedestrian amenities included in
our constructs.
The literature indicates that many of the suburban activity centers
are actually more like "trend" development than the constructs.
Individual cases where higher transit use or walking rates have been
achieved are those, like Bellevue, where there is transit, more
housing units, better integrated design, or pedestrian walkways, etc.
Beyond the NCHRP #323 report, other case studies are useful insofar
as they measure effects of transportation demand management measures,
individual land use or transit service characteristics. Those others
do not consider the land use mixing.
d. Basic Trip Reduction
Thus, a decision was made to use the average values from NCHRP #323
as a base indicator of trip reductions which can be achieved through a
limited amount of mixing land uses and increasing density in suburban
activity centers which would otherwise be dispersed in a "trend"
(sprawl) pattern. The case study data provided the benchmark values
and empirical evidence which were used as the starting point for the
regional testing.
It should be noted that the base trip reductions are fairly
substantial in themselves. Their impact, regionally, could be fairly
significant without full construct development.
e. Enhanced Trip Reduction Factors in Constructs
Then, for each land use under each construct, additional case
studies and the experience of observed behavior were used to estimate
added reductions which could be attributed to the particular features
assumed for our constructs. Some of these reductions are tied to the
Hooper data for Bellevue and other case study data of developments
which are most like our constructs.
Others are estimates, based on work/non-work trip percentages,
ratios of employment to housing, etc. For some trip types there are
no further trip reductions beyond those indicated in the Hooper cases.
(As noted in Appendix A.)
The exception is the walking construct, which is not really a
"suburban activity center" as currently defined, and for which there
is the least case study data. The most comparable data, if available,
would probably be from new towns such as Reston or the new "neo-
traditional suburbs." In this case, the project team reached a
decision that the base
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1. Hooper, Kevin G. Travel Characteristics at Large-Scale Suburban
Activity Centers, National Cooperative Highway Research Program Report
323, (October, 1989).
2. Cervero, Dr. Robert. America's Suburban Centers: A Study of the
Land Use/Transportation Link, Prepared for Office of Policy and
Budget, Urban Mass Transportation Administration, Report No. DOT-T-88-
14, Washington D.C. (January, 1988).
27
case trip type values could not be achieved in all cases, since the
walking construct had the least similarity to the mixed-use centers
studied, notably its lack of employment opportunities. Therefore, in
the case of the walking construct, smaller base reductions were made
for some trip types through negative adjustments.
f. Factoring to Avoid Double Counting
The resulting trip reductions were then combined for each construct
through factoring. In this way the values for the individual
components were combined as the product of sub-factors for each
percentage. This was done to avoid double counting. For example,
transit users produced by construct conditions are not available for
carpools, and vice versa. If individual trip reductions of 15 percent
and 10 percent might be estimated for transit mode shift and
carpooling, respectively, the reduction factor would be 0.765 (0.85 x
0.90), implying a lesser reduction of 1 - 0.765 = 0.235 or 23.5
percent.
Table 2 on page 30 summarizes the total vehicle trip reductions by
construct. The table shows that compared to the trend vehicle trip
generation rate, the number of vehicle trips generated and attracted
to that construct will be reduced by that factor. (See Section 5 on
the following page, for example. Detailed tables showing calculations
of vehicle trip reductions for each construct are included in Appendix
D.)
It is difficult to substantiate every factor as applied to every
trip type. However, it is possible to see how each construct compares
to the current suburban activity centers for each type of trip.
Looking at the literature, the values chosen for use in the analysis
are within ranges which have been measured in other case studies such
as those presented in the ITE 1987 Trip Generation Manual1 and the
Stover and Koepke text Transportation and Land Development.2
Similarly, the February, 1990 FHWA report, Evaluation of Travel
Demand Management Measures to Relieve Congestion3, states that by
instituting programs of Transportation Demand Management (TDM)
measures, "trip reductions in the range of 20% to 40% can be the norm,
rather than the exception." Although our study purposely does not
attempt to isolate TDM program effects from land use factors, TDM
programs such as constrained and priced parking, TMA activity,
rideshare incentives, and staggered work hours are considered part of
each construct "package" along with the land use mix, density, and
design features which are the focus of this analysis.
Land use based vehicle trip reduction factors were later converted
to Home Based-Work, Home Based-Other, and Non-Home Based categories in
the AM peak hour, as required by the network model used in the
TransCAD package. Figure 11 shows the travel reduction factor for
each construct type for the four key time periods, compared to the
same land use developed under trend conditions. The model was run for
1988 conditions, the 2010 "trend" scenario, and two construct
scenarios (ADI), as explained in Chapter IV.
--------------------
1. Institute of Transportation Engineers. Trip Generation, 4th
Edition (1987) pp.17-21.
2. Stover, Virgil G. and Frank J. Koepke, Transportation and Land
Development, Institute of Transportation Engineers, Englewood Cliffs,
New Jersey (1988) pp. 47-48.
3. Kuzmyak, J. Richard, Eric N. Schreffler, and Harold Katz, et al.
Evaluation of Travel Demand Management (TDM) Measures to Relieve
Congestion, Report No. FHWA-SA-90-005, prepared for Federal Highway
Administration, Washington, D.C. (February, 1990), p. 28.
28
5. Producing a Vehicle Trip Reduction Factor: An Example
An example of how this method is applied, related to office trips,
follows. The numbers correspond to those shown in Table 2 on the next
page.
- For office use in the AM peak hour, NCHRP #323 shows that for
"smaller centers," (those most similar in size to the
constructs), an average of 10 percent of employees make a stop
within the activity center. Mode shift data from NCHRP for the
non-Bellevue suburban centers1 show that, on average, 1 percent
use transit, walk or bike, and 7 percent carpool. These values
were put into the matrix as base case study values. It was
assumed that these reductions would be achieved as a minimum
vehicle trip decrease from the trend values in any of the
constructs. Result: 0.90 x 0.99 x 0.93 = 0.83 net vehicle trip
reduction factor.
- Then, for the transit construct an additional 2 percent internal
trip reduction was estimated, due to the internal transit system
and improved walking conditions. An additional 12 percent
transit use was estimated, based on Bellevue's 10 percent
transit mode share (with radial bus system), plus an estimated 2
percent reduction due to the rail access. Reductions due to
ridesharing were not increased over the case study value.
Result: 0.83 (from base case, above) x 0.98 x 0.88 = .71 net
vehicle trip reduction factor.
- For the short drive construct, reductions due to increased
internal walking were increased by 1 percent, and carpooling was
increased 8 percent over the base values, based on Cervero's
findings of 15 percent carpool rates for large and medium mixed
use centers. Result: 0.83 (from base case, above) x .99 x .92 =
.75 net vehicle trip reduction factor.
- For the walking construct, office trips represent a much smaller
proportion of total travel, but, due to their location, they
attract a large proportion of employees and visitors from within
the construct. Thus, the 10 percent internal trip reduction
from the base case was deemed valid for office uses in this
construct. However, no external transit use or carpooling
increases were predicted for the walking construct, due to the
absence of new regional services and the low proportion of use
in commercial space, which would not justify adding local bus
service. Thus, these values were listed as negative values
(translated into factors greater than one) in the table.
Result: 0.83 x 1.01 x 1.07 = 0.90 net vehicle trip reduction
factor.
Vehicle trip reduction factors were then applied to vehicle trip
generation numbers that the basic model produces. By this method, the
special vehicle trip reduction characteristics of constructs as
opposed to land uses in the region were taken into account.
--------------------
1. For the transit use value, Bellevue is excluded from the base case
value due to its atypical, higher level of transit service which would
raise the base value too high to be used in all cases.
29
Table 2
Summary of Vehicle Trip Reduction Factors
Trip Type Land Use Construct Factor
Short
Trend Transit Drive Walking
COMMERCIAL:
Average Daily 1.00 0.69 0.73 0.81
AM Peak Hour 1.00 0.71 0.75 0.90
PM Peak Hour 1.00 0.71 0.75 0.90
Off-Peak Periods 1.00 0.67 0.71 0.75
RETAIL/RESTAURANT:
Average Daily 1.00 0.73 0.76 0.81
AM Peak Hour 1.00 0.83 0.85 0.86
PM Peak Hour 1.00 0.83 0.85 0.86
Off-Peak Periods 1.00 0.67 0.70 0.77
RESIDENTIAL:
Average Daily 1.00 0.73 0.78 0.82
AM Peak Hour 1.00 0.59 0.69 0.77
PM Peak Hour 1.00 0.59 0.69 0.77
Off-Peak Periods 1.00 0.82 0.84 0.86
Note: Compared to the development pattern expected to occur in the MSM
region by the year 2010 (if Trend conditions continue),
constructs would produce fewer vehicle trips on the regional
highway network. As this chart shows, if the Trend represents
the expected level of vehicle tripmaking, then the constructs
produce daily trip levels between 0.59 and 0.90 of what would be
expected to occur, depending upon trip types and construct
types.
30
Figure 11
Ratio of Construct Total Trips Compared
to Some Construct with Trend Rate
Click HERE for Graphic
31
CHAPTER III:
DEVELOPING THE REGIONAL TRANSPORTATION MODEL
A. Basic Components of the Regional Transportation Model
In MSM's Land Use/Transportation Project, a regional transportation
model was developed to provide a platform for evaluating the traffic
impacts of alternative land use forms in the MSM study area. In
particular, it was designed as a means for testing the hypothesis that
placing future development in constructs would have a positive impact
on traffic in central New Jersey.
The modeling procedure involved three methodologies of particular
interest:
- Building the MSM network with reliance on previous efforts;
- Using the GIS-based TransCAD package; and
- Accounting for the traffic reduction effects of construct
development in the regional model.
These are briefly described below and more extensively in the
remainder of this chapter. More detailed descriptions and tables are
included in Appendix B.
1. Building the MSM Network with Reliance on Previous Efforts
The MSM area presented a particularly intriguing modeling
challenge. The region lies at the edge of two regional planning
agency boundaries: Philadelphia to the south and New York
City/Northern New Jersey to the north. Although parts of the three
counties were included in previous transportation modeling projects,
there was no uniform network and no calibrated model covering the four
standard transportation planning steps (trip generation, trip
distribution, modal choice, and network assignment) for all three
counties. Thus, the project team was faced with piecing together data
and information from other studies and regional planning efforts.
2. Using the GIS-Based TransCAD Package
The demands placed on the regional transportation model were
similar for this study to those for any regional study, but with the
added desire to control and manipulate land use and demographic data
more easily. Because of this goal, enhanced capabilities compared to
typical transportation packages were needed.
The TransCAD package, which combines the normal battery of
transportation models with a Geographic Information System (GIS),
provides these capabilities and thus was used in this study.
32
3. Accounting for Traffic Reduction Effects of Construct
Development in the Regional Model Another challenge for this
project was the fact that the typical four-step travel demand
models used throughout the nation generally are not capable of
reflecting land use variables related to density/cluster
development attributes or accessibility by walking and other
non-motorized means. The regional transportation model used in
this study was geared toward a more typical urban/suburban
setting, and it dealt exclusively with vehicle trips.
As a result, a two-step process for defining and accounting for the
traffic reduction features of the constructs was undertaken, as
illustrated in Figure 12. The first step, distinct from the TransCAD
package and described in Chapter II, was undertaken by the project
team with input from the peer review panel and the steering committee.
As discussed, and because the regional models dealt only with
vehicle trips, this process first analyzed the specific effects of
each construct's land use density, mix, and design and its transit
service availability on mode choice, trip length, and auto occupancy
for each individual construct. This provided the detailed zone-level
analysis of specific construct impacts for various time periods.
Then, to enable input into the regional model, these effects were
translated into vehicle "trip reduction factors," which could be input
directly into the regional model by traffic zone at the vehicle trip
generation stage to modify construct tripmaking relative to "trend.'
In regional aggregation, this provided the means to compare each
construct scenario to the "trend" scenario development trips.
It should be noted that the basic vehicle trip reduction factors
used to adjust trend rates for each construct were initially
formulated on the basis of ITE Trip Generation rates on a land use
basis, as described in Chapter II. For application to the trip
generation categories of the regional model, it was necessary to
convert the basic factors to apply to the model categories of separate
productions and attractions by varying purpose definitions. This will
be discussed further in Section D below.
B. Building the MSM Network with Reliance on Previous Efforts
1. Building the 1988 Network
To conduct the travel demand portion of this study, it was
necessary to assemble a data base reflecting the highway and
demographic conditions of the study area. The highway portion of the
data base was used to simulate traffic flows for a given year. In
this study, a calibration year of 1988 and a future year of 2010 were
used. The demographic data used as inputs to the traffic models were
also estimates for the years 1988 and 2010.
Data sources for the highway data base consisted of four networks
supplied by the New Jersey Department of Transportation (NJDOT) from
studies it had completed. The networks supplied were from the North
Jersey Regional Transportation Model Development Project and the Route
1, Route 130 and Route 571 studies. Three of the four networks
(Routes 1, 130 and 571) consisted of existing and future links,
although not representing the same years. The North Jersey network
supplied only the links for 1988 because the future network for that
study was still in development. These four networks were used because
they covered the majority of the MSM study area with the exception of
Hopewell
33
Figure 12: Chart of Study Process
Click HERE for Graphic
34
and a portion of Ewing Township. No individual network provided
complete coverage of the study area, so the four networks were
"stitched" together. (NOTE: Although Trenton and New Brunswick were
covered, the network was not fine-grained enough to accurately
describe urban travel behavior. Because of time and financial
constraints, refinements of the cities' network and zone system were
not attempted in this study, and the results are therefore limited to
suburban analyses).
To simplify this process, all four networks were loaded over a
common base map in TransCAD. By doing this, the consultant team was
able to eliminate any portion of a given network that was covered by
another. By first establishing the Route 1 network as the base to
build from, the other three networks were reduced by deleting where
they overlapped the Route 1 network.
The link detail, zone size and coarseness of the Route 130 network
closely matched that of the Route 1 network, so it was retained and
the Route 571 network was dropped. In addition, although much of the
Route 130 network was dropped because of duplicate coverage with Route
1, its network was used to complete the eastern portion of Mercer
County and fill in areas of sparse coverage on the eastern fringe of
the Route 1 network.
The North Jersey network supplied coverage for the southern halves
of Somerset and Middlesex counties. This was the southern-most extent
of the North Jersey network and was stitched to the northern limits of
the Route 1 network. Each of the older networks had somewhat
different attribute conventions since the Route 1 study used UTPS, the
Route 130 study used a MINUTP network and the North Jersey network was
developed using Tranplan. For the MSM network, the consultant team
needed to transfer the number of lanes, initial speeds and per lane
capacity (facility; and type) from the parent network. This was done
by using the TransCAD package, which has superior capabilities for
defining link length and location with greater accuracy than the
parent systems.
The project team developed new networks and a zone system for
Hopewell and Ewing Townships. Speed and capacity classifications for
these new links were defined using the facility and area
classification table from the Route 1 Corridor Study Report.
2. Building the 2010 Network
The calibration network was used as a base from which the future
network, used in the Trend and Scenarios 1 and 2, was constructed.
Both the Route 1 and Route 130 Studies contained future networks. The
differences between the calibration and future networks of these two
studies represent the proposed projects in the MSM Region. Since the
completion of the Route 1 and Route 130 Studies and since the start of
this study, a number of highway projects assumed to be constructed are
either under further study or lack funding to implement. These
projects include Route 92 through Middlesex County and the widening of
Routes 27 and 130. Therefore, they were not included in this study.
Discussions with NJDOT revealed four highway facility changes to
the MSM calibration network that could be completed by 2010: 1)
extension of Route 29 from the Trenton Freeway to the I-195/295
Trenton Complex in western Washington Township, Trenton and Hamilton
Township; 2) extension of I-295 from the Trenton Complex into Bucks
County, Pennsylvania (this extension functions as an external
connector in the network); 3) the Hightstown Bypass; and 4) the
widening of the New Jersey Turnpike by two lanes from Cranbury Road to
State Highway 18.
35
These changes were incorporated into the existing (calibration)
1988 network to form the 2010 network used for this analysis. It
should be stressed, however, that these projects are not necessarily
included in NJDOT's committed capital programs.
3. Building Traffic Zones
The MSM region was divided into nearly 200 geographic zones, within
which population, employment and other relevant land use/demographic
data was stored. Trips originating from or destined to each zone link
up to the regional network from each zone centroid via a centroid
connector to the highway links. Zones were built as an amalgam of
census blocks, a process expedited by TransCAD's GIS capabilities.
There is some correspondence between the zones built for this effort
and those used in the other modeling efforts described earlier.
(Appendix B shows the zonal layout for the MSM region.)
Constructs for the year 2010 were assigned either to an existing
traffic zone or to a new zone created from segments of one or more
existing zones. Placing a construct in an existing zone(s) meant that
any existing development in the zone (as of 1988) would be absorbed in
and take on the behavior pattern of the construct development. This
implies that the existing development served as a foothold upon which
the construct was built. All but four of the constructs created in
this study were assumed to be developed in this so-called "piggyback"
fashion. In the four new zones, the travel behavior is characterized
by the construct factors, but the persons in the surrounding zones
with trend-type development would not change as a result of proximity
to the construct development.
4. External Trips
The model accommodates external trips. There are two types of such
trips: first, trips that pass through the MSM region without origins
or destinations in the area; and second, trips that either originate
from or are destined to the region, but with destinations or origins
outside the region. The Route 1 model had to be adjusted to account
specifically for the trip generation of zones that the original model
treated as external points, but which were now contained within the
larger MSM network.
C. Using the GIS-Based TransCAD Package
The GIS-based TransCAD package contains a gravity model and an
equilibrium traffic assignment model among its battery of procedures.
It also provides numerous procedures for processing land use data,
constructing/subdividing traffic zones, calculating the precise
location and adjustment of transportation links, and summarizing
traffic phenomena by geographic area. Thus, it provided most of the
models necessary for the current study, and allowed for direct entry
and manipulation of the land use database by the MSM professional
staff. A spreadsheet model calculated the daily person trip ends. A
complex combination of case study results provided the modal choice
(reduction) percentages for each type of construct. Constructs were
easily accommodated by creating new zones or altering zone boundaries.
36
D. Accounting for Traffic Reduction Effects of Construct Development
in the Regional Model
Because they were based on vehicle trip generation rates by
individual land use, the traffic reduction effects of the constructs
were taken into account in the Trip Generation step of the standard
four-step transportation modeling process. To be used in the model,
however, the rates had to be converted from land use based rates (i.e.
vehicle trips per 1,000 square feet of floor space) to rates which
could be applied to the different trip categories used in the model.
This procedure is discussed below.
This study used a simplified set of vehicle trip generation
equations in order to reduce the need for detailed zone level land use
forecasts. The relationships in the parent studies required estimates
of housing units by type (single family, low-rise multi-family, high-
rise), or household size and income. It should be noted that the
North Jersey study, which used income and household size, did not
forecast dwelling unit levels for any future year.
This study developed a simplified set of vehicle trip generation
rates from the Route 1 Study rates, as shown in Appendix C. Where land
uses combined (e.g., single family and multi-family dwelling units),
the new rates were calculated as the weighted averages of the rates
from the parent study. Thus, they contained an implicit assumption
that the relative mix of dwelling types would remain the same in the
future for the basic trip generation equations. In a similar fashion,
new factors for trip attractions were weighted functions of various
employment categories which have been aggregated into retail and non-
retail categories.
The trip generation formulas used generated vehicle trips for three
basic trip types:
- Home-based-work trips, meaning trips made from home to work or
work to home;
- Home-based-other trips, meaning trips made to or from home, to
or from another, non-work destination; and
- Non-home-based trips, meaning a trip not made either to or from
home.
The formulas generated these vehicle trips for four different land
use types (reduced from the 16 land use types used in the Route 1
Study) namely:
- one residential type, combining various density types;
- two employment types, one being retail and the other non-retail,
which includes office, industrial, hospital, etc.; and
- one for university students.
All vehicle trips to (i.e., trip attractions) and from (i.e., trip
productions) zones were generated. A daily vehicle trip rate
represents the sum of attraction and productions for three trip types
and four land use types.
37
The vehicle trip generation formulas developed were applied to all
scenarios studied. The trip modification effects of the special land
use constructs were incorporated by applying construct trip reduction
factors (ITE/land use derived) as described in Chapter II, converted
to the model trip categories described on the previous page, and
applied to traffic zones where constructs are located.
The factor conversion or adaptation was done by analogy or
combination. Among the assumptions made were those that peak hour
travel, particularly AM, is home-based and work oriented, and that
off-peak non-retail commercial trips are dominantly non-home-based.
For example, the factor for residential AM peak hour trips is
appropriate for home-based-work productions, as virtually all of such
trips leave home and are destined principally to work. Similarly, the
off-peak commercial (non-retail) trip factor is appropriate for
application to non-home-based productions or attractions, as such
trips are unlikely to be going to or from home.
Once the vehicle trip reduction factors were converted to the model
categories, they were input into the model to reduce average daily
vehicle trips going to or from each construct zone in each of the two
scenarios analyzed. The results of the trend analysis, and the
analyses of the two alternative construct development scenarios are
discussed in the following chapters.
38
CHAPTER IV:
FORECASTING DEVELOPMENT SCENARIOS
A key element in testing the effectiveness of constructs of higher
density, mixed-use centers is to develop a forecast of future land use
patterns in the MSM region. In fact, multiple forecasts must be
developed: one representing the best estimate of current land use
development patterns without any shift to construct-type development;
and one or more forecasts representing the presence of construct
centers in the MSM region. A 2010 forecast year was used,
representing the latest year in which reasonable estimates of
regionwide development can be projected and the earliest year in which
to expect constructs to become a significant presence in the region.
Prior to developing these forecasts, however, it is important to
build a consistent set of baseline conditions, using the most recent
estimate of current land use and demographic characteristics in the
region. The year 1988 was designated as the latest year in which
existing conditions can be determined with any reasonable accuracy.
A. Developing 1988 Baseline Conditions
MSM staff developed 1988 conditions for the following key indices:
- Total number of households;
- Total retail employment;
- Total non-retail employment; and
- Total university student population.
The 1988 estimates were based on 1980 census data, more recent
estimates from the various municipalities in the region, and knowledge
of recent site specific developments from MSM's annual Current
Development Survey (MSM Regional Data Book). The 1988 levels were
estimated for each of the nearly 200 traffic zones. Table 3 shows the
various estimates aggregated at the municipal level.
B. Year 2010 Trend Conditions
The total growth increment from 1988 to the year 2010 for the MSM
region was based on county projections prepared for New Jersey's
Cross-Acceptance Process. This process required counties to help
develop the New Jersey State Development and Redevelopment Plan, by
soliciting input from municipal officials, interest groups and
community leaders. The expected growth levels in the MSM region for
the year 2010 as published in the 1988 Preliminary Plan are:
39
Table 3
1988 Baseline Conditions for MSM Region Communities
Municipality Non-Retail Employment Retail Employment Households
East Windsor 7,615 848 8,666
Ewing 26,152 2,508 12,541
Hamilton 22,302 5,623 31,336
Hightstown 2,055 800 1,818
Hopewell Township & Borough 3,299 168 4,673
Lawrence 14,684 6,617 8,616
Pennington 1,596 40 872
Princeton Township & Borough 20,615 1,647 8,804
Trenton 51,442 3,405 33,952
Washington 1,601 500 2,250
West Windsor 11,112 1,050 4,436
Mercer County 162,473 23,206 117,964
Franklin 21,855 2,087 13,502
Hillsborough 3,309 1,071 9,165
Manville 996 283 3,868
Millstone 35 19 180
Montgomery & Rocky Hill 7,385 595 3,290
South Bound Brook 426 69 1,502
Somerset County (part) 34,006 4,124 31,507
Cranbury 6,653 50 913
East Brunswick 17,315 8,004 13,555
Helmetta 154 11 439
Jamesburg 1,649 433 1,688
Milltown 2,415 242 2,412
Monroe 1,946 0 8,640
New Brunswick 32,395 2,857 12,682
North Brunswick 13,606 2,169 10,730
Plainsboro 5,847 1,152 6,833
South Brunswick 11,906 780 8,341
South River 1,814 423 4,823
Spotswood 1,720 454 2,904
Middlesex County (part) 97,420 16,575 73,960
MSM Region Total 293,899 43,905 223,431
40
- A growth of 182,581 new jobs, of which 14,292 are expected to be
retail jobs and 168,287 are expected to be non-retail jobs; and
- A growth of 187,905 new residents, or 92,016 new households.
Once again, year 2010 estimates at the zonal level are based on
projections of municipalities, knowledge of "pipeline" projects and
judgment of likely growth areas. Table 4 shows the various estimates
for the year 2010 aggregated at the municipality level.
C. Alternative Development Scenarios
The basis for alternatives to the expected trend development was
the substitution of construct centers for typical suburban land use
development. Chapter II introduced the three construct types: the
Transit Construct, the Short Drive Construct, and the Walking
Construct. All three are projected to be utilized in the MSM region
under alternative scenarios. In fact, this study assumes in its
alternative growth scenarios that all suburban growth will take the
form of constructs.
A major undertaking was to assign the appropriate number of
constructs to the region in particular geographic locations. The
purpose of this effort should be carefully understood: Placing
constructs in actual sites is done to indicate that such development
could reasonably fit within the region. However, the sites selected
are not meant to be actual recommendations for construct development,
but merely representative locations. The project team has not
performed any of the necessary detailed planning, environmental or
design analyses that would be required to recommend particular
development sites.
Two alternative scenarios of construct development were used in
this analysis. Scenario 1 tests the effects of channeling some of the
growth which would occur in suburban areas under trend conditions into
the urban areas of New Brunswick and Trenton, on the hypothesis that
placing more development in the urban areas with higher land use
densities and more transit services would help reduce auto travel. It
is also a policy goal of the emerging New Jersey State Development and
Redevelopment Plan.
Scenario 2 assumes that the cities will grow only at their expected
trend rates, with suburban constructs absorbing all the remaining
growth. Both scenarios take as given the regional projections of
employment and household growth. Therefore, the total growth
projected for the year 2010 in the Trend, Scenario 1 and Scenario 2
are all the same. It is the disaggregate distribution of development
that differs among the Trend and two scenarios. (NOTE: The analysis of
the data published in this report does not include the cities. See
Chapter V, Defining the Study Area).
1. Scenario 1: Constructs and Major Urban Growth
a. The Urban Growth Component
Preceding the assignment of constructs, it was necessary to make
some assumptions about the major urban centers in the region, New
Brunswick and Trenton. Their projected growth rates
41
Table 4
2010 Trend Conditions for MSM Region Communities
Municipality Non-Retail Employment Retail Employment Households
East Windsor 12,097 1,403 13,562
Ewing 30,949 2,791 14,512
Hamilton 27,722 6,568 40,394
Hightstown 3,680 1,000 1,819
Hopewell Township & Borough 4,426 394 7,231
Lawrence 22,170 7,180 11,235
Pennington 3,510 40 1,113
Princeton Township & Borough 28,263 1,837 13,295
Trenton 65,644 4,256 39,619
Washington 3,340 600 4,159
West Windsor 23,392 2,128 9,327
Mercer County 225,193 28,197 156,266
Franklin 24,221 2,389 23,293
Hillsborough 9,311 1,339 14,249
Manville 3,347 283 4,133
Millstone 191 19 187
Montgomery & Rocky Hill 9,961 1,149 5,548
South Bound Brook 1,011 69 1,669
Somerset County (part) 48,042 5,248 49,079
Cranbury 7,360 316 2,165
East Brunswick 22,211 10,551 17,768
Helmetta 214 11 986
Jamesburg 2,270 433 2,215
Milltown 2,615 242 3,000
Monroe 9,913 2,391 14,215
New Brunswick 34,002 3,013 16,461
North Brunswick 30,665 3,667 15,223
Plainsboro 32,097 1,452 13,566
South Brunswick 42,262 1,801 15,645
South River 2,829 423 5,504
Spotswood 2,508 454 3,354
Middlesex County (part) 188,946 24,754 110,102
MSM Region Total 462,181 58,199 315,447
42
for the year 2010 are shown in Table 4, and are taken from the State
Plan's prediction (not policy) of little growth in those areas. These
became our 2010 Trend levels for the cities.
MSM's REGIONAL FORUM, discussed in Chapter II, developed a growth
policy scenario which placed much higher employment and population in
these two cities than did the trend estimates. These became our
Scenario 1 levels for the cities.
The remaining regional growth was distributed among constructs.
b. The Construct Component
The assignment of constructs was performed by the project team,
with input from the steering committee. As a first step, three
Transit Constructs were found to be a reasonable number for the
region. Two were located on the Northeast Corridor rail line (at
Princeton Junction in West Windsor and the projected station for
Monmouth Junction in South Brunswick), and one was positioned near
Exit 8 of the New Jersey Turnpike, where there is convenient bus
service to New York City.
Next, eight Short Drive Constructs were assigned, absorbing
virtually all the remaining regional employment growth not picked up
by the cities and the Transit Constructs. Short Drive Constructs were
placed where employment centers are already emerging, and/or there is
some major highway access.
Finally, the remaining population growth (and a small amount of
employment growth) was distributed into eight Walking Constructs.
Figure 13 shows a map of the locations of these constructs, while the
municipalities in which they are located are listed in Table 5.
2. Scenario 2: Constructs with. Trend Urban Growth
In Scenario 2, the year 2010 Trend growth assumptions for New
Brunswick and Trenton were assumed to prevail, meaning that the
Regional FORUM's goal for a major resurgence of the cities is not met.
Instead, the same level of suburban growth as projected in the Trend
is expected in this scenario, and all of the 1988-2010 growth
increment (except for the small amount predicted for the cities) is
absorbed by the constructs. Figure 14 shows how employment and
population levels in Trenton and New Brunswick differ among the
Baseline 1988, the 2010 Trend, and Scenarios 1 and 2.
It was assumed that the same number of constructs would be sited in
the region in Scenario 2 as in Scenario 1, at the same locations. But
in order to absorb the larger amount of suburban growth, a number of
the constructs have been increased in size. It should be noted,
however, that although the land area was increased, the land use
density (i.e. average dwelling units per acre) was maintained.
Finally, Tables 6 and 7 show the differences in the total level of
employment and households among the Baseline 1988, the year 2010
Trend, Scenario 1 and Scenario 2, aggregated at the municipal level.
Detailed descriptions of these forecasts by traffic zone are included
in Appendices D and E.
43
Figure 13
Click HERE for graphic.
44
Table 5
Location of Constructs in Both Scenarios 1 and 2
Number of constructs in Ealch Municipality of this Type:
Transit Short-Drive Walking
Municipality Construct Construct Construct
East Windsor 1(bus) - -
Hopewell Township - 1 2
Lawrence - 1 -
Washington - 1 1
West Windsor 1(rail) - -
Franklin - 1 1
Hillsborough - 1 -
Montgomery - - 2
Cranbury - - 1
North Brunswick - 1 -
Plainsboro - 1 -
South Brunswick 1(rail) 1 1
NOTE: The site selected are not meant to be actual recommendations
for construct development, merely representative locations.
The project team has not performed any of the necessary
detailed planning, environmental or design analyses that
would be requied to recommend particular development sites.
45
Figure 14
Employment and Household Projections for Trenton and New Brunswick
Click HERE for graphic.
46
Table 6
Current and Projected Employment Under Different Scenarios
Construct Total Employment:
Municipality Types 1988 2010 Trend 2010 Scen. 1 2010 Scen. 2
East Windsor T 8,463 13,500 21,563 27,031
Ewing 28,660 33,740 28,660 28,660
Hamilton 27,925 34,290 27,925 27,925
Hightstown 2,855 4,680 2,855 2,855
Hopewell Twnshp/Boro D,2W 3,467 4,820 13,427 17,656
Lawrence D 21,301 29,350 30,301 34,006
Pennington 1,636 3,550 1,636 1,636
Princeton Twnshp/Boro 22,262 30,100 22,262 22,262
Trenton 54,847 69,900 87,817 69,900
Washington D,W 2,101 3,940 11,830 15,959
West Windsor T 12,162 25,520 25,262 30,731
Mercer County 185,679 253,390 273,538 278,621
Franklin D,W 23,942 26,610 33,672 37,801
Hillsborough D 4,380 10,650 13,880 17,899
Manville 1,279 3,630 1,279 1,279
Millstone 54 210 54 54
Montgomery/Rocky Hill 2W 7,980 11,110 8,440 8,660
South Bound Brook 495 1,080 495 495
Somerset County (part) 38,130 53,290 57,820 66,188
Cranbury W 6,703 7,676 6,933 7,043
East Brunswick 25,319 32,762 25,319 25,319
Helmetta 165 225 165 165
Jamesburg 2,082 2,703 2,082 2,082
Milltown W 2,657 2,857 2,657 2,657
Monroe 1,946 12,304 1,942 1,942
New Brunswick 35,252 37,015 68,223 37,015
North Brunswick D 15,775 34,332 25,275 26,311
Plainsboro D 6,999 33,549 16,499 20,518
South Brunswick T 12,686 44,063 35,516 45,115
South River 2,237 3,252 2,237 ,237
Spotswood 2,174 2,962 2,174 2,174
Middlesex County (part) 113,995 213,700 189,022 175,561
MSM Region Total 337,804 520,380 520,380 520,380
T = transit construct, W = walking construct, D = short-drive construct
47
Table 7
Current and Projected Households Under Different Scenarios
Construct Total Households:
Municipality Types 1988 201O Trend 201O Scen.1 201O Scen.2
East Windsor T 8,666 13,562 14,666 17,994
Ewing 12,541 14,512 12,541 12,541
Hamilton 31,336 40,394 31,336 31,336
Hightstown 1,818 1,819 1,818 1,818
Hopewell Twnshp/Boro D,2W 4,673 7,231 10,673 13,976
Lawrence D 8,616 11,235 11,416 12,958
Pennington 872 1,113 872 872
Princeton Twnshp/Boro 8,804 13,295 8,804 8,804
Trenton 33,952 39,619 53,359 39,619
Washington D,W 2,250 43,159 6,650 9,073
West Windsor T 4,436 9,327 10,436 13,764
Mercer County 117,964 156,266 162,571 162,755
Franklin D,W 13,502 23,293 17,902 20,325
Hillsborough D 9,165 14,249 11,965 13,507
Manville 3,868 4,133 3,868 3,868
Millstone 180 187 180 180
Montgomery/Rocky Hill 2W 3,290 5,548 6,490 8,253
South Bound Brook 1,502 1,669 1,502 1,502
Somerset County (part) 31,507 49,079 41,907 47,635
Cranbury W 913 2,165 2,513 3,394
East Brunswick 13,555 17,768 13,555 13,555
Helmetta 439 986 439 439
Jamesburg 1,688 2,215 1,688 1,688
Milltown W 2,412 3,000 2,412 2,412
Monroe 8,640 14,215 8,640 8,640
New Brunswick 12,682 16,461 32,090 16,462
North Brunswick D 10,730 15,223 13,530 15,072
Plainsboro D 6,833 13,566 9,633 11,175
South Brunswick T 8,341 15,645 18,741 24,493
South River 4,823 5,504 4,823 4,823
Spotswood 2,904 3,354 2,904 2,904
Middlesex County (part) 73,960 110,102 110,968 105,057
MSM Region Total 223,431 315,447 315,447 315,447
T = transit construct, W = walking construct, D = short-drive construct
48
CHAPTER V:
ANALYZING THE TRANSPORTATION IMPACTS OF CONSTRUCT SCENARIOS
A. Defining the Study Area
In analyzing the results of the constructs, a somewhat smaller
study area was selected from the MSM region. For technical reasons,
the cities of New Brunswick and Trenton are excluded. The reasons for
examining this smaller, non-urban study area are twofold:
- First, the study was funded to analyze suburban land use trends
and alternatives. Although a key assumption is made in Scenario
1 regarding the growth of the cities, it was not within the
scope of this analysis to assess the specific impacts of that
growth.
- Second, the vehicle trip generation rates used in the analysis
represent the suburban qualities of the region, not its two
urban centers. As A result, the transportation model within the
TransCAD package over-predicts auto trips in both New Brunswick
and Trenton by a considerable amount (since auto trip rates are
significantly higher in suburban vs. urban areas). The results
showed worse auto congestion in the cities, neither the intent
nor a realistic outcome of the planning goals for the cities.
In order to adequately include New Brunswick and Trenton in future
analyses, either of two future methodological steps should be taken:
1) Fine tune the network to allow for a greater number of zones
within the two urban areas; and
2) Develop specific urban area vehicle trip generation formulas, or
urban area vehicle trip reduction factors, similar to those
developed for the constructs.
Neither of these steps was within the purview of this study.
The study area, excluding the cities of New Brunswick and Trenton,
is referred to as the MSM Construct Study Area.
B. Regional Impacts of the Scenarios
1. Total Vehicle Trips on the Regional Network
Figure 15 shows the effect of constructs on the growth of vehicle
trips in the MSM Construct Study Area. The Trend represents a growth
of 1.74 million daily vehicle trips from the 1988 baseline, or an
increase of 43 percent. In Scenario 1, the growth is just under
687,000 daily
49
Figure 15
Growth in Daily Trip Ends: 1988 - 2010 MSM Construct Study Area:
Trend Versus Alternative Development Scenarios
Click HERE for graphic.
50
trips, growth of 17 percent from the 1988 baseline. In Scenario 2,
the growth is 1.18 million daily trips, an increase of 29 percent from
the 1988 baseline.
Table 8 shows the total number of vehicular trips (existing, plus
growth related) on the network in 1988, Year 2010 Trend, Scenario 1
and Scenario 2, disaggregated by jurisdiction. For this table (and in
subsequent Tables 9-11), some of the smaller municipalities have been
grouped together with larger ones to create a set of 17 jurisdictions
(MCD's) as mapped in Appendix F. This was done because the limited
size of smaller jurisdictions did not allow for substantial network
building within them, producing skewed estimates of vehicle miles
traveled, speeds and travel time. (However, the full breakout of
vehicle trips for all municipalities and zones can be found in
Appendix E.) The combined jurisdictions are as follows:
- Hopewell includes Pennington
- East Windsor includes Hightstown
- East Brunswick includes Milltown, South River and Spotswood
- Monroe includes Helmetta and Jamesburg
- Hillsborough includes Manville and Millstone
- Franklin includes South Bound Brook
In addition, as previously discussed, the cities of New Brunswick
and Trenton are not shown in the tables or reflected in the
accompanying figures.
Table 8 indicates that Scenario 1 produces an 18 percent reduction
in total Year 2010 vehicle trips on the regional network, while
Scenario 2 produces nearly a 10 percent reduction in total vehicle
trips. The higher impact of Scenario 1 is due to the combined effects
of channeling more growth into the two urban areas (where higher
overall densities and better transit service lead to lower vehicle
trip generation), and channeling the remaining suburban growth into
constructs. Scenario 2, on the other hand, keeps all trend growth
(except a nominal level in the cities) in the suburban areas.
2. Total Vehicle Miles on the Regional Network
Figure 16 shows the effect of constructs on the growth of vehicle
miles traveled (VMT) during the AM peak hour on the regional highway
network in the MSM Construct Study Area. The trend represents a
growth of 299,000 VMT from the 1988 baseline, or a growth of 38
percent. (Baseline VMT in the AM Peak is just over 918,000.) In
Scenario 1, the growth is just under 168,000 VMT, or an increase of 21
percent from the 1988 baseline. In Scenario 2, the growth is 202,000
VMT, an increase of 26 percent.
Table 9 shows total AM Peak hour VMT on the network in 1988, Year
2010 Trend, Scenario 1 and Scenario 2, disaggregated by jurisdiction.
Scenario 1 causes a 12 percent reduction in the level of year 2010 VMT
on the regional network, while Scenario 2 produces nearly a 9 percent
reduction.
51
Table 8
Vehicle Trips in the MSM Construct Study Area
Daily Vehicle Trip Ends (Total In and Out):
------------------------------------------ Percentage Difference
1988 2010 2010 2010 From 2010 Trend for:
Jurisdiction Base Trend Scen. 1 Scen. 2 Scen.1 Scen.2
Washington 46,697 74,273 127,177 169,591 71.2% 128.3%
Ewing 339,907 378,957 328,756 328,756 -13.2% -13.2%
Lawrence 372,560 434,553 375,431 400,411 -13.6% - 7.9%
Hopewell 87,780 132,168 183,871 235,956 39.1% 78.5%
Princeton 257,165 335,439 249,691 249,691 -25.6% -25.6%
West Windsor 130,647 263,981 216,928 271,722 -17.8% 2.9%
Hamilton 608,927 721,057 578,606 578,606 -19.8% -19.8%
East Windsor 206,673 296,117 303,654 358,426 2.5% 21.0%
Cranbury 41,837 65,079 57,003 66,956 -12.4% 2.9%
Plainsboro 135,014 326,649 183,302 215,762 -43.9% -33.9%
South Brunswick 167,383 404,977 348,622 445,840 -13.9% 10.1%
North Brunswick 243,498 410,384 278,127 310,587 -32.2% -24.3%
East Brunswick 640,491 776,287 610,623 610,623 -21.3% -21.3%
Monroe 145,211 313,060 136,739 136,739 -56.3% -56.3%
Montgomery 87,275 135,090 120,435 140,350 -10.8% 3.9%
Hillsborough 199,948 288,157 246,814 279,274 -14.3% -3.1%
Franklin 331,006 439,748 383,140 425,554 -12.9% -3.2%
MSM Construct
Study Area 4,042,019 5,795,976 4,728,919 5,224,844 -18.4% -9.9%
52
Figure 16
Growth in AM Peak Hour Vehicle Miles of Travel 1988 to 2010
MSM Construct Study Area:
Trend Versus Alternative Development Scenarios
Click HERE for graphic.
53
Table 9
AM Peak Hour Vehicle Miles Traveled (VMT)
in the MSM Construct Study Area
Peak Hour VMT:
----------------------------- Percentage Difference
1988 2010 2010 2010 From 2010 Trend for:
Jurisdiction Base Trend Scen. 1 Scen. 2 Scen. 1 Scen.2
Washington 91,926 109,419 106,211 102,221 -2.9% -6.6%
Ewing 51,551 55,055 57,756 50,929 4.9% -7.5%
Lawrence 74,568 96,545 102,519 99,980 6.2% 3.6%
Hopewell 25,494 32,276 33,776 37,927 4.6% 17.5%
Princeton 39,966 56,184 42,922 43,845 -23.5% -22.0%
West Windsor 45,731 72,124 59,708 65,460 -17.2% -9.2%
Hamilton 34,764 45,624 47,844 51,608 4.9% 13.1%
East Windsor 25,986 36,666 35,761 41,203 -2.5% 12.4%
Cranbury 40,201 53,285 45,217 48,301 -15.1% -9.4%
Plainsboro 19,634 37,605 21,786 24,772 -42.1% -34.1%
South Brunswick 71,936 134,703 104,437 118,289 -22.5% -12.2%
North Brunswick 35,178 54,081 50,225 48,668 -7.1% -10.0%
East Brunswick 77,835 88,530 77,480 76,117 -12.5% -14.0%
Monroe 31,246 50,256 32,738 33,026 -34.9% -34.3%
Montgomery 27,441 39,015 30,887 34,152 -20.8% -12.5%
Hillsborough 32,948 46,970 37,555 40,980 -20.0% -12.8%
Franklin 56,065 72,939 63,207 67,221 -13.3% -7.8%
MSM Construct
Study Area 782,019 1,081,277 950,099 984,699 -12.1% -8.9%
54
3. Travel Speeds
As a result of Trend growth between the years 1988 and 2010, speeds
on a number of the region's highway links deteriorate. As Table 10
shows, average regionwide AM Peak speeds on the network (which
represents only a subset of the region's key highway links), would
fall by 4 miles per hour, or a 16 percent decline. Under Scenario 1,
there would be virtually no change in speed from 1988 levels. Under
Scenario 2, average speed would decline by less than 2 miles per hour,
or a 7 percent decline. In both cases, therefore, construct
development has a key beneficial effect upon travel speeds, relative
to trend development patterns.
Table 10
AM Peak Hour Average Vehicle Speeds
in the MSM Construct Study Area
AM Peak Hour Average Vehicle Speeds (miles per hour):
------------------------------- Percentage Difference
1988 2010 2010 2010 From 2010 Trend for:
Jurisdiction Base Trend Scen.1 Scen.2 Scen.1 Scen.2
Washington 29.4 30.6 32.9 31.8 7.7% 4.2%
Ewing 12.2 11.0 13.1 15.1 19.3% 37.4%
Lawrence 35.6 31.2 33.1 35.6 6.0% 13.9%
Hopewell 34.0 32.6 31.5 29.9 -3.2% -8.3%
Princeton 14.5 14.2 14.9 13.8 4.7% -3.1%
West Windsor 32.2 17.9 30.3 27.6 69.0% 53.8%
Hamilton 47.4 44.7 45.5 44.7 1.7% -0.2%
East Windsor 29.4 28.7 27.2 25.1 -5.1% -12.3%
Cranbury 44.3 42.0 44.8 44.7 6.6% 6.3%
Plainsboro 29.2 19.1 27.6 23.5 44.4% 22.7%
South Brunswick 33.4 21.2 28.9 22.1 36.2% 4.2%
North Brunswick 28.6 23.8 27.8 24.8 16.8% 4.0%
East Brunswick 21.6 19.8 21.5 21.5 9.0% 8.9%
Monroe 24.5 22.5 24.7 25.0 9.8% 11.2%
Montgomery 32.3 26.4 31.3 30.5 18.5% 15.5%
Hillsborough 15.8 8.8 15.3 8.8 75.2% 0.8%
Franklin 18.8 17.5 18.5 16.9 5.7% -3.1%
MSM Construct
Study Area 24.6 20.6 25.0 22.9 21.4% 11.1%
55
4. Travel Time
Figure 17 shows the effect of constructs on vehicle travel time in
the AM peak hour. This represents an increase in the total number of
minutes required to traverse the highway network as a result of
additional tripmaking in the year 2010. The total new minutes of
delay experienced in the trend would mean a growth of more than 65
percent. In Scenario 1, the growth in minutes of delay is only 20
percent from the 1988 base year. In Scenario 2, the growth in minutes
of delay is 36 percent.
Table 11 shows total vehicle travel time during the AM peak hour on
the network in 1988, Year 2010 Trend, Scenario 1 and Scenario 2,
disaggregated by jurisdiction. It indicates that Scenario 1 produces
a 28 percent reduction in the level of year 2010 travel time on the
regional network, while Scenario 2 produces an 18 percent reduction.
Table 11
AM Peak Hour Vehicle Travel Minutes
in the MSM Construct Study Area
AM Peak Hour Vehicle Travel Minutes:
----------------------------- Percentage Difference
1988 2010 2010 2010 From 2010 Trend for:
Jurisdiction Base Trend Scen. 1 Scen. 2 Scen. 1 Scen. 2
Washington 187,404 214,849 193,563 192,672 -9.9% -10.3%
Ewing 253,058 301,650 265,206 203,020 -12.1% -32.7%
Lawrence 125,680 185,458 185,864 168,561 0.2% -9.1%
Hopewell 44,966 59,492 64,337 76,233 8.1% 28.1%
Princeton 165,878 236,602 172,901 190,643 -26.9% -19.4%
West Windsor 85,344 241,125 118,129 142,297 -51.0% -41.0%
Hamilton 43,983 61,178 63,073 69,337 3.1% 13.3%
East Windsor 53,100 76,750 78,907 98,322 2.8% 28.1%
Cranbury 54,467 76,032 60,521 64,811 -20.4% -14.8%
Plainsboro 40,351 118,028 47,364 63,374 -59.9% -46.3%
South Brunswick 129,393 381,204 216,929 321,286 -43.1% -15.7%
North Brunswick 73,928 136,191 108,273 117,800 -20.5% 13.5%
East Brunswick 216,182 268,745 215,810 212,109 -19.7% -21.1%
Monroe 76,501 133,951 79,485 79,150 -40.7% 40.9%
Montgomery 50,962 88,626 59,201 67,154 -33.2% -24.2%
Hillsborough 125,204 321,775 146,884 278,466 -54.4% -13.5%
Franklin 179,020 250,563 205,342 238,193 -18.0% -4.9%
MSM Construct
Study Area 1,905,421 3,152,219 2,281,789 2,583,448 -27.6% -18.0%
56
Figure 17
Growth in Travel Time (Vehicle Minutes of Travel) 1988 to 2010
MSM Construct Study Area: Trend Versus Alternative Development Scenarios
Click HERE for graphic.
57
CHAPTER VI:
CONCLUSIONS AND NEXT STEPS
A. Conclusions
The questions that were asked as the impetus of the study, as outlined in Chapter
I, have now been addressed. We have examined the suburban character of higher
density, mixed-use centers and measured the potential results that can be achieved by
changing our current practice of creating lowdensity, single-use development.
The extent to which these results can be achieved in the MSM region will depend on
our ability to implement construct-like development. Although the total
implementation of these constructs is ambitious, several current initiatives are
pushing practice in the construct direction: the concept of "communities of place" in
the emerging New Jersey State Development and Redevelopment Plan; the federal Clean
Air Act mandating significant reductions in vehicle miles traveled (VMT), as well as
in emissions in New Jersey; and the struggle in which many towns are engaged to
reduce the impact of recent growth on their character and infrastructure.
Four main conclusions can be drawn from this study, and these are discussed below.
1. Mixed-Use Centers Can Produce Significant Regional Transportation Benefits
The results of the previous chapter are clear: Constructs can have significant
effects on slowing the growth of trips, VMT, and the deterioration of highway speeds
normally associated with growing suburban areas. In the year 2010, construct
scenarios have the following effect, relative to the trend:
- 10-18 percent reduction in total projected regional automobile trips -- and a
30-60 percent reduction in the incremental impacts of forecasted growth;
- 9-12 percent reduction in total projected regional vehicle miles traveled (VMT)
and a 33-45 percent reduction in the incremental impacts of growth;
- little, if any, change in regional speeds; and
- 18-28 percent reduction in added travel time.
All of these regional network impacts have far-reaching consequences in many areas:
- The continued deterioration of air quality is retarded;
- Energy utilization growth rates are lessened;
58
- Increasing traffic delays for passenger and commercial vehicles are reduced;
- The rapid pace of degradation of highway surface and capacity conditions is
curtailed, with consequent cost savings implications for funding agencies;
- Less land is required for new roads and parking areas to accommodate the
automobile; and
- The overall amenities of suburban life can be better preserved for all the
region's inhabitants, while still accommodating the demand for further growth.
NOTE: The scenarios outlined in this study place the entire 1988 to 2010 growth
increment either into a city or a higher density, mixed-use, carefully
planned construct. No sprawling, dispersed suburban development was
projected. Achieving this level of success in planning and implementing new
development patterns by the year 2010 is unlikely because of the number of
new developments that already have planning permits for traditional, low
density, single-use patterns. Success in the future will be achieved by
working with uncommitted lands and by redesigning existing development over
a much longer time frame. The extent to which we can achieve these goals
will depend on the extent to which we can change current land use practices.
2. Mixed-Use Centers Are A Viable Concept For Suburban Centers
As conceived in this study, constructs of higher-density, suburban mixed-use
centers assume continued reliance on automobiles for most forms of travel. At the
same time, their design is based upon familiarity with and general acceptance of
transit and ridesharing alternatives. Further, they incorporate the types of
pedestrian amenities and interaction that are often lacking in suburban settings.
Finally, the construct design assumes that, given the opportunity to work and shop
near home, and encouraged to take advantage of this opportunity by demand management
policies, a number of suburban dwellers will opt to do so.
With this understanding as background, it is possible to define constructs that
are clearly suburban in nature, but which draw on the efficiencies of density and
variety to make them active and successful places to live and work. Constructs can
take advantage of nearby rail stations or regional highway links as a way of
supporting their higher densities (i.e., 10-15 dwelling units per acre; commercial
floor area ratios of 1.1 to 2.0), while reducing (but by no means eliminating) the
typical suburban dependence upon the automobile. Walking constructs can offer
residential amenities that help support other nearby constructs which have higher
densities and significantly more employment opportunities.
Constructs of limited size (i.e., from 350 to 900 acres) can be sited in a
suburban area and expected to absorb development pressures for employment and
residential growth without converting the suburban setting into an urban one. They
can incorporate some of the better features of current suburban single-use centers
and make them work to better advantage for residents and employees.
59
3. Mixed-Use Centers, Through Design and Function, Can Have Tangible Local
Transportation Benefits
The nature of higher density, mixed-use centers around the nation has made them
more efficient places to travel from, to and within. Constructs encourage more
internal tripmaking--where the trip never reaches the regional highway network--
because of greater employment opportunities for residents and more retail/services to
attract residents and employees. Furthermore, a number of these internalized trips
are not made by automobile, since 1) pedestrian-oriented site planning and design, as
well as density itself, encourages pedestrian tripmaking, and 2) densities allow
greater reliance on internal transit shuttle systems.
External vehicular tripmaking is reduced as well, due to the availability of
transit services and the encouragement of transit modes through the Travel Demand
Management (TDM) polices and programs of employers and government. In addition,
densities enhance ridesharing opportunities, while active TDM policies bring
ridesharing into reality.
All these factors have the effect of reducing vehicular tripmaking during all
periods of the day and in each type of construct, relative to typical suburban
development patterns. During the peak commuting hour, the Transit Construct produces
a 28 percent reduction in vehicles accessing the regional highway network for some
trip types. In the Short Drive Construct, reductions on the order of 24 percent are
likely, while even in Walking Constructs, reductions of up to 18 percent are likely.
Off peak reductions are typically less, but can have an impact.
4. Promoting Strong Urban Growth Along With Suburban Mixed-Use Centers Gives the
Best Regional Results
The type of strong urban resurgence that the Regional Forum set as a goal for New
Brunswick and Trenton has beneficial effects on the region as a whole, particularly
when combined with suburban constructs. As shown in Chapter V, major urban growth in
employment and households, combined with the suburban constructs, reduces the growth
in total trips by nearly 20 percent. Without that type of urban growth--meaning that
it must be absorbed into the suburban constructs--the overall growth in regional
trips is reduced by only; 10 percent. Similar differences occur for the other impact
criteria. This points out, as the Regional Forum previously indicated, that strong
urban development policies must be in effect and that they can support suburban
development.
B. Next Steps
Three areas are indicated for further analysis as a result of this study: making
technical improvements to the first study, addressing more questions relevant to the
relationship of land use and transportation; and developing a methodology to
encourage land use change by those entities which control the development process.
60
1. Technical Improvements to the MSM Model and Regional Network
Technical issues that remain at the conclusion of this study include: 1) redoing
an overall regional analysis to include the cities of New Brunswick and Trenton, and
2) further expanding of the TransCAD/construct modeling effort for use as a more
refined planning tool by MSM and its constituents. These are briefly described
below:
a. Improving the Modeling of the Cities
As discussed in Chapter V, regionwide trip generation formulas do not reflect
urban tripmaking conditions well. In order to understand better the full regional --
urban and suburban -- and subregional consequences of constructs and strong urban
growth, new formulas should be developed or urban area vehicle trip reduction factors
devised. In addition, more detailed networks and traffic zones for the urban areas
need to be built (e.g., Trenton is represented by only one zone in this model) to
better distribute tripmaking within and around the periphery of the cities.
This type of modeling will also help urban areas to implement traffic and public
transportation improvements which are responsive to the changing commuting patterns
of the 1990's.
b. Expanded/Refined Use of the Study Methods
The construct vehicle trip reduction methodology, in combination with the TransCAD
regional modeling package, is used here primarily as a tool for analyzing major,
areawide development and transportation impacts. However, it can be readily refined
to forecast discrete network impacts of site specific development types at the
municipal and sub-municipal levels.
In particular, use of the spreadsheets offers analysis of vehicle trip reductions
for peak hours and off-peak periods not analyzed by the regional model in this study.
With this tool, MSM can assist the municipalities and counties in assessing land use
decisions in conjunction with the status of the transportation network.
In order to accomplish this, a more detailed network for the MSM region should be
built, including a peak hour version, as well as more refined traffic zones created
to account for particular projects.
2. Quantifying the Public and Private Costs and Benefits of the Study Findings
The finding that the vehicle trip generation of projected new development in the
region can be reduced by as much as 60 percent through changes in land use and
development patterns is dramatic. Even the lesser reductions potentially achieved by
these changed development policies are worth further consideration. The benefits of
these reduced vehicle trips to the public and private sectors deserve further
quantification. For example, if year 2010 travel demand was reduced by 20 percent
from forecast levels, what savings in highway maintenance costs would result? What
new highway links could be postponed or not constructed? What energy savings would
result? And, what are the longerterm environmental savings in terms of such measures
as improved air quality or preserved open land?
61
These questions are probably of most interest to public sector decision makers.
However, the development community would be interested to know whether these
"construct-style" projects could be built at less or at least the same cost as the
current type of projects. Will they be marketable? Are there savings afforded by
reduced parking requirements? Lower lot sizes? Lower roadway costs? Less impact
fees? Specific case studies of construct patterns should be conducted to explore
these questions, as MSM develops design guidelines and an implementation framework
for the new options.
3. Seeking Public Support for Changing Regional Development Patterns
In this study, MSM Regional Council and the consultant team have worked together
to see whether higher density, mixed-use suburban development can achieve traffic
impact reduction on a regional level. The conclusion is that indeed it can. As MSM
moves forward, this evidence needs to be supported by data from other subject areas,
including that outlined in Section 2 above, and presented to local officials,
employers, developers, and residents.
MSM recognizes the institutional strength that is invested in current land use
patterns. Besides changing the zoning ordinances and master plans specifying the
preference for low density, single-use development, banks, developers, residents
associations, and many planning professionals will need to be convinced that a new
pattern of development will be worth the risk of making a change.
MSM is a unique private, non-profit planning organization, carrying-out both
research and advocacy activities in central New Jersey. As a nongovernmental agency,
MSM has no authority to implement its recommendations, but its twenty-three-year
history in the region has given MSM considerable credibility among its constituents.
MSM staff will widely disseminate the results of this study and will use their
influence through private and public meetings and seminars to ensure that serious
consideration is given to the recommendations.
Further, the concepts outlined here will be strengthened by the goals and
objectives of the New Jersey State Development and Redevelopment Plan and the federal
Clean Air Act, as communities seek to bring their local plans into conformity with
state policies. These state initiatives will provide the needed incentive for county
and local governments to change their land use decision-making process.
The Urban Mass Transportation Administration and the New Jersey Department of
Transportation have agreed to sponsor some of the additional work outlined above.
The results of this work will determine whether the benefits of land use change can
be translated from the pages of this research report into the protection and
enhancement of the quality of life in the region.
62
REFERENCES
1. Cervero, Dr. Robert. America's Suburban Centers: A Study of the Land Use-
Transportation Link, prepared for the Office of Policy and Budget, Urban Mass
Transportation Administration, Report NO. DOT-T-88-14, Washington, D.C. (January,
1988).
2. Hooper, Kevin G. Travel Characteristics at Large-Scale Suburban Activity Centers,
National Cooperative Highway Research Program, Report 323 (October, 1989).
3. Institute of Transportation Engineers. A Toolbox for Alleviating Traffic
Congestion (1989).
4. Institute of Transportation Engineers. Trip Generation, 4th Edition (1987).
5. Kuzmyak, J. Richard, Schreffler, Eric N., and Katz, Harold, et al. Evaluation of
Travel Demand Management (TDM) Measures to Relieve Congestion, Report No. FHWA-SA-
90-005, prepared for Federal Highway Administration, Washington, D.C. (February,
1990).
6. Middlesex Somerset Mercer Regional Council. Suburban Mobility and Growth
Management: Initiatives in Central New Jersey (April, 1989).
7. Middlesex Somerset Mercer Regional Council. An Action Agenda for Managing
Regional Growth (June, 1987).
8. New Jersey State Planning Commission. Communities of Place: A Legacy for the Next
Generation, the Preliminary State Development and Redevelopment Plan for the State
of New Jersey, two volumes (November, 1988).
9. Stover, Vergil G., and Koepke, Frank J. Transportation and Land Development,
Institute of Transportation Engineers, Englewood Cliffs, New Jersey (1988).
Appendix A
Calculation of Vehicle Trip Reduction Factors
for Walking, Transit, and Short Drive Constructs
MEMORANDUM
TO: MSM Regional Council
FROM: Land Use/Transportation Study Consultant Team
DATE: October 26, 1990
SUBJECT: Construct Trip Reduction Factors
This memo is intended to serve as a working record of trip reduction expected from
Land Use Constructs, for review and final comment from appropriate parties. The trip
reduction factors have been prepared both from the perspective of land use and for
direct use in the TransCAD network model.
Based on our prior memorandum of September 25, and our study team meeting of
October 11, we have finalized our estimate of the vehicular trip reductions which can
be attributed to the various land use mix and density characteristics of our three
constructs. As you know, we have worked hard to tie the estimated reductions to
documented case study data. This memo presents the estimated reductions for each
construct on a land use basis, the methodology utilized for translating these to the
categories required for the regional network model, along with the results of each
analysis stage.
Attached are tables summarizing case study trip reduction data which are con-
sidered applicable to our constructs as base values, plus additional trip reduction
increments which can be expected for the various land use types under each of the
three constructs. These factors have been devised for use with vehicle trip rates
based on land use, similar to standard ITE trip generation rates. As we discussed,
they can also be applied to person trip rates, provided that the same vehicle
occupancy rates are used in the basic trip generation for the trend and construct
scenarios.
Reduction Factor Determination Method
The methodology for determining the trip reduction factors is summarized below.
All land use based factors were estimated for the AM peak hour, PM peak hour, offpeak
period and the average daily traffic (ADT) conditions, while the values for the
network were focused on only the AM peak hour.
1) Define land use and transit characteristics of constructs.
1
2) Determine conditions which lead to reduction of network vehicle trips through
the means of a) changing external trips to internal trips (either vehicle,
transit, or walk) and b) shifting mode of external trips (from SOV's to either
transit or rideshare modes).
3) Use data from actual case studies at existing Suburban Activity Centers to help
determine the level of trip reductions that would be experienced in our
constructs under the conditions established in 2).
4) Compare constructs to case study data conditions to see if case study
reductions apply, or if additional trip reductions can be expected beyond the
case study values due to more favorable construct conditions.
5) Sum trip reductions for each construct. The initial reduction estimates were
expressed as in indidual percentages for each relevant condition, and presented
as simple sums for "gross" reductions. For "net" reduction factors, the values
for the individual component conditions were combined as the product of sub-
factors for each percentage. This was done to avoid double counting, as the
effects of one condition remove a portion of total trips that can be affected
by other conditions. For example, transit users produced by construct
conditions are not available for carpools and vice versa. Numerically, if
individual trip reductions of 15% and 10% might be estimated for transit mode
shift and carpooling, repectively, the gross reduction would be 25% (15 + 10),
but the net reduction factor would be 0.765 (0.85 x 0.90), implying a lesser
reduction of 1 - 0.765 = 0.235 or 23.5%.
6) As a basis for comparison of construct trip making with the same development
program under"trend" conditions, the ITE trip generation rates for AM peak, PM
peak and average daily vehicle trips (with offpeak trips as a byproduct) were
applied to construct land use programs. Trip generation under construct
conditions was calculated using ITE rates modified by the estimated reduction
factors. For each construct, trips made with reduced rates were compared with
trips produced with unmodified rates, yielding estimates of trip reduction
performance compared with "trend" conditions. THIS STEP IS IMPORTANT FOR
OVERALL ANALYSIS, BUT WAS NOT USED FOR ESTABLISHING CONSTRUCT TRIP MAKING IN
THE NETWORK MODEL
7) Convert construct land use based trip reduction factors to HBW (Home Based
Work), HBO (Home Based Other), and NHB (Non Home Based) categories in the AM
peak hour, as required by the TransCAD network model.
8) Run TransCAD model for "trend" scenario and first construct alternative (AM
peak hour).
2
9) To supplement and expand on the 'AM peak hour only" operations of TransCAD,
analyze trip characteristics on a construct level versus the same land use
programs on a trend basis for AM peak, PM peak, off-peak and ADT, as set forth
in 6).
Application
In prior study phases, we have already identified the characteristics of the
constructs. This memo summarizes the identification of the trip reduction factors to
be applied to ITE rates for each land use. These steps are explained below.
1) The trip reductions from the constructs are due to a combination of factors.
These include:
- overall office/retail/housing mix;
- jobs/housing ratio;
- total employment;
- design integration;
- proximity to rail transit;
- presence of radial bus service;
- presence of internal bus service;
- constrained (and in the case of the transit construct priced) parking supply
for commercial uses; and
- increased residential density.
2) These factors in various combinations can result in varying degrees of
reduction of single occupant vehicles, due to:
- internalization of external vehicle trips, whether by vehicle, transit, or
walking; and/or
- reduction of external vehicle trips by mode shifts to transit or rideshare
modes.
3) In looking for case study data to use in measuring the trip reduction effects
of these characteristics, we found no comparable existing data for areas which
combine all of the factors as our constructs are intended to do. Probably the
largest, most recent, most consistent data set is that found in NCHRP 323,
Travel Characteristics at Large-Scale Suburban Activity Centers (October, 1989)
by Kevin Hooper of JHK1, one of our "peer review group." As shown in his report
and in other studies such as Cervero's2, existing "suburban activity centers"
or
____________________
1. Hooper, Kevin G. Travel Characteristics at Large-Scale Suburban Activity Centers,
National Cooperative Highway Research Program Report 323, (October, 1989).
3
"suburban employment centers" typically exhibit some of the above characteris-
tics, but not all. With the possible exception of Bellevue, Washington, the
existing suburban activity centers exhibit some land use mixing (particularly
office/retail), but generally not the parking restraints, clustering, rail
service, internal transit service, or pedestrian amenities which are included
as assumptions in our constructs. And, many of the suburban activity centers
are actually more like the "trend" development than the constructs. In fact,
those individual cases where higher transit use or walking rates have been
achieved are those like Bellevue which seem closer to our constructs in terms
of adding transit, providing more housing units, better integrated design,
pedestrian walkways, etc. Beyond the Hooper report, other case studies are
useful in that they measure effects of transportation demand management
measures, individual land use or transit service characteristics, but do not
consider the land use mixing.
4) Thus, a decision was made to use the average values from NCHRP 323 as a base
Indicator of trip reductions which can be achieved through mixing land uses and
Increasing density In activity centers which would otherwise be dispersed in
the "trend" (sprawl) pattern. The case study averages provide the benchmark
values, tied to reality, which can be the starting point for the regional
testing. Bear in mind that these trip reductions are fairly substantial in
themselves. Their impact, when applied regionally, should be fairly
significant.
5) Then, for each land use under each construct, additional references and
"professional judgment". are used to estimate added reductions which can be at-
tributed to the particular features we are assuming for our constructs. Some
of these are tied to the Hooper data for Bellevue and other case study data of
developments which are most like our constructs. Others are estimates, based
on work/non-work trip percentages, ratios of employment to housing, etc. For
some trip types there will be no further trip reductions beyond those indicated
in the Hooper cases.
The exception is the walking construct, which is not really a "suburban
activity center" as currently defined, and for which there is the least case
study data. The most comparable data, if available, would probably be from new
towns such as Reston or the new "neotraditional suburbs." In this case, the
study team reached a decision that the base case trip type values could not be
achieved in all cases, since the walking construct had the least similarity to
the mixed use centers studied, notably in its lack of employment opportunities.
Therefore, in the case of the walking construct, base reductions were made
smaller for some trip types through negative adjustments, as shown in the
tables.
____________________
2. Cervero, Dr. Robert. America's Suburban Centers: A Study of the Land Use--
Transportation Link, Prepared for Office of Policy and Budget, Urban Mass Transporta-
tion Administration, Report No. DOT-T-88-14, Washington, D.C. (January, 1988).
4
An example of how this method is applied, related to office trips, follows.
The numbers correspond to those shown in Page 1 of the attached tables.
For office use in the AM peak hour, NCHRP 323 shows that for "smaller centers,"
(those most similar in size to our constructs), an average of 10% of employees make a
stop within the activity center. Mode shift data from NCHRP for the non-Bellevue
suburban centers3 show that on average 1% use transit, walk or bike, and 7% carpool.
These values are put into the matrix as base case study values. It is assumed that
these reductions would be achieved as a minimum vehicle trip decrease from the trend
values in any of the constructs. Result: 0.90 x 0.99 x 0.93 = 0.83 net trip reduction
factor.
Then, for the transit construct, an additional 2% internal trip reduction is es-
timated, due to the internal transit system and improved walking conditions. An
additional 12% transit use is estimated, based on Bellevue's 10% transit mode share
(with radial bus system) plus an estimated 2% reduction due to the rail access.
Reductions due to ridesharing are not increased over the case study value. Result:
0.83 (from base case, above) x 0.98 x 0.88 = .71 net trip reduction factor (as shown
in page 1 of the Tables).
For the short drive construct, reductions due to increased internal walking are
increased by 1%, and carpooling is increased 8% over the base values, based on
Cervero's findings of 15% carpool rates for large and medium mixed use centers.
Result: 0.83 (from base case, above) x .99 x .92 = .75 net trip reduction factor.
For the walking construct, office trips will be a much smaller proportion of total
travel, but, due to their location they will attract a large proportion of employees
and visitors from within the construct. Thus, the 10% internal trip reduction from
the base case is deemed valid for office uses in this construct. However, no
external transit use or carpooling increases are predicted for the walking construct,
due to the absence of new regional services and the low proportion of use in
commercial space, which would not justify adding local bus service. Thus, these
values are listed as negative values (translated into factors greater than one) in
the table. Result: 0.83 x 1.01 x 1.07 = 0.90 net trip reduction factor.
Pages 1, 2, and 3 of the attached tables list trip reductions by land use for each
construct. Then, Page 4 of the tables summarizes the total trip reductions by
construct.
____________________
3. For the transit use value, Bellevue is excluded from the base case value due to
its atypical, higher level of transit service which would raise the base value too
high to be used in all cases.
5
As we have talked about before, it is difficult to substantiate every factor as
applied to every trip type. However, it should be reasonable to predict, as we have
done here, how each construct stacks up against the current suburban activity centers
for each type of trip. Looking at the literature, the values we have calculated here
seem within ranges which have been measured in other case studies such as those
presented in the ITE 1987 Trip Generation manual4 and the Stover and Koepke text
Transportation and Land Development.5
Similarly, the February, 1990 FHWA report, Evaluation of Travel Demand Management
Measures to Relieve Congestion6, states that, for programs of Transportation Demand
Management (TDM) measures in combination, "trip reductions in the range of 20% to 40%
can be the norm, rather than the exception." Although our study purposely does not
attempt to isolate TDM program effects, TDM programs such as constrained and priced
parking, TMA activity, rideshare incentives, and staggered work hours are considered
part of each construct "package" along with the land use mix, density and design
features which are the focus of this analysis effort.
We welcome the comments of the "peer review group" in adding comparative data.
Also, as the constructs become incorporated into existing town centers, shopping
centers, etc., it may be possible to adapt the trip reduction factors to reflect
actual conditions.
Attachments: Tables, Charts
____________________
4. Institute of Transportation Engineers, Trip Generation, 4th Edition, (1987), pp.
17-21.
5. Stover, Vergil G. and Koepke, Frank J. Transportation and Land Development,
Institute of Transportation Engineers, Englewood Cliffs, New Jersey (1988), pp. 47-
48.
6. Kuzmyak, J. Richard, Schreffler, Eric N. and Katz, Harold et al. Evaluation of
Travel Demand Management (TDM) Measures to Relieve Congestion Report No. FHWA-SA-90-
005, prepared for Federal Highway Administration, Washington D.C. (February, 1990),
p. 28.
6
MSM Trip Reduction Relationships: 10/20/90
Land Use Type: COMMERCIAL (OFFICE) TRIPS
AM Peak PM Peak Off Peak
Values Refer. Values Refer. Values Refer.
CASE STUDIES: MIXED USE DEV'T/SUBURBAN ACTIVITY CENTERS
Construct/Reduction Type:
Base Reductions for ALL Constructs:
Internal Trips: 10% 1 10% 11 25% 21
(All Modes)
External-Transit 1% 2 1% 12 0%
External-Carpool 7% 3 7% 13 0%
Subtotal (Gross): 18% 18% 25%
Additional Reductions/Totals by Construct:
TRANSIT CONSTRUCT:
Internal-Vehicle 0% 0% 0%
Internal-Transit 1% 4 1% 14 1% 22
Internal-Walking 1% 5 1% 15 10% 23
External-Transit 12% 6 12% 16 0%
External-Carpool 0% 0% 0%
CONSTRUCT TOTAL (Gross): 32% 32% 36%
* Net Ratios = 0.71 0.71 0.67
SHORT DRIVE CONSTRUCT:
Internal-Vehicle 0% 0% 0%
Internal-Transit 0% 0% 0%
Internal-Walking 1% 7 1% 17 5% 24
External-Transit 0% 0% 0%
External-Carpool 8% 8 8% 18 0%
CONSTRUCT TOTAL (Gross): 27% 27% 30%
* Net Ratios = 0.75 0.75 0.71
WALKING CONSTRUCT:
Internal-Vehicle 0% 9 0% 19 0% 25
Internal-Transit 0% 0% 0%
Internal-Walking 0% 0% 0%
External-Transit -1% 10 -1% 20 0%
External-Carpool -7% 10 -7% 20 0%
CONSTRUCT TOTAL Gross): 10% 10% 25%
* Net Ratios = 0.90 0.90 0.75
* Ratios combine individual percentages as a product of corresponding reduction
factors.
REFERENCES:
1,11 Hooper, p. 72, Table 17 Average for smatter centers, stop within SAC, 10%
2,12 Hooper, p. 68
3,13 Av. mode split for non-Bellevue sites: 92% auto, 7% carpool, 1%
bus/walk/bike
4,14 H/SH estimate
5,15 H/SH estimate
6,16 H/SH estimate based on Bellevue 10% transit/bike/walk mode share with radial
bus and 2% due to rail access
7,17 H/SH estimates: slightly higher walk commute due to more housing nearby
8,18 Cervero, America's Suburban Centers, p. 955 - increase due to density/Land
use mix
9,19 H/SH estimates: base reduction applicable to commercial trips because nature
and location make office uses likely to attract local workers
10,20 H/SH estimates: reduced from base due to low proportion of office use in
construct
21 Hooper, p. 72, Table 17 -- midday trips by office workers within SAC -
smaller centers
22,23 H/SH estimates: marginal diversion to transit beyond case study value;
Large increase in walk trips due to density, design features
24 H/SH estimate: increase in walk trips due to more retail integration and
design features, but less than that for transit construct due to greater
distances
25 H/SH estimate: base rates apply for offpeak trips due to high ratio of
commercial to office space, design features
MSM Trip Reduction Relationships: 10/20/90
Land Use Type: RETAIL TRIPS
AM Peak PM Peak Off Peak
Values Refer. Values Refer. Values Refer.
CASE STUDIES: MIXED USE DEVELOPMENT/SUBURBAN ACTIVITY CENTERS
Construct/Reduction Type:
Base Reductions for All Constructs:
Internal Trips:
(all modes) 14% 1 14% 6 23% 11
External-Transit 0% 0% 1% 12
External-Carpool 0% 0% 1% 13
Subtotal (Gross): 14% 14% 25%
Additional Reductions/Totals by Construct:
TRANSIT CONSTRUCT:
Internal-Vehicle 0% 0% 0%
Internal-Transit 0% 0% 2% 14
Internal-Walking 1% 2 1% 7 10% 15
External-Transit 2% 3 2% 8 0%
External-Carpool 0% 8% 0%
CONSTRUCT TOTAL (Gross): 17% 17% 37%
* Net Ratios = 0.83 0.83 0.67
SHORT DRIVE CONSTRUCT:
InternaL-Vehfcle 0% 0% 0%
Internal-Transit 0% 0% 2% 16
Internal-Walking 1% 4 1% 9 5% 17
External-Transit 0% 0% 0%
External-Carpool 0% 0% 0%
CONSTRUCT TOTAL (Gross): 15% 15% 32%
* Not Ratios = 0.85 0.85 0.70
WALKING CONSTRUCT:
Internal-Vehicle 0% 5 0% 10 0% 18
Internal-Transit 0% 0% 0%
Internal-WaLking 0% 0% 0%
External-Transit 0% 0% -1% 19
External-Carpoot 0% 0% -1% 20
CONSTRUCT TOTAL (Gross): 14% 14% 23%
* Net Ratios = 0.86 0.86 0.77
* Ratios combine individual percentages as a product of corresponding reduction
factors.
REFERENCES:
1,6,11 Hooper, p. 89 -- average of smaller activity centers (Bellevue, South
Coast Metro and Southdate)
12,13 Hooper, p. 89 average of smatter activity centers (Bellevue, South Coast
Metro and Southdale)
2,3,7,8 Slightly higher retail commute trips by radial transit, walking
(estimate)
4 Slightly higher retail commute trips by walking -- estimate
5,10 Base values hold for retail employment due to relatively low number of
jobs to be fitted by high number of households in construct
14,15 For transit construct, 12% increase in internal offpeak trips estimated
over case study values - due to higher density, design, constrained
parking
16,17 For short drive, moderate increase in retail offpeak internal trips due
to better design, clusteering (estimate)
18 For walking construct, 1base values assumed to hold for offpeak due to
large number of households to support neighborhood commercial center
19,20 Base values do not apply due to low square footage of retail -- not large
enough center to attract carpool, transit offpeak trips.
MSM Trip Reduction Relationships: 10/20/90
Land Use Type: RESIDENTIAL TRIPS
AM Peak PM Peak Off Peak
Values Refer. Values Refer. Values Refer.
CASE STUDIES: MIXED USE DEV'T/SUBURBAN ACTIVITY CENTERS
Construct/Reduction Type:
Base Reductions for Transit and Short Drive Constructs:
Internal Trips:
(All Modes) 27% 1 27% 13 7% 25
External-Transit 0% 0% 0%
External-Carpool 0% 2 0% 14 0%
Subtotal (Gross) 27% 27% 7%
Additional Reductions/Totats by Construct:
TRANSIT CONSTRUCT:
Internal-Vehicle 1% 3 1% 15 3%26
Internal-Transit 1% 4 1% 16 3%27
Internal-Walking 4% 5 4% 17 4%28
External-Transit 10% 6 10% 18 2%29
External-Carpool 5% 7 5% 19 0%
CONSTRUCT TOTAL (Gross): 48% 48% 19%
* Net Ratios = 0.59 0.59 0.82
SHORT DRIVE CONSTRUCT:
Internal-Vehicle 0% 8 0% 20 4%30
Internal-Transit 0% 0% 2% 31
Internal-Walking 0% 9 21 4% 32
External-Transit 0% 0% 0%
External-Carpool 5% 10 5% 22 0%
CONSTRUCT TOTAL (Gross): 32% 32% 17%
* Net Ratios = 0.69 0.69 0.84
WALKING CONSTRUCT:
Internal-Vehicle 0% 0% 3% 33
Internal-Transit 0% 0% 0%
Internal-Walking -17% 11 -17% 23 5% 34
Extetnal-Transit 0% 0% 0%
External-Carpool 10% 12 10% 24 0%
CONSTRUCT TOTAL (Gross): 20% 20% 15%
* Net Ratios = 0.77 0.77 0.86
* Ratios combine individual percentages as a product of corresponding reduction
factors.
REFERENCES:
1,13 Hooper, p. 94, average for smaller centers.
2,14 No data found on carpool rates per residential unit in suburban centers.
3,4,5 Moderate increases estimated due to increase in housing units, design,
density Increases bring total to 33% -- compare to Hooper, p. 94
6,18 NYC commuters estimated at 10%
7,19 Moderate carpool increase seen as result of higher residential density
8,9 No increases over base data seen for short drive
10,22 Moderate carpool increase seen as result of higher residential density
11,23 Reduced internal trips beyond base due to fewer employment opportunities
within zone
12,24 Residential clustering assumed to foster carpooling - 10% of HBW trips
15,16,17 Moderate increases estimated due to increase in housing units, design,
density Increases bring total to 33% -- compare to Hooper, p. 94
20,21 No increases over base data seen for short drive
25 50% of non-employee trips (14%) internal to construct - H/SH estimate
26,27,28 Overall 10% increase in internal tripmaking due to constrained parking,
land use mix
29 Low off-peak transit use increase -- trips to NYC
30,31,32 Overall increase in internal tripmaking due to fewer workers/hh (more
families), land use mix, design
33,34 Increase over base condition in internal offpeak tripmaking, due to larger
HH size, more families, fewer workers, clustering, presence of shopping,
services within construct
Appendix B
MSM Region Traffic Zones and 1988 Calibration Network
MSM UMTA Study, Transportation Analysis Zones
Click HERE for graphic.
MSM Calibration Network - 1988
Click HERE for graphic.
Appendix C
TransCAD Package Steps and Trip Generation Equations
1
Appendix 3
Models - Calibration
The following discussion detailing the steps involved in running model
applications in TransCAD is being supplied to MSM staff to supplement
tutorial and seminar training already completed.
The model execution involves creating a database network, building a
matrix table of shortest paths, determining trip distribution with the
gravity model, assigning the trips to the network and evaluating the
results. This is accomplished through a series of models and
worksheets executed sequentially. They will be discussed in the order
which they occur.
I. Data Assignment Network
The MSM application database contains a line database commonly refered
to as the network. When it is used as part of an application line
database, it will be referred to as a database network. When being
used as input to one of the transportation models, it will be referred
to as the assignment network.
To create a database assignment network, select all the links in a
line application database that will be used in the assignment process.
Select all the centroids that will be used in zonal interchanges. It
is not necessary to select all links and centroids in a line database.
If a small area is to be studied such as Mercer County, only those
links and centroids need to be selected from the three county set.
From the procedures menu, choose Network Builder (80386). Fill in the
template with information on the name and location of the new network.
A listing of the node fields of the line database will be displayed.
Select those fields that will be used in any calculations based on the
nodes in the network. Node fields that may be included in the
application network are those that contain transfer penalties.
Because the MSM application database does not currently contain any
information on transit routes, no fields should be selected.
The next list is of the available fields on the links in the network.
Select the fields containing generalized cost and capacity for the
link. The generalized cost of any link is the free flow travel time
for that link plus any additional cost (in minutes) that would be
incurred by any user of the link. A toll fee is an example of an
additional cost to a user of that link. The current version of the
MSM application network only uses the free flow travel time in the
generalized cost for most links. The exception to this
2
is the centroid connectors. Centroid connectors are given an
additional penalty of 999 minutes to every user on the link. This is
done to prevent trips from passing through a zone via the centroid
connectors on the way to a destination zone. Because of this, travel
times for all origin/destination pairs will be increased by 1998
minutes (999 when leaving a zone and 999 when entering) The l998
minutes are later removed to arrive at the true travel time. The
resultant file will be used to create a shortest path table and assign
trips.
II. Matrix of Travel Time
With the assignment network built, the next step is to calculate the
shortest path between zones which are represented as centroid
connectors. TransCAD calculates the shortest path based on the
generalized cost for a set of links whether it is in travel time or
distance. First set the current layer to a node layer of a line
database, and select all the centroids and external stations that will
be used in the travel time matrix table. From the procedure list,
choose Pathtabl, and fill in the template with file name, location and
a descriptive label for the table. Choose a network file created in
Step I. Enter the weights for link fields contained in the network.
For our discussion, enter 1 for generalized cost and 0 for link
capacity. The cost of the path is a linear equation (Field 1 * Weight
+ Field 2 * Weight ... ). The resulting matrix table of zone to zone
travel times will be used in the gravity model in Step 3. Because it
is a zone to zone matrix, the internal zone travel time is not
calculated and is represented in the table as a missing value.
Because of the addition of 999 to all centroid connectors, every cell
in the travel time matrix table will be 1998 too high. This value-
can be removed by creating a second matrix table in the Table Editor
using the same set of centroids and external stations as were used to
create the travel time matrix table in Pathtab1. Fill the new table
with 1998. Using the Table Manipulations procedure, subtract the 1998
table from the travel time matrix table. This will yield a table with
the correct travel time except for internal trips (the diagonal) which
will be -1998. This number must be changed to either missing (press
delete key) or any amount of positive travel time in minutes. By
leaving the diagonal value as missing, all trips generated are forced
onto the network. The lower the diagonal number, the more intrazonal
trips will occur. Inversely, the higher the intrazonal time, the
fewer the number of trips. Edits to the diagonal must be done one
cell at a time, either in a different matrix table where they can be
manipulated and added to the travel time matrix, or the diagonal of
the travel time matrix can be edited directly.
3
III. Gravity Model
Trip distribution is accomplished through the gravity model. To
execute the gravity model, you will need to create two table files,
one with production and the other with attractions. The structure of
these files must be the same as the matrix table created in the
Pathtab1 step. There are three choices of gravity models, Origin
Constrained, Destination Constrained or Doubly Constrained. If the
Double Constrained model is used, then the production and attractions
(P's and A's) must be balanced. To balance P's and A's, choose the
Balance procedure and balance P's and A's to either P's or A's; or use
Balance2 to adjust both P's and A's. To accomplish the balancing,
first, import the raw productions and attractions into the node list,
and run either Balance or Balance2. Copy the results to the table
files through the table menu. From the procedure list, choose Grav04.
Select the type of gravity model to be used (Origin, Destination or
Doubly Constrained). Enter the output table file name and path
location. Use a generalized cost table created in Step II. Enter the
name of the production and/or attraction table to be used (this is
based on the type of gravity model used). Select the type of
functional form to be used, either negative exponential or inverse
power. Finally, enter the cost function (friction factor) to be used.
The output file will contain a zone to zone matrix of the trip
distribution (O/D demand).
IV. Assignment
The assignment model procedure brings together the output produced in
Steps II and III. To run an assignment, select the capacity
restrained assignment model from the traffic assignment menu. Enter
the name of the solution file and where it is located. Select a
network created in Step I. Select the fields with generalized cost
and link capacity data. Enter the values for alpha and beta in the
Bureau of Public Roads (BPR) formula (.15 and 4.0 respectively).
Enter the trip distribution table created in Step III. Finally, enter
the number of iterations to be run if closure is not made. Twenty
iterations are recommended. At the completion of the assignment, the
user will be prompted to input the fields in the network application
database that will contain the forward and reverse flows. Forward
flows are those traveling from Node A to Node B on any link. Reverse
flows are trips from Node B to Node A on two-way links. All two-way
links will contain both forward and reverse flows, while one-way links
will contain only forward flows.
V. Measures of Effectiveness
4
Post processing of assignments is done both inside and outside of
TransCAD. Numerous measures of effectiveness were used to monitor the
calibration process and gauge the effect of changes to scenarios.
Measure of effectiveness used during post processing included Root
Mean Square Error (RMSE), Volume to Capacity Ratio (V/CR), Level of
Service (LOS), congested travel time, average trip length (in both
miles and minutes), Vehicle Miles of Travel (VMT), Average Speed and
percent of intrazonal trips.
RMSE
RMSE is the only measure that must be calculated outside of Trans CAD.
The remaining can be calculated using the data editor. The formula to
calculate RMSE is as follows:
Click HERE for graphic.
where: sim = Simulated Flow
obs = Observed Flow
n = Number of Observed Counts
The resulting value indicator of the effectiveness of the simulation.
The caveat to this is if the observed counts are taken at locations
with large variations day to day, the RMSE is less reliable. Observed
counts along major arterials are most desirable because of the
consistency of the daily volumes, while counts along local or
neighborhood streets are less desirable. RMSE can be applied to the
network at regional levels as well as subregional levels (such as a
separate RMSE calculated for each county) dependant on the number of
counts available.
Traffic Counts
Traffic count data was supplied by NJDOT for state roads in the MSM
region from their traffic survey program. The time frame of the
counts ranged from 1986 to 1990. Where the 1988 data was available, it
was used as is. On the segments where there was no data for 1988, the
counts were adjusted by weighting to represent a 1988 count. The
distribution of available data throughout the MSM region is not as
even as we would like with most of the counts along Routes 1, 130, 31,
and Interstate 195/295 in Mercer and Middlesex counties, Somerset
County contained only three points with usable count data. This lack
of observed traffic data brings up concern about calibration volumes
in the south Somerset sub-region. Because it is an isolated area, its
effects on the rest of the regional calibration would be minimal. If
the Somerset area will be used in the future for a more detailed
5
study, it is recommended additional traffic counts be obtained to
assist in refining the calibration and subsequent applications.
Volume to Capacity Ratios/LOS
Volume to Capacity Ratio is used to determine simulated levels of
service. This is a link level measure that should be looked at with
an area wide approach. Groups of links should be compared, not
individual link segments. This can be used as another measure to
judge the effectiveness of the calibration process. It could also be
used as an indicator of possible future conditions. Again, it should
only be taken in a general area context. LOS categories used were
taken from the Highway Capacity Manual:
A = 0.0 - 0.4
B = 0.4 - 0.7
C = 0.7 - 0.8
D = 0.8 - 0.95
E = 0.95 - 1.05
F = 1.05 - 1.5.
Congested Travel Times and Speeds
Congested Travel Time and Speeds is another good measure of the
effectiveness of the calibration process. It is similar to V/CR and
LOS in that it should be used on an area basis when compared to real
world conditions. By comparing them to free flow travel time and
speeds, the effect of the simulation becomes readily apparent. To
calculate congested travel time and speeds, apply the following
formula to the links in the network.
Congested Travel Time = timeo [1 + A(Vt/C]
where: timeo = free flow travel time
A = alpha from BPR formula
B = beta from BPR formula
Vt= calculated flow
C = capacity
Congested speeds are derived from the congested travel time.
Congested travel time/distance * 60.
Average Trip Length in Miles and Minutes
During calibration, average trip length in miles and minutes is an
indicator of the improvement of the calibration process. Average trip
lengths in miles are calculated by simply taking the sum of the miles
traveled divided by the sum of the trips assigned. For the average
length of trip in minutes and the sum of minutes of travel over the
sum of the trips assigned will yield the average length of trips in
minutes. Targets used were 8 miles and 20 minutes in length which
were based on Montgomery County, Maryland travel time.
6
Vehicle Miles of Travel
VMT is used as an indicator of the increased use of a network during
scenario applications. VMT is calculated by summing the number of
trips and multiplying that by link length and number of lanes. The
difference between calibration VMT and scenario application VMT can be
due to an increase in the P's and A's, or excessive congestion causing
increased trip length. If there is little or no change in the average
trip length, then the increase VMT would be due to an increase in the
number of trips on the system.
Table 5
Trip Generation Equations
Independent Variables
DU - Dwelling Units - all sizes/types
RE - Retail Employees
NE - All Other Employees
US - University Students
Present (1980 - 1990)
Production
HBW 2.48 * DU
HBO 6.64 * DU + 0.84 * US
NHB O.25 * DU =0.39 * US + 2.92 * RE +1.13 *NE
Attractions
HBW 0.57 * US +1.84 * RE + 1.89 * NE
HBO 0.99 * DU + 0.81 * US + 23.24 RE + 0.45 * NE
NHB 0.25 * DU + 0.39 * US + 2.92 RE + 1.13 * NE
Future (2005 - 2010)
Production
HBW 2.34 * DU
HBO 6.03 * DU + 0.84 * US
NHB 0.25 * DU + 0.39 * US + 3.47 RE +1.16 * NE
Attractions
HBW 0.57 * US + 1.89 * RE + 1.89 * NE
HBO 0.99 * DU + 0.81 * US + 20.56 RE + 0.47 * NE
NHB 0.25 * DU + 0.39 * US + 3.47 * RE + 1.16 * NE
APPENDIX D: DEVELOPMENT OF LAND USE DATA
FOR MUNICIPALITIES AND ZONES
General Description
The modeling process required the formulation of land use data at
the municipal and zone levels. The basic units of analysis were:
number of dwelling units and students (to represent the population),
and retail and non-retail employment. The most up-to-date municipal
population available at the commencement of the study was for 1988;
therefore, this was chosen as the base year. The future year 2010 was
selected, in part, because of the municipal employment and population
projections made available by the counties to satisfy the requirements
of the State Plan cross-acceptance process.
Tables 1 and 2 present the derivation of the traffic zone structure
itself. New Jersey Department of Transportation provided data from
four of their modeling efforts: the Route 1 Corridor Study, the North
New Jersey Model, the Route 130 Study and the Route 518 Study.
Because of some redundancy among models, we found it necessary to use
only the first three in establishing the boundaries of the traffic
zones. New zones were delineated in the portions of the region
outside the scope of these existing models.
Tables 3 and 8 show the municipal population and employment figures
we assumed for 1988, 2010 Trend, Scenario 1 and Scenario 2. While the
total number of dwelling units and employment is held constant for the
region in Trend and Scenarios 1 and 2, these tables illustrate the
fundamental differences in the allocation of growth in each of the
three cases. The Trend assumes that the regional distribution of
growth among municipalities will occur as projected by the three
counties and MSM. In Scenario 1, the cities receive a much larger
share of the growth than projected in Trend, while the remainder is
absorbed by the constructs. In Scenario 2, the cities are assumed to
grow only by the 2010 Trend amount, with the increment allocated among
the constructs.
Tables 4 to 7 and 9 to 12 show the assumptions made about the
distribution of land uses at the zone level. Data from the NJDOT
models and MSM's Current Development Survey was utilized to calculate
the figures. The municipal totals were used as controls for the 1988
and 2010 Trend allocation process. The 1988 numbers were derived from
1980 zone data, in the case of the Route 1 Study portion, and 1986
zone data for the North New Jersey and Route 130 areas. Only the
Route 1 and Route 130 models included future year zone data (2005 and
2006, respectively), and this was used to guide the allocation process
for the 2010 Trend. Zoning ordinances and other in-house land use
information were utilized whenever necessary, particularly in the
portions of the region where new zones were created.
For Scenarios 1 and 2, it was determined that four additional zones
were needed to accommodate walking constructs. Zones 200 to 203 were
established for this purpose, having been split off from much larger
zones 4, 88, 189 and 194. This step was taken because it was assumed
that in these particular areas, traffic behavior in the remainder of
the zones outside the walking constructs would not be like that within
the constructs and should be modeled differently.
Table 1: Derivation of Zones from Existing Studies
Route 1 North New Jersey Route 130 New
Cranbury Twp. X
East Brunswick Twp. X
Helmetta Boro X
Jamesburg Boro X
Milltown Boro, X
Monroe Twp. X
New Brunswick City X
North Brunswick Twp. X X
Plainsboro Twp. X
South Brunswick Twp. X
South River Boro X
Spotswood Boro X
Franklin Twp. X X
Hillsborough Twp. X
Manville Boro, X
Millstone Boro X
Montgomery Twp. X
Rocky Hill Boro X
So. Bound Brook Boro X
East Windsor Twp. X
Ewing Twp. X X
Hamilton Twp. X X
Hightstown Boro X
Hopewell Boro X
Hopewell Twp. X
Lawrence Twp. X X
Pennington Boro X
Princeton Boro X
Princeton Twp. X
Trenton City X
Washington Twp X
West Windsor Twp. X
Note: 'Route 1,' 'North New Jersey', and 'Route 130' refer to zones
drawn from modeling efforts previously undertaken by the NJ Dept. of
Transportation; otherwise, now zones were created as indicated by
'New.'
ZONEMODS
Table 2: Derivation of the Study Zone Structure
Route 1 North NJ Route 130 New
Zone Municipality Model Model Model Zone
1 Franklin 1
2 Franklin 2
3 Montgomery/Rocky Hill 3
4 Montgomery 4
5 Montgomery 5
6 Montgomery 6
7 Montgomery 7
11 Montgomery 3
9 Montgomery 9
10 Princeton Township 10
11 Princeton Township 11
12 Princeton Township 12
13 Princeton Township 13
14 Princeton Township/Boro,14
15 Princeton Boro 15
16 Princeton Township 16
17 Princeton Township 17
15 Princeton Township 15
19 Princeton Boro 19
20 Princeton Township 20
21 Franklin 21
22 Franklin 22
23 South Brunswick 23
24 South Brunswick 24
25 South Brunswick 25
26 South Brunswick 26
27 South Brunswick 27
28 Plainsboro 23
29 West Windsor 29
30 West Windsor 30
31 West Windsor 31
32 West Windsor 32
33 Plainsboro 33
34 Plainsboro 34
35 Plainsboro 35
36 Plainsboro 36
37 South Brunswick 37
39 South Brunswick 38
39 South Brunswick 39
40 South Brunswick 40
41 South Brunswick 41
42 South Brunswick 42
43 Plainsboro 43
44 Plainsboro
45 Plainsboro 45
46 Plainsboro 46
47 West Windsor 47
48 West Windsor 48
49 West Windsor 49
50 West Windsor 50
51 West Windsor 51
52 East Windsor 52
53 East Windsor 53
54 West Windsor 54
55 Cranbury 55
56 Cranbury 56
ZONEMOD2
Table 2: Derivation of the Study Zone Structure
Route 1 North NJ Route 130 New
Zone Municipality Model Model Model Zone
57 Cranbury 57
58 Cranbury 58
59 Cranbury 59
60 South Brunswick 60
61 South Brunswick 61
62 South Brunswick 62
63 Cranbury 63
64 Cranbury 64
65 -- 65
66 Cranbury 66
67 Cranbury 67
63 -- 69
69 Lawrence 69
70 East Windsor 70
71 East Windsor 71
72 East Windsor 72
73 East Windsor 73
74 Hightstown 74
75 Hightstown 75
76 East Windsor 76
77 East Windsor 77
78 East Windsor 78
79 East Windsor 79
50 Lawrence 50
51 Hamilton 81
82 Lawrence 92
83 Lawrence 83
84 West Windsor 34
85 West Windsor 95
86 West Windsor 86
87 West Windsor 87
88 Franklin 88
89 Franklin 89
90 North Brunswick 90
91 South Brunswick 91
92 North Brunswick 92
93 North Brunswick 93
94 North Brunswick 94
95 North Brunswick 93
96 Lawrence 96
97 Lawrence 97
98 Hillsborough 1097
99 Hillsborough 1098
100 Hillsborough 1096
101 Hillsborough 1101
102 Hillsborough 1102
103 Hillsborough 1100
104 Millstone 1099
105 Manville 1064
106 Manville 1065
107 Manville 1066
109 Franklin 1092
109 Franklin 1091-P
110 Franklin 1093
111 Franklin 1087
112 Franklin 1088
ZONEMOD2
Table 2: Derivation of the Study Zone Structure
Route 1 North NJ Route 130 New
Zone Municipality Model Model Model Zone
113 Franklin 1086
114 Franklin 1095
115 Franklin 1089
116 Franklin 1090
117 New Brunswick 617
118 New Brunswick 619
119 New Brunswick 619
120 New Brunswick 620
121 New Brunswick 621
122 New Brunswick 616
123 New Brunswick 615
124 New Brunswick 614
125 New Brunswick 613
126 New Brunswick 622
127 North Brunswick 623-p
128 North Brunswick 627-p
129 East Brunswick 629
130 South River 637
131 South River 639
132 South River 639
133 East Brunswick 630
134 Milltown 629
135 East Brunswick 632
136 East Brunswick 633
137 East Brunswick 631
139 East Brunswick 634
139 East Brunswick 633
140 East Brunswick 636
141 Spotswood 659
142 Spotswood 660
143 Helmetta 661
144 Monroe 46
145 Jamesburg 47
146 Monroe 43
147 Monroe 49
149 Monroe 50
149 Monroe 32
150 Monroe 51
151 Monroe 53
152 Washington 69
153 Washington 69
154 Washington 72
155 Washington 71
156 Washington 70
157 Washington 74
153 Washington 75
159 Washington 76
160 Washington 79
161 Washington 78
162 Washington 77
163 Washington 73
164 Hamilton 92
165 Hamilton 35
166 Hamilton 90
167 Hamilton 95
168 Hamilton 94
ZONEMOD2
Table 2: Derivation of the Study Zone Structure
Route North NJ Route 130 New
Zone Municipality Model Model Model Zone
169 Hamilton 93
170 Hamilton 89
171 Hamilton 88
172 Hamilton 34
173 Hamilton 81
174 Hamilton 87
175 Hamilton 91
176 Hamilton 92
177 Hamilton 86
178 Hamilton 83
179 Lawrence 140-p
180 Trenton 147
181 Ewing 146
182 Ewing x
183 Hopewell Township x
184 Hopewell Township x
185 Pennington x
186 Hopewell Township x
187 Hopewell Township x
188 Hopewell Township x
189 Hopewell Township x
190 Hopewell Township x
191 Hopewell Township x
192 Hopewell Township x
193 Hopewell Boro x
194 Hopewell Township x
195 Hopewell Township x
196 South Bound Brook x
Note: Any zone number with the suffix '-p' indicates that only a
portion of that zone was used to create a now one.
ZONEMOD2
Table 3: Dwelling Unit Growth Assumptions -
1988, 2010 Trend, 2010 Scenarios
Click HERE for graphic.
*Note: For the purposes of this study, it was assumed that the
number of households, derived from population estimates, is
equal to the number of dwelling units which would generate
traffic.
Sources: MSM Regional Council - "Estimated Average Household Size in
1980, 1984, and 2000;" NJ Dept. of Labor - Population
Estimates; Middlesex, Somerset, Mercer Counties - Population
Projections.
HHGROW
Table 4: Derivation of Dwelling Units by Zone - 1980/1986, 1988
1980/1986 1988 Estimated
Dwelling Dwelling University
Zone Municipality Units Units Students
1 Franklin 223 223
2 Franklin 88 138
3 Montgomcry/Rocky Hill 643 653
4 Montgomery 740 795
5 Montgomery l07 195
6 Montgomery 938 959
7 Montgomery 175 195
3 Montgomery 121 121
9 Montgomery 33 393
10 Princeton Township 203 203
11 Princeton Township 300 365
12 Princeton Township 1,725 1,725
13 Princeton Township 787 807
14 Princeton Township/Boro 1,073 1,073 3,945
15 Princeton Boro 2,132 2,132 650
16 Princeton Township 392 727
17 Princeton Township 358 375
15 Princeton Township 399 449
19 Princeton Boro 576 576 915
20 Princeton Township 372 372 190
21 Franklin 23 28
22 Franklin 145 145
23 South Brunswick 1,400 1,288
24 South Brunswick 1,955 2,097
25 South Brunswick 130 425
26 South Brunswick 158 158
27 South Brunswick 217 423
23 Plainsboro 326 551
29 West Windsor 250 250
30 West Windsor 5 20
31 West Windsor 8 8
32 West Windsor 600 600
33 Plainsboro 39 39
34 Plainsboro 10 10
35 Plainsboro 6 6
36 Plainsboro 224 224
37 South Brunswick 21 42
38 South Brunswick 326 707
39 South Brunswick 93 92
40 South Brunswick 172 611
41 South Brunswick 604 1,669
42 South Brunswick 94 178
43 Plainsboro 77 27
44 Plainsboro 992 992
45 Plainsboro 1,669 4,997
46 Plainsboro 87 97
47 West Windsor 248 466
48 West Windsor 551 781
49 West Windsor 154 494
50 West Windsor 80 80
51 West Windsor 252 392
52 East Windsor 2,616 2,642
53 East Windsor 108 109
54 West Windsor 232 420
55 Cranbury 4 6
ZONEPOP
Table 4: Derivation of Dwelling Units by Zone - 1980/1986, 1988
1980/1986* 1988 Estimated
Dwelling Dwelling University
Zone Municipality Units Units Students
56 Cranbury 63 273
57 Cranbury 14 14
58 Cranbury 358 353
59 Cranbury 96 96
60 South Brunswick 49 47
61 South Brunswick 95 283
62 South Brunswick 124 137
63 Cranbury 34 34
64 Cranbury 92 102
65 -- -- --
66 Cranbury 35 15
67 Cranbury 15 15
68 -- -- --
69 Lawrence 22 22
70 East Windsor 2,648 2,648
71 East Windsor 30 56
72 East Windsor 1,007 1,033
73 East Windsor 543 1,169
74 Hightstown 630 691
75 Hightstown 1,066 1,127
76 East Windsor 161 391
77 East Windsor 150 176
78 East Windsor 301 3V
79 East Windsor 100 126
80 Lawrence 825 978
81 Hamilton 1,245 3,021
82 Lawrence 692 692
83 Lawrence 27 627
84 West Windsor 5 5
35 West Windsor 190 790
86 West Windsor 130 130
87 West Windsor 10 10
88 Franklin 191 627
89 Franklin 155 455
90 North Brunswick 1,308 2,663
91 South Brunswick 105 189
92 North Brunswick 479 1,129
93 North Brunswick 1,721 2,399
94 North Brunswick 1,211 1,211
95 North Brunswick 2,765 3,311
96 Lawrence 232 993
97 Lawrence 1,012 2,558
98 Hillsborough 1,045 1,062
99 Hillsborough 2,059 2,654
100 Hillsborough 773 1,167
101 Hillshorough 1,526 1,526
102 Hillsborough 1,017 1,017
103 Hillsborough 1,356 1,739
104 Millstone 180 180
105 Manville 1,729 1,679
106 Manville 1,104 1,055
107 Manville 1,193 1,134
108 Franklin 613 613
109 Franklin 1,622 342
110 Franklin 310 310
ZONEPOP
Table 4: Derivation of Dwelling Units by Zone - 198O/1986, 1988
1980/1986* 1988 Estimated
Dwelling Dwelling University
Zone Municipality Units Units Students
111 Franklin 601 601
112 Franklin 1,049 2,033
113 Franklin 1,937 1,957
114 Franklin 1,887 2,373
115 Franklin 2,309 2,309
116 Franklin 1,343 1,343
117 New Brunswick 1,637 1,637
118 New Brunswick 1,536 1,511
119 New Brunswick 842 817
120 New Brunswick 1,390 1,365 500
121 New Brunswick 1,190 1,165 500
122 New Brunswick 462 437
123 New Brunswick 956 331
124 New Brunswick 2,052 2,027
125 New Brunswick 1,155 1,015 4,500
126 New Brunswick 1,992 1,877 2,000
127 North Brunswick 1,314-p 7
128 North Brunswick 2,105-p 5
129 East Brunswick 2,199 2,672
130 South River 1,536 1,516
131 South River 1,194 1,174
132 South River 2,153 2,133
133 East Brunswick 338 838
134 Milltown 2,436 2,412
135 East Brunswick 902 353
136 East Brunswick 2,609 3,057
137 East Brunswick 1,559 1,627
138 East Brunswick 1,472 1,722
139 East Brunswick 1,166 1,166
140 East Brunswick 1,525 1,620
141 Spotswood 1,362 1,859
142 Spotswood 1,047 1,045
143 Helmetta 342 439
144 Monroe 3,881 4,252
145 Jamesburg 1,538 1,698
146 Monroe 3,178 3,553
147 Monroe 52 52
148 Monroe 262 262
149 Monroe 313 313
150 Monroe 149 149
151 Monroe 59 59
152 Washington 66 66
153 Washington 53 53
154 Washington 57 57
155 Washington 10 10
156 Washington 66 66
157 Washington 124 124
158 Washington 177 351
159 Washington 554 554
160 Washington 25 25
161 Washington 65 65
162 Washington 202 202
163 Washington 220 677
164 Hamilton 1,226 1,226
165 Hamilton 1,901 1,901
ZONEPOP
Table 4: Derivation of Dwelling Units by Zone - 1980/1986, 1988
1980/1986* 1988 Estimated
Dwelling Dwelling University
Zone Municipality Units Units Students
166 Hamilton 104 104
167 Hamilton 324 336
168 Hamilton 1,091 1,211
169 Hamilton 633 642
170 Hamilton 1,545 1,345
171 Hamilton 2,091 2,091
172 Hamilton 2,702 3,102
173 Hamilton 1,957 1,973
174 Hamilton 4,413 4,413
175 Hamilton 3,264 3,264
176 Hamilton 2,694 2,695
177 Hamilton 2,095 2,095
178 Hamilton 1,717 1,717
179 Lawrence 2,541 2,500
180 Trenton 33,952
181 Ewing 11,341 2,500
182 Ewing 1,200
183 Hopewell Township 420
184 Hopewell Township 460
185 Pennington 872
186 Hopewell Township 310
187 Hopewell Township 360
188 Hopewell Township 410
189 Hopewell Township 310
190 Hopewell Township 360
191 Hopewell Township 260
192 Hopewell Township 360
193 Hopewell Boro 803
194 Hopewell Township 310
195 Hopewell Township 310
196 South Bound Brook 1,502
STUDY AREA (TOTAL) 223,431 18,200
*Note: Zones 1 - 97 have a 1980 base; zones 98 - 178 have a 1986
base.
Sources: NJDOT - Route 1 Corridor Study, North New Jersey Model,
Route 130 Model; MSM Regional Council - Current
Development Survey, 1987, 1988.
ZONEPOP
Table 5: Dwelling Units by Zone - 2010 Trend
1988 Estimated 2010 Dwelling
Dwelling University Dwelling Unit
Zone Municipality Units Students Units Growth
1 Franklin 223 816 593
2 Franklin 138 731 593
3 Montgomery/Rocky Hill 653 700 47
4 Montgomery 795 1,895 1,100
5 Montgomery 195 418 223
6 Montgomery 958 1,180 222
7 Montgomery 185 407 222
8 Montgomery 121 343 222
9 Montgomery 383 605 222
10 Princeton Township 203 668 465
11 Princeton Township 365 831 466
12 Princeton Township 1,725 2,191 466
13 Princeton Township 807 1,273 466
14 Princeton Township/Boro 1,073 4,245 1,328 255
15 Princeton Boro 2,132 650 2,386 254
16 Princeton Township 727 1,193 466
17 Princeton Township 375 841 466
18 Princeton Township 449 915 466
19 Princeton Boro 576 915 466
20 Princeton Township 372 190 838 466
21 Franklin 28 621 593
22 Franklin 145 738 593
23 South Brunswick 1,288 2,192 904
24 South Brunswick 2,097 2,413 316
25 South Brunswick 425 1,499 1,074
26 South Brunswick 158 269 111
27 South Brunswick 423 1,619 1,196
28 Plainsboro 551 1,513 962
29 West Windsor 250 250 0
30 West Windsor 20 20 0
31 West Windsor 8 1,775 1,767
32 West Windsor 600 600 0
33 Plainsboro 39 1,001 962
34 Plainsboro 10 10 0
35 Plainsboro 6 6 0
36 Plainsboro 224 1,186 962
37 South Brunswick 42 154 112
38 South Brunswick 707 855 148
39 South Brunswick 82 1,131 1,049
40 South Brunswick 611 1,019 408
41 South Brunswick 1,669 2,469 800
42 South Brunswick 178 459 281
43 Plainsboro 27 989 962
44 Plainsboro 992 1,954 962
45 Plainsboro 4,897 5,859 962
46 Plainsboro 87 1,048 961
47 West Windsor 466 865 399
48 West Windsor 781 1,127 346
49 West Windsor 484 1,179 695
50 West Windsor 80 537 457
51 West Windsor 392 859 467
52 East Windsor 2,642 3,132 490
53 East Windsor 108 598 490
54 West Windsor 420 861 441
55 Cranbury 6 145 139
2010DGRO
Table 5: Dwelling Units by Zone - 2010 Trend
1988 Estimated 2010 Dwelling
Dwelling University Dwelling Unit
Zone Municipality Units Students Units Growth
56 Cranbury 273 412 139
57 Cranbury 14 153 139
58 Cranbury 353 497 139
59 Cranbury 96 235 139
60 South Brunswick 47 159 112
61 South Brunswick 288 957 569
62 South Brunswick 137 249 112
63 Cranbury 34 173 139
64 Cranbury 102 241 139
65 -- --
66 Cranbury 15 1.54 139
67 Cranbury 15 155 140
68 -- --
69 Lawrence 22 999 966
70 East Windsor 2,648 3,133 490
71 East Windsor 56 546 490
72 East Windsor 1,033 1,323 490
73 East Windsor 1,169 1,658 489
74 Hightstown 691 692 1
75 Hightstown 1,127 1,127 0
76 East Windsor 391 971 490
77 East Windsor 176 665 499
78 East Windsor 327 816 489
79 East Windsor 126 615 489
80 Lawrence 978 1,192 204
81 Hamilton 3,021 3,587 566
82 Lawrence 692 970 279
83 Lawrence 627 1,243 616
84 West Windsor 5 5 0
85 West Windsor 790 1,109 319
86 West Windsor 130 130 0
87 West Windsor 10 10 0
88 Franklin 627 2,109 1,482
89 Franklin 455 1,048 593
90 North Brunswick 2,669 3,597 919
91 South Brunswick 189 301 112
92 North Brunswick 1,129 4,129 3,000
93 North Brunswick 2,399 2,779 330
94 North Brunswick 1,211 1,211 0
95 North Brunswick 3,311 3,505 194
96 Lawrence 899 899 0
97 Lawrence 2,559 2,671 113
98 Hillsborough 1,062 1,699 637
99 Hillsborough 2,654 3,995 1,231
100 Hillsborough 1,167 2,198 1,031
101 Hillsborough 1,526 2,164 638
102 Hillsborough 1,017 1,655 638
103 Hillsborough 1,739 2,648 909
104 Millstone 180 197 7
105 Manville 1,679 1,768 89
106 Manville 1,055 1,143 99
107 Manville 1,134 1,222 88
108 Franklin 613 1,206 593
109 Franklin 342 935 593
110 Franklin 310 904 594
2010DGRO
Table 5: Dwelling Units by Zone - 2010 Trend
1988 Estimated 2010 Dwelling
Dwelling University Dwelling Unit
Zone Municipality Units Students Units Growth
166 Hamilton 104 670 566
167 Hamilton 336 902 566
168 Hamilton 1,211 1,777 566
169 Hamilton 642 1,208 566
170 Hamilton 1,545 2,111 566
171 Hamilton 2,091 2,657 566
172 Hamilton 3,102 3,668 566
173 Hamilton 1,973 2,539 566
174 Hamilton 4,413 4,979 566
175 Hamilton 3,264 3,830 566
176 Hamilton 2,695 3,262 367
177 Hamilton 2,095 2,662 567
178 Hamilton 1,717 2,293 566
179 Lawrence 2,841 3,000 3,283 442
180 Trenton 33,952 39,619 5,667
181 Ewing 11,341 3,000 13,073 1,732
182 Ewing 1,200 1,439 239
183 Hopewell Township 420 490 70
184 Hopewell Township 460 541 81
185 Pennington 972 1,113 241
186 Hopewell Township 310 353 43
187 Hopewell Township 360 412 52
188 Hopewell Township 410 524 114
189 Hopewell Township 310 524 214
190 Hopewell Township 360 398 38
191 Hopewell Township 260 749 489
192 Hopewell Township 360 1,373 1,015
193 Hopewell Boro 803 1,093 230
194 Hopewell Township 310 349 39
195 Hopewell Township 310 433 123
196 South Bound Brook 1,502 1,669 167
STUDY AREA (TOTAL) 223,431 20,500 315,447 92,016
Sources: NJDOT - Route 1 Corridor Study, Routc 130 Model; MSM
Regional Council - Current Development Survey, 1989.
2010DGRO
Table 6: Dwelling Units by Zone - 2010 Sccnario No. 1
1988 Estimated 2010 Dwelling
Dwelling University Dwelling Unit
Zone Municipality Units Students Units Growth
1 Franklin 223 223 0
2 Franklin 138 139 0
3 Montgomery/Rocky Hill653 653 0
4* Montgomery 795 795 0
5 Montgomery 195 195 0
6 Montgomery 959 953 0
7 Montgomery 195 195 0
8* Montgomery 121 1,721 1,600
9 Montgomery 383 383 0
10 Princeton Township 203 203 0
11 Princeton Township 365 365 0
12 Princeton Township1, 725 1,725 0
13 Princeton Township 807 807 0
14 Princeton Township/Boro 1,073 4,245 1,073 0
15 Princeton Boro 2,132 650 2,132 0
16 Princeton Township 727 727 0
17 Princeton Township 375 375 0
18 Princeton Township 449 449 0
19 Princeton Boro 576 915 576 0
20 Princeton Township 372 190 372 0
21 Franklin 28 23 0
22 Franklin 145 145 0
23 South Brunswick 1,288 1,288 0
24 South Brunswick 2,097 2,097 0
25 South Brunswick 425 425 0
26 South Brunswick 158 158 0
27 South Brunswick 423 423 0
28*Plainsboro 551 3,351 2,900
29 West Windsor 250 250 0
30 West Windsor 20 20 0
31 West Windsor 8 8 0
32*West Windsor 600 6,600 6,000
33 Plainsboro 39 39 0
34 Plainsboro 10 10 0
35 Plainsboro 6 6 0
36 Plainsboro 224 224 0
37 South Brunswick 42 42 0
38 South Brunswick 707 707 0
39 South Brunswick 82 82 0
40*South Brunswick 611 6,611 6,000
41 South Brunswick 1,669 1,669 0
42 South Brunswick 178 178 0
43 Plainsboro 27 27 0
44 Plainsboro 992 992 0
45 Plainsboro 4,897 4,897 0
46 Plainsboro 87 87 0
47 West Windsor 466 466 0
48 West Windsor 781 781 0
49 West Windsor 484 494 0
50 West Windsor 80 80 0
51 West Windsor 392 392 0
52 East Windsor 2,642 2,642 0
53 East Windsor 108 108 0
54 West Windsor 420 420 0
55 Cranbury 6 6 0
SCEN1DUS
Table 6: Dwelling Units by Zone - 2010 Scenario No. 1
1988 Estimated 2010 Dwelling
Dwelling University Dwelling Unit
Zone Municipaliity Units Students Units Growth
56 Cranbury 273 273 0
57 Cranbury 14 14 0
58 Cranbury 358 358 0
59 Cranbury 96 96 0
60*South Brunswick 47 1,647 1,600
61 South Brunswick 288 288 0
62 South Brunswick 137 2,937 2,800
63 Cranbury 34 34 0
64*Cranbury 102 1,702 1,600
65 -- -- --
66 Cranbury 15 15 0
67 Cranbury 15 15 0
68 -- -- --
69 Lawrence 22 22 0
70 East Windsor 2,648 2,648 0
71*East Windsor 56 56 0
72 East Windsor 1,033 1,033 0
73 East Windsor 1,169 1,169 0
74 Hightstown 691 691 0
75 Hightstown 1,127 1,127 0
76 East Windsor 381 381 0
77 East Windsor 176 176 0
78 East Windsor 327 327 0
79*East Windsor 126 6,126 6,000
80 Lawrence 978 978 0
81 Hamilton 3,021 3,021 0
82 Lawrence 692 692 0
83*Lawrence 627 3,427 2,800
84 West Windsor 5 5 0
85 West Windsor 790 790 0
86 West Windsor 130 130 0
87 West Windsor 10 10 0
88*Franklin 627 627 0
89 Franklin 455 455 0
90 North Brunswick 2,668 2,668 0
91 South Brunswick 189 189 0
92*North Brunswick 1,129 1,129 0
93 North Brunswick 2,399 2,399 0
94 North Brunswick 1,211 1,211 0
95 North Brunswick 3,311 3,311 0
96 Lawrence 898 898 0
97 Lawrence 2,558 2,558 0
98 Hillsborough 1,062 1,062 0
99*Hillsborough 2,654 5,454 2,800
100Hillsborough 1,167 1,167 0
101Hillsborough 1,526 1,526 0
102Hillsborough 1,017 1,017 0
103Hillsborough 1,739 1,739 0
104Millstone 180 180 0
105Manville 1,679 1,679 0
106Manville 1,055 1,055 0
107Manville 1,134 1,134 0
108Franklin 613 613 0
109Franklin 342 342 0
110*Franklin 310 3,110 2,800
SCEN1DUS
Table 6: Dwelling Units by Zone - 2010 Scenario No. 1
1988 Estimated 2010 Dwelling
Dwelling University Dwelling Unit
Zone Municipality Units Students Units Growth
111 Franklin 601 601 0
112 Franklin 2,038 2,038 0
113 Franklin 1,957 1,957 0
114 Franklin 2,373 2,373 0
115 Franklin 2,309 2,309 0
116 Franklin 1,343 1,343 0
117 New Brunswick 1,637 3,578 1,941
113 Now Brunswick 1,511 3,452 1,941
119 New Brunswick 817 2,758 1,941
120 New Brunswick 1,365 500 3,306 1,941
121 New Brunswick 1,165 500 3,106 1,941
122 New Brunswick 437 2,378 1,941
123 New Brunswick 331 2,772 1,941
124 New Brunswick 2,027 3,968 1,941
125 New Brunswick 1,015 5,500 2,955 1,940
126 New Brunswick 1,877 2,000 3,917 1,940
127 North Brunswick 7 7 0
128 North Brunswick 5 5 0
129 East Brunswick 2,672 2,672 0
130 South River 1,516 1,516 0
131 South River 1,174 1,174 0
132 South River 2,133 2,133 0
133 East Brunswick 838 838 0
134 Milltown 2,412 2,412 0
135 East Brunswick 853 853 0
136 East Brunswick 3,057 3,057 0
137 East Brunswick 1,627 1,627 0
138 East Brunswick 1,722 1,722 0
139 East Brunswick 1,166 1,166 0
140 East Brunswick 1,620 1,620 0
141 Spotswood 1,859 1,859 0
142 Spotswood 1,045 1,045 0
143 Helmetta 439 439 0
144 Monroe 4,252 4,252 0
145 Jamesburg 1,688 1,688 0
146 Monroe 3,553 3,553 0
147 Monroe 52 52 0
148 Monroe 262 262 0
149 Monroe 313 313 0
150 Monroe 149 149 0
151 Monroe 59 59 0
152 Washington 66 66 0
153 Washington 53 53 0
154 Washington 57 57 0
155 Washington 10 10 0
156 Washington 66 66 0
157* Washington 124 1,724 1,600
158 Washington 351 351 0
159 Washington 554 554 0
160* Washington 25 2,825 2,800
161 Washington 65 65 0
162 Washington 202 202 0
163 Washington 677 677 0
164 Hamilton 1,226 1,226 0
165 Hamilton 1,901 1,901 0
SCEN1DUS
Table 6: Dwelling Units by Zone - 201O Scenario No. 1
1988 Estimated 2010 Dwelling
Dwelling University Dwelling Unit
Zone Municipality Units Students Units Growth
166 Hamilton 104 104 0
167 Hamilton 336 336 0
168 Hamilton 1,211 1,211 0
169 Hamilton 642 642 0
170 Hamilton 1,545 1,545 0
171 Hamilton 2,091 2,091 0
172 Hamilton 3,102 3,102 0
173 Hamilton 1,973 1,973 0
174 Hamilton 4,413 4,413 0
175 Hamilton 3,264 3,264 0
176 Hamilton 2,695 2,695 0
177 Hamilton 2,095 2,095 0
178 Hamilton 1,717 1,717 0
179 Lawrence 2,341 3,000 2,341 0
180 Trenton 33,952 53,359 19,407
181 Ewing 11,341 3,000 11,341 0
182 Ewing 1,200 1,200 0
183 Hopewell Township 420 420 0
184* Hopewell Township 460 3,260 2,800
185 Pennington 872 872 0
186 Hopewell Township 310 310 0
187 Hopewell Township 360 360 0
188 Hopewell Township 410 410 0
189* Hopewell Township 310 310 0
190 Hopewell Township 360 360 0
191 Hopewell Township 260 260 0
192 Hopewell Township 360 360 0
193 Hopewell Boro 803 803 0
194* Hopewell Township 310 310 0
195 Hopewell Township 310 310 0
196 South Bound Brook 1,502 1,502 0
200 Montgomery (W/C) 0 1,600 1,600
201 Franklin (W/C) 0 1,600 1,600
202 Hopewell (W/C) 0 1,600 1,600
203 Hopewell (W/C) 0 1,600 1,600
STUDY AREA (TOTAL) 223,431 20,500 313,446 92,015
*Note: Constructs are located in these zones. Zones 200 - 203
are new zones created from sections of zones 4, 88, 189
and 194 for walking constructs.
SCEN1DUS
Table 7: Dwelling Units by zone - 201O Scenario No.2
1988 Estimated 2010 Dwelling
Dwelling University Dwelling Unit
Zone Municipality Units Students Units Growth
1 Franklin 223 223 0
2 Franklin 138 138 0
3 Montgomery/Rocky Hill 653 653 0
4* Montgomery 795 795 0
5 Montgomery 195 195 0
6 Montgomery 959 959 0
7 Montgomery 185 185 0
8* Montgomery 121 2,603 2,482
9 Montgomery 383 383 0
10 Princeton Township 203 203 0
11 Princeton Township 365 365 0
12 Princeton Township 1,725 1,725 0
13 Princeton Township 807 807 0
14 Princeton Township/Boro 1,073 4,245 1,073 0
15 Princeton Boro 2,132 650 2,132 0
16 Princeton Township 727 727 0
17 Princeton Township 375 375 0
18 Princeton Township 449 449 0
19 Princeton Boro 576 913 576 0
20 Princeton Township 372 190 372 0
21 Franklin 28 28 0
22 Franklin 145 145 0
23 South Brunswick 1,288 1,288 0
24 South Brunswick 2,097 2,097 0
25 South Brunswick 425 425 0
26 South Brunswick 158 158 0
27 South Brunswick 423 423 0
28*Plainsboro 551 4,893 4,342
29 West Windsor 250 250 0
30 West Windsor 20 20 0
31 West Windsor 8 8 0
32*West Windsor 600 9,928 9,328
33 Plainsboro 39 39 0
34 Plainsboro 10 10 0
35 Plainsboro 6 6 0
36 Plainsboro 224 224 0
37 South Brunswick 42 42 0
38 South Brunswick 707 707 0
39 South Brunswick 82 82 0
40*South Brunswick 611 9,940 9,329
41 South Brunswick 1,669 1,669 0
42 South Brunswick 178 178 0
43 Plainsboro 27 27 0
44 Plainsboro 992 992 0
45 Plainsboro 4,897 4,897 0
46 Plainsboro 97 97 0
47 West Windsor 466 466 0
48 West Windsor 781 781 0
49 West Windsor 484 484 0
50 West Windsor 80 80 0
51 West Windsor 392 392 0
52 East Windsor 2,642 2,642 0
53 East Windsor 108 108 0
54 West Windsor 420 420 0
55 Cranbury 6 6 0
SCEN2DUS
Table 7: Dwelling Units by Zone - 2010 Scenario No.2
1988 Estimated 2010 Dwelling
Dwelling University Dwelling Unit
Zone Municipality Units Students Units Growth
56 Cranbury 273 273 0
57 Cranbury 14 14 0
58 Cranbury 358 358 0
59 Cranbury 96 96 0
60*South Brunswick 47 2,528 2,481
61 South Brunswick 293 293 0
62*South Brunswick 137 4,479 4,342
63 Cranbury 34 34 0
64*Cranbury 102 2,583 2,481
65 -- -- --
66 Cranbury 15 15 0
67 Cranbury 15 15 0
68 -- -- --
69 Lawrence 22 22 0
70 East Windsor 2,648 2,648 0
71*East Windsor 56 56 0
72 East Windsor 1,033 1,033 0
73 East Windsor 1,169 1,169 0
74 Hightstown 691 691 0
75 Hightstown 1,127 1,127 0
76 East Windsor 381 381 0
77 East Windsor 176 176 0
78 Fast Windsor 327 327 0
79*East Windsor 126 9,454 9,328
80 Lawrence 978 978 0
81 Hamilton 3,021 3,021 0
82 Lawrence 692 692 0
83*Lawrence 627 4,969 4,342
84 West Windsor 5 5 0
85 West Windsor 790 790 0
86 West Windsor 130 130 0
87 West Windsor 10 10 0
88*Franklin 627 627 0
89 Franklin 455 455 0
90 North Brunswick 2,669 2,669 0
91 South Brunswick 189 189 0
92*North Brunswick 1,129 5,471 4,342
93 North Brunswick 2,399 2,399 0
94 North Brunswick 1,211 1,211 0
95 North Brunswick 3,311 3,311 0
96 Lawrence 898 898 0
97 Lawrence 2,558 2,558 0
98 Hillsborough 1,062 1,062 0
99*Hillsborough 2,654 6,996 4,342
100Hillsborough 1,167 1,167 0
101Hillsborough 1,526 1,526 0
102Hillsborough 1,017 1,017 0
103Hillsborough 1,739 1,739 0
104Millstone 150 150 0
105Manville 1,679 1,679 0
106Manville 1,055 1,055 0
107Manville 1,134 1,134 0
108Franklin 613 613 0
109Franklin 342 342 0
110*Franklin 310 4,652 4,342
SCEN2DUS
Table 7: Dwelling Units by Zone - 201O Scenario No.2
1988 Estimated 2010 Dwelling
Dwelling University Dwelling Unit
Zone Municipality Units Students Units Growth
111 Franklin 601 601 0
112 Franklin 2,039 2,039 0
113 Franklin 1,957 1,957 0
114 Franklin 2,373 2,373 0
115 Franklin 2,309 2,309 0
116 Franklin 1,343 1,343 0
117 New Brunswick 1,637 2,015 378
118 New Brunswick 1,511 1,889 378
119 New Brunswick 817 1,195 378
120 New Brunswick 1,365 500 1,743 378
121 New Brunswick 1,165 500 1,543 378
122 New Brunswick 437 815 378
123 New Brunswick 831 1,209 378
124 New Brunswick 2,027 2,405 378
125 New Brunswick 1,015 5,500 1,393 378
126 New Brunswick 1,877 2,000 2,254 377
127 North Brunswick 7 7 0
128 North Brunswick 5 5 0
129 East Brunswick 2,672 2,672 0
130 South River 1,516 1,516 0
131 South River 1,174 1,174 0
132 South River 2,133 2,133 0
133 East Brunswick 838 838 0
134 Milltown 2,412 2,412 0
135 East Brunswick 953 953 0
136 East Brunswick 3,057 3,057 0
137 East Brunswick 1,627 1,627 0
138 East Brunswick 1,722 1,722 0
139 East Brunswick 1,166 1,166 0
140 East Brunswick 1,620 1,620 0
141 Spotswood 1,859 1,859 0
142 Spotswood 1,045 1,045 0
143 Helmetta 439 439 0
144 Monroe 4,252 4,252 0
145 Jamesburg 1,688 1,689 0
146 Monroe 3,553 3,553 0
147 Monroe 52 52 0
148 Monroe 262 262 0
149 Monroe 313 313 0
150 Monroe 149 149 0
151 Monroe 59 59 0
152 Washington 66 66 0
153 Washington 53 53 0
154 Washington 57 57 0
155 Washington 10 10 0
156 Washington 66 66 0
157* Washington 124 2,605 2,481
158 Washington 351 351 0
159 Washington 554 554 0
160* Washington 25 4,367 4,342
161 Washington 65 65 0
162 Washington 202 202 0
163 Washington 677 677 0
164 Hamilton 1,226 1,226 0
165 Hamilton 1,901 1,901 0
SCEN2DUS
Table 7: Dwelling Units by Zone - 2010 Scenario No.2
1988 Estimated 2010 Dwelling
Dwelling University Dwelling Unit
Zone Municipality Units Students Units Growth
166 Hamilton 104 104 0
167 Hamilton 336 336 0
168 Hamilton 1,211 1,211 0
169 Hamiltod 642 642 0
170 Hamilton 1,545 1,545 0
171 Hamilton 2,091 2,091 0
172 Hamilton 3,102 3,102 0
173 Hamilton 1,973 1,973 0
174 Hamilton 4,413 4,413 0
175 Hamilton 3,264 3,264 0
176 Hamilton 2,695 2,695 0
177 Hamilton 2,095 2,095 0
178 Hamilton 1,717 1,717 0
179 Lawrence 2,841 3,000 2,841 0
180 Trenton 33,952 39,619 5,667
181 Ewing 11,341 3,000 11,341 0
182 Ewing 1,200 1,200 0
183 Hopewell Township 420 420 0
184* Hopewell Township 460 4,801 4,341
185 Pennington 872 872 0
186 Hopewell Township 310 310 0
187 Hopewell Township 360 360 0
188 Hopewell Township 410 410 0
189 Hopewell Township 310 310 0
190 Hopewell Township 360 360 0
191 Hopewell Township 260 260 0
192 Hopewell Township 360 360 0
193 Hopewell Boro 803 803 0
194* Hopewell Township 310 310 0
195 Hopewell Township 310 310 0
196 South Bound Brook 1,502 1,502 0
200 Montgomery (W/C) 0 2,481 2,481
201 Franklin (W/C) 0 2,481 2,481
202 Hopewell (W/C) 0 2,481 2,481
203 Hopewell (W/C) 0 2,481 2,481
STUDY AREA (TOTAL) 223,431 20,500 315,446 92,015
*Note: Constructs are located in these zones. Zones 200 - 203
are new zones created from sections of zones 4, 88, 189
and 194 for walking constructs.
SCEN2DUS
Table 8: Employment Growth Assumptions - 1988, 2010 Trend, 2010
Scenarios
Click HERE for graphic.
MEMP2010
Table 9: Employment by Zone - 1980/1986, 1988
1980/1986* 1980/1986* 1988 1988
Non-Retail Retail Non-Retail Retail
Zone Municipality Employment Employment Employment Employment
1 Franklin 13 0 13 0
2 Franklin 0 0 0 0
3 Montgomery/Rocky Hill 1,680 507 1,943 550
4 Montgomery 592 0 304 5
5 Montgomery 184 0 200 30
6 Montgomery 2,574 0 4,644 0
7 Montgomery 44 0 44 0
8 Montgomery 237 0 250 10
9 Montgomery 0 0 0 0
10 Princeton Township 73 0 73 0
11 Princeton Township 1,219 0 1,719 25
12 Princeton Township 630 495 1,230 650
13 Princeton Township 84 0 84 0
14 Princeton Township/Boro 6,768 0 9,224 100
15 Princeton Boro 5,030 6,958 772
16 Princeton Township 343 0 343 50
17 Princeton Township 409 0 409 0
18 Princeton Township 197 0 200 0
19 Princeton Boro 159 200 25
20 Princeton Township 175 0 175 25
21 Franklin 281 0 281 40
22 Franklin 53 200 53 160
23 South Brunswick 118 50 150 205
24 South Brunswick 38 125 38 125
25 South Brunswick 11 0 0 130
26 South Brunswick 227 0 227 0
27 South Brunswick 27 0 30 30
28 Plainsboro 60 0 60 685
29 West Windsor 1,468 0 1,500 40
30 West Windsor 749 0 949 0
31 West Windsor 0 0 0 0
32 West Windsor 772 138 1,172 175
33 Plainsboro 980 0 980 0
34 Plainsboro 1,500 0 750 0
35 Plainsboro 3,460 0 3,569 0
36 Plainsboro 210 0 210 0
37 South Brunswick 874 0 2,606 0
38 South Brunswick 565 0 0 184
39 South Brunswick 481 0 0 0
40 South Brunswick 353 22 631 22
41 South Brunswick 1,548 0 1,895 84
42 South Brunswick 1,544 0 3,276 0
43 Plainsboro 0 0 0 0
44 Plainsboro 51 0 81 350
45 Plainsboro 24 0 147 117
46 Plainsboro 0 0 50 0
47 West Windsor 506 0 506 0
48 West Windsor 183 0 183 0
49 West Windsor 285 0 285 0
50 West Windsor 529 0 579 0
51 West Windsor 34 0 34 0
52 East Windsor 0 82 0 82
53 East Windsor 3,398 0 3,609 0
54 West Windsor 12 0 112 0
55 Cranbury 0 0 0 0
ZONEEMP
Table 9: Employment by Zone - 1980/1986, 1988
1980/1986* 1980/1986* 1988 1988
Non-Retail Retail Non-Retail Retail
Zone Municipality Employment Employment Employment Employment
56 Cranbury 58 0 148 0
57 Cranbury 0 0 0 0
58 Cranbury 518 0 0 25
59 Cranbury 0 0 0 0
60 South Brunswick 0 0 0 0
61 South Brunswick 1,459 0 782 0
62 South Brunswick 1,742 0 2,035 0
63 Cranbury 0 0 783 0
64 Cranbury 383 0 3,101 25
65 -- -- -- -- --
66 Cranbury 431 0 2,621 0
67 Cranbury 1,386 0 0 0
68 -- -- -- --
69 Lawrence 107 329 107 1,150
70 East Windsor 80 214 587 214
71 East Windsor 1,146 0 1,222 0
72 East Windsor 23 0 100 0
73 East Windsor 411 656 722 552
74 Hightstown 1,185 0 1,742 0
75 Hightstown 1,377 0 313 800
76 East Windsor 241 0 684 0
77 East Windsor 0 0 0 0
78 East Windsor 31 0 0 0
79 East Windsor 630 0 691 0
80 Lawrence 1,330 50 1,730 350
81 Hamilton 1,957 0 2,213 0
82 Lawrence 615 0 2,444 0
83 Lawrence 608 2,361 1,625 2,561
84 West Windsor 6 0 56 0
85 West Windsor 396 0 796 800
86 West Windsor 2,704 0 4,940 25
87 West Windsor 37 0 0 10
88 Franklin 45 62 145 62
89 Franklin 73 50 73 50
90 North Brunswick 3,610 0 1,630 487
91 South Brunswick 473 0 236 0
92 North Brunswick 275 0 838 0
93 North Brunswick 1,290 0 585 72
94 North Brunswick 735 0 997 112
95 North Brunswick 993 625 9,106 1,498
96 Lawrence 4,250 0 4,205 0
97 Lawerce 87 0 110 25
98 Hillsborough 428 100 428 100
99 Hillsborough 198 67 381 188
100 Hillsborough 235 62 790 62
101 Hillsborough 84 33 267 155
102 Hillsborough 286 303 469 425
103 Hillsborough 974 141 974 141
104 Millstone 30 19 35 19
105 Manville 723 200 519 209
106 Manville 192 25 192 33
107 Manville 285 33 285 41
108 Franklin 8,684 414 9,834 414
109 Franklin 2905-p 270-p 2,440 213
110 Franklin 1,612 162 2,428 162
ZONEEMP
Table 9: Employment by Zone - 1980/1986, 1993
1980/1986* 1980/1986* 1988 1988
Non-Retail Retail Non-Retail Retail
Zone Municipality Employment Employment Employment Employment
111 Franklin 612 89 612 89
112 Franklin 1,541 171 1,541 171
113 Franklin 499 92 499 92
114 Franklin 700 372 700 372
115 Franklin 1,643 143 1,643 143
116 Franklin 1,593 119 1,593 119
117 New Brunswick 3,523 189 3,332 189
118 New Brunswick 558 62 367 62
119 New Brunswick 1,369 63 1,177 63
120 New Brunswick 108 47 108 47
121 New Brunswick 1,675 85 1,483 107
122 New Brunswick 6,837 319 6,645 319
123 New Brunswick 3,789 273 3,597 273
124 New Brunswick 2,428 95 2,236 95
125 New Brunswick 14,874 631 10,111 631
126 New Brunswick 3,530 1,071 3,339 1,071
127 North Brunswick 7176-p 552-p 150 0
128 North Brunswick 585-p 72-p 300 0
129 Fast Brunswick 3,644 1,815 4,811 1,815
130 South River 1,288 180 1,122 217
131 South River 371 82 288 119
132 South River 487 50 404 87
133 East Brunswick 4,958 1,097 5,537 1,097
134 Milltown 2,083 304 2,415 242
135 East Brunswick 86 44 86 44
136 East Brunswick 1,829 488 1,829 488
137 East Brunswick 689 206 689 206
138 East Brunswick 319 313 898 313
139 East Brunswick 2,028 3,149 2,028 3,149
140 East Brunswick 1,437 892 1,437 892
141 Spotswood 756 118 784 183
142 Spotswood 907 206 936 271
143 Helmetta 52 11 154 11
144 Monroe 0 0 0 0
145 Jamesburg 1,304 0 1,649 433
146 Monroe 1,612 0 1,542 0
147 Monroe 0 0 0 0
148 Monroe 0 0 0 0
149 Monroe 469 0 400 0
150 Monroe 0 0 0 0
151 Monroe 0 0 0 0
152 Washington 0 0 50 75
153 Washington 0 0 50 0
154 Washington 0 0 50 0
155 Washington 0 0 0 0
156 Washington 0 0 50 50
157 Washington 0 0 50 50
158 Washington 0 1,882 50 25
159 Washington 0 0 500 25
160 Washington 0 0 0 0
161 Washington 0 0 225 25
162 Washington 0 0 350 100
163 Washington 0 0 225 150
164 Hamilton 0 0 0 50
165 Hamilton 0 196 270 196
ZONEEMP
Table 9: Employment by Zone - 1980/1986, 1988
1980/1986* 1980/1986* 1988 1988
Non-Retail Retail Non-Retail Retail
Zone Municipality Employment Employment Employment Employment
166 Hamilton 1,133 0 1,133 50
167 Hamilton 440 0 440 0
168 Hamilton 0 0 270 100
169 Hamilton 426 0 426 0
170 Hamilton 327 250 597 250
171 Hamilton 730 466 730 550
172 Hamilton 2,423 1,221 2,576 1,221
173 Hamilton 583 1,166 853 1,893
174 Hamilton 2,629 124 2,763 124
175 Hamilton 654 523 523 523
176 Hamilton 2,504 0 2,720 0
177 Hamilton 3,288 538 3,288 538
178 Hamilton 8,761 128 3,500 128
179 Lawrence 4,463 2,531
180 Trenton 51,442 3,405
181 Ewing 24,952 2,458
182 Ewing 1,200 50
183 Hopewell Township 355 0
184 Hopewell Township 370 0
185 Pennington 1,596 40
186 Hopewell Township 700 98
187 Hopewell Township 300 0
188 Hopewell Township 250 0
189 Hopewell Township 500 30
190 Hopewell Township 0 0
191 Hopewell Township 345 0
192 Hopewell Township 0 0
193 Hopewell Boro 499 40
194 Hopewell Township 0 0
195 Hopewell Township 0 0
196 South Bound Brook 426 69
STUDY AREA (TOTAL)
293,894 43,905
*Note: Zones 1-97 have a 1980 base; zones 98-178 have a 1986 base.
Sources: NJDOT - Route 1 Corridor Study, North New Jersey Model, Route
130 Model; MSM Regional Council - Current Development Survey,
1987, 1988; US Census Bureau - 1987 Census of Retail Trade.
ZONEEMP
Table 10: Non-Retail and Retail Employment by Zone - 2010 Trend.
Click HERE for graphic.
2010EGRO
Table 10: Non-Retail and Retail Employment by Zone - 2010 Trend.
Click HERE for graphic.
2010EGRO
Click HERE for graphic.
2010EGRO
Click HERE for graphic.
2010EGRO
Table 11: Non-Retail And Retail Employment by Zone -
2010 Scenario No. 1
Click HERE for graphic.
SCEN1EMP
Table 11: Non-Retail and Retail Employment by Zone - 2010 Scenario
No. 1
Click HERE for graphic.
SCENIEMP
Table 11: Non-Retail and Retail Employment by Zone - 2010
Scenario No. 1
Click HERE for graphic.
SCENIEMP
Table 11: Non-Retail and Retail Employment by Zone - 2010
Scenario No. 1
Click HERE for graphic.
SCENIEMP
Table 12: Non-Retail and Retail Employment by Zone - 2010
Scenario No.2
Click HERE for graphic.
SCEN2EMP
Table 12: Non-Retail and Retail Employment by Zone - 2010
Scenario No.2
Click HERE for graphic.
SCEN2EMP
Table 12: Non-Retail and Retail Employment by Zone - 2010
Scenario No.2
Click HERE for graphic.
SCEN2EMP
Table 12: Non-Retail and Retail Employment by Zone - 2010
Scenario No.2
Click HERE for graphic.
SCEN2EMP
Appendix E
MSM Employment and Housing Projections, Vehicle Trip Productions
and Attractions, Daily Trip Ends, and Jobs/Housing Ratios: 1988,
2010, Scenario 1, Scenario 2
03-Jan-91 Page 1.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
03-Jan-91 Page 2.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
03-Jan-91 Page 3.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
03-Jan-91 Page 4.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
03-Jan-91 Page 5.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
03-Jan-91 Page 6.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
03-Jan-91 Page 7.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
03-Jan-91 Page 8.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
03-Jan-91 Page 9.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
03-Jan-91 Page 10.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
03-Jan-91 Page 11.
Click HERE for graphic.
Source: Douglas& Douglas, Inc.
03-Jan-91 Page 12.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
03-Jan-91 Page 13.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
03-Jan-91 Page 14.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
03-Jan-91 Page 15.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
03-Jan-91 Page 16
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
03-Jan-91 Page 17.
Click HERE for graphic.
Source: Douglas& Douglas, Inc.
03-Jan-91 Page 18.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
03-Jan-91 Page 19.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
03-Jan-91 Page 20.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
03-Jan-91 Page 21.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
03-Jan-91 Page 22.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
03-Jan-91 Page 23.
Click HERE for graphic.
Source: Douglas& Douglas, Inc.
03-Jan-91 Page 24.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
03-Jan-91 Page 25.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
03-Jan-91
Page 26.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
03-Jan-91 Page 27.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
03-Jan-91 Page 28.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
03-Jan-91 Page 29.
Click HERE for graphic.
Source., Douglas & Douglas, Inc.
03-Jan-91 Page 30.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
03-Jan-91 Page 31.
Click HERE for graphic.
Source:Douglas & Douglas, Inc.
03-Jan-91 Page 32.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
03-Jan-91 Page 33.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
03-Jan-91 Page 34.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
03-Jan-91 Page 35.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
03-Jan-91 Page 36.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
Appendix F
Vehicle Trips, Speeds, and Vehicle Miles of Travel for Study
Area Municipalities: 1988,2010 Trend, Scenario 1, Scenario 2
THE MSM REGION MCD Codes
Click HERE for graphic.
Vehicle Miles Summary
Calb Tmd Scen1 Scen2
Veh Veh Veh Veh
Jurisdiction Mile Mile Mile Mile
1 Washington 91,926 109,419 106,211 102,221
2 Trenton 101,186 112,092 148,313 127,922
3 Ewing 51,551 55,055 57,756 50,929
4 Lawrence 74,658 96,545 102,519 99,980
5 Hopewell 25,494 32,276 33,776 37,927
6 Princeton 39,966 56,184 42,992 43,945
7 W.Windsor 45,731 72,124 59,708 65,460
8 Hamilton 34,764 45,624 47,844 51,608
9 E.Windsor 25,986 36,666 35,761 41,203
10 Crabury 40,201 53,285 45,217 48,301
11 Plainsboro 19,634 37,605 21,786 24,772
12 S.Bunswick 71,936 134,703 104,437 118,289
13 N.Brunswick 35,178 54,081 50,225 48,668
14 New Brunswick 34,454 37,147 52,020 40,194
15 S. Brunswick 77,835 88,530 77,480 76,117
16 Monroe 31,246 50,256 32,738 33,026
17 Montgonicry 27,441 39,015 30,887 34,152
18 Hillsborough 32,948 46,970 37,555 40,980
19 Franklin 56,065 72,939 63,207 67,221
20 Mercer Ext 22,152 23,761 26,809 23,856
21 Somerset Ext 8,340 9,990 8,655 8,042
22 Middlesex Ext 40,975 42,593 41,438 40,603
County Summary (Excluding Ext)
Calb Tmd Scen1 Scen2
Veh Veh Veh Veh
Jurisdiction Mile Mile Mile Mile
Mercer 491,263 615,986 634,881 621,094
Somerset 310,482 455,607 383,903 389,367
Middlesex 116,454 158,924 131,649 142,353
Total 918,198 1,230,517 1,150,433 1,152,314
County Summary (Including Ext)
Calb Tmd Scen1 Scen2
Veh Veh Veh Veh
Jurisdiction Mile Mile Mile Mile
Mercer 513,414 639,747 661,689 644,950
Somerset 318,822 465,597 392,558 397,408
Middlesex 157,429 201,817 173,087 182,956
Total 989,665 1,307,161 1,227,335 1,15,315
Source: Douglas & Douglas, Inc.
Vehicle Miles Summary
Click HERE for graphic.
Source: Douglas& Douglas, Inc.
Vehicle Minutes Summary
Calb Tmd Scen1 Scen2
Veh Veh Veh Veh
Jurisdiction Min Min Min Min
1 Washington 187,404 214,849 193,563 192,672
2 Trenton 475,827 530,091 1,262,744 778,741
3 Ewing 253,058 301,650 265,206 203,020
4 Lawrence 125,680 185,458 185,864 168,581
5 Hopewell 44,966 59,492 64,337 76,233
6 Princeton 165,878 236,602 172,901 190,643
7 W.Windsor 85,344 241,125 118,129 142,297
8 Hamilton 43,983 61,178 63,073 69,337
9 E.Windsor 53,100 76,750 78,907 98,322
10 Crabury 54,467 76,032 60,521 64,811
11 Plainsboro 40,351 118,028 47,364 63,374
12 S.Brunswick 129,393 381,204 216,929 321,286
13 N.Brunswick 73,928 136,191 108,273 117,800
14 New Brunswick 133,415 167,799 865,640 168,922
15 E. Brunswick 216,182 268,745 215,810 212,109
16 Monroe 76,501 133,951 79,485 79,150
17 Montgomery 50,962 88,626 59,201 67,154
18 Hillsborough 125,204 321,775 146,884 278,466
19 Franklin 179,020 250,563 205,342 238,193
20 Mercer Ext 25,610 26,588 29,442 26,687
21 Somerset Ext 14,123 16,872 15,252 12,782
22 Middlesex Ext 85,541 95,862 96,273 94,513
County Summary (Excluding Ext.)
Calb Tmd Scen1 Scen2
Veh Veh Veh Veh
Jurisdiction Min Min Min Min
Mercer 1,435,240 1,907,195 2,404,725 1,919,847
Somerset 724,238 1,281,951 1,594,022 1,027,454
Middlesex 355,186 660,965 411,426 583,813
Total 2,514,664 3,850,110 4,410,173 3,531,114
County Summary (Including EXQ
Calb Tmd Scen1 Scen2
Veh Veh Veh Veh
Jurisdiction Min Min Min Min
Mercer 1,460,850 1,933,783 2,434,167 1,946,533
Somerset 738,361 1,298,822 1,609,274 1,040,235
Middlesex 440,728 756,827 507,699 678,327
Total 2,639,939 3,989,433 4,551,140 3,665,095
Source: Douglas & Douglas, Inc.
Click HERE for graphic.
Source: Douglas & Douglas, Inc.
Speed Summary (MPH)
Click HERE for graphic.
Speed Summary (MPH)
Calb Trnd Scen1 Scen2
Ave Ave Ave Ave
Jurisdiction Speed Speed Speed Speed
1 Washington 29.4 30.6 32.9 31.8
2 Trenton 12.8 12.7 7.0 9.9
3 Ewing 12.2 11.0 13.1 15.1
4 Lawrence 35.6 31.2 33.1 35.6
5 Hopewell 34.0 32.6 31.5 29.9
6 Princeton 14.5 14.2 14.9 13.8
7 W.Windsor 32.2 17.9 30.3 27.6
8 Hamilton 47.4 44.7 45.5 44.7
9 E.Windsor 29.4 28.7 27.2 25.1
10 Crabury 44.3 42.0 44.8 44.7
11 Plainsboro 29.2 19.1 27.6 23.5
12 S.Brunswick 33.4 21.2 28.9 22.1
13 N.Brunswick 28.6 23.8 27.8 24.8
14 New Brunswick 15.5 13.3 3.6 14.3
15 E. Brunswick 21.6 19.8 21.5 21.5
16 Monroe 24.5 22.5 24.7 25.0
17 Montgomery 32.3 26.4 31.3 30.5
18 Hillsborough 15.8 8.8 15.3 8.8
19 Franklin 18.8 17.5 18.5 16.9
20 Mercer Ext 51.9 53.6 54.6 53.6
21 Somerset Ext 35.4 35.5 34.0 37.8
22 Middlesex Ext 28.7 26.8 25.8 25.8
County Summary (Excluding Ext)
Calb Tmd Scen1 Scen2
Ave Ave Ave Ave
Jurisdiction Speed Speed Speed Speed
Mercer 20.5 19.4 15.8 19.4
Somerset 25.7 21.3 14.5 22.7
Middlesex 19.7 14.4 19.2 14.6
Total 21.9 19.2 15.7 19.6
County Summary (Including Ext.)
Calb Tmd Scen1 Scen2
Ave Ave Ave Ave
Jurisdiction Speed Speed Speed Speed
Mercer 21.1 19.8 16.3 19.9
Somerset 25.9 21.5 14.6 22.9
Middlesex 21.4 16.0 20.5 16.2
Total 22.5 19.7 16.2 20.1
Source: Douglas & Douglas, Inc.
Appendix G
Suburban Mixed-Use Centers and Transportation:
Current Research and Issues
A technical report submitted to the Steering Committee
of the MSM Land Use/Transportation Project
The Land/Use Transportation Project is funded by a public
grant from the Urban Mass Transportation Administration, with
the support of the New Jersey Department of Transportation,
and with a private grant from the Fund for New Jersey.
Prepared By: Donna Bender, Senior Research Associate
For: MSM Regional Council
November, 1989
Revised June, 1990
Table of Contents
Chapter Page
1. Introduction - Summary of Issues and Current Research 1
2. Reality Rolls Around - Demographics on Wheels 5
3. Fashioning a Suburban Prototype 7
A. Density and Size 7
B. Land Use Mix 8
C. Pedestrian Encouragement 10
D. Transit-Friendly Features 12
4. Transportation Demand Management Strategies 17
5. Travel Behavior at Existing Mixed-Use Centers 22
6. New Jersey: Route 1 Corridor Region 32
7. Proposed Center Prototype 40
Appendix - NCHRP Trip Generation Rates 42
Bibliography 55
1. Introduction
Faced with the task of finding solutions to the burgeoning traffic
in present-day suburbia, transportation professionals and policy-
makers have been considering both old and new strategies. Planning
wisdom of the 1980's has suggested that building mixed-use centers, in
concert with the use of "demand management" techniques, is one of the
most effective ways of mitigating traffic growth. Afterall, before
the American love affair with automobile began, people actually lived
in settlements dense enough to support mass transit and mixed enough
to allow errands to be completed using the power of shoe leather.
Furthermore, the mixed-use approach is even stronger when considered
within the current context of "no new taxes," ergo, no new highways.
The intent of this study is to determine if the introduction of new
suburban land use patterns will reduce the growth in traffic
congestion on the regional network compared to what would occur if
current trends were to continue.
While the mixed-use solution seems sensible, we are still faced
with many questions about how (and if) it can be effectively
implemented in various suburban regions. To begin to identify, and
perhaps answer, some of the pertinent questions, we will examine what
others have learned in their analyses of existing and emerging
suburban mixed-use centers throughout the United States and elsewhere.
This technical report is intended to serve as both a catalyst for
discussion and a foundation for the "center" design and evaluation
procedures to be carried out in the second phase of this project.
Note: For the purposes of this report "centers" will refer to suburban
activity centers in which housing, retail, and commercial activity is
located.
Summary of Issues and Current Research
The literature search has revealed that, generally speaking, there
are no hard and fast rules which can be applied to land use
guaranteeing the achievement of our traffic growth reduction
objectives. We do, however, have some evidence that certain
approaches are more effective than others and that a combination of
strategies can produce a whole that is greater than the sum of the
parts. We are proposing that a built environment and policy approach
be created that encourages carpooling and vanpooling, living closer or
taking transit to work. The following is a summary of what we have
learned to date from the literature:
1. Suburban Demographics - Suburban areas have received the lion's
share of the population and employment growth over the past
several decades. The characteristics of the new suburban
population have great implications for the future of land use
and the transportation system. For example, travel patterns are
significantly affected by the increasing entry of women into the
workforce, the decline in the traditional married couple family
and the growing proportion of unmarried people. More people
must make daycare stops on the way to work or need to choose
housing somewhere between the workplaces of the husband and
wife.
1
New demographic patterns contribute to the fact that, on
average, most people old enough to drive a vehicle, have one.
Auto accessibility is the single strongest indicator of what
mode someone will choose to get to work or shopping. In
addition, suburban origins and destinations are too dispersed to
support frequent public transportation service.
Female and clerical workers are more likely to stop on the way
to and from work, while managerial/professional workers are more
likely than non-management workers to make midday trips. The
most frequently cited reason for stopping on the way to work is
to drop children at childcare or school; on the trip home,
shopping is the most common reason for interrupting the trip.
Conversely, non-management workers are also more likely to
rideshare than professional employees.
2. Density and Scale - Large, dense suburban activity centers tend
to have a higher rate of ridesharing and transit use and
increased pedestrian activities. It is not clear, however, if
there is some minimum density and size threshold, although it
has been suggested (without much substantiation) that a floor-
area-ratio (FAR) of at least 2.0 is necessary to achieve trans-
portation benefits. We have found that expressing density in
terms of FAR alone is not adequate. Measures such as employees
per acre and commercial space per acre may be more enlightening.
In addition, these large, dense centers also have associated
roadway congestion and may compete for capacity with through
traffic on the highway arteries where the centers are located.
3. Land Use Mix - While the predominate activity in suburban
centers tends to be office use, there is some correlation
between providing on-site retail and services and an increased
rate of ridesharing. If the services are well integrated into
the overall design, midday pedestrian travel is enhanced. The
land use mix should also accommodate other workforce needs like
daycare and household shopping. Case studies show that more
homebound intermediate trips will be captured on-site if the
center offers adequate shops and services, and is located in a
relatively isolated place with no adjacent shopping
opportunities.
4. Jobs-Housing Mismatch - A major factor contributing to traffic
congestion on the regional system is the spatial mismatch of
jobs and affordable housing. While providing housing within the
center might be considered desirable, it has often been the case
that few people both live and work within the center. Case
studies have shown, however, that those who own their own homes
within the center are more likely to work within the center than
those who rent. Housing that is appropriately priced and phased
will better accommodate the center's workforce. A jobs-housing
ratio of 1.5 has been suggested as an optimal balance within a
community, although having adequate housing within a three-to-
five-mile radius of the workplace has also been proposed as
being sufficient.
2
5. Designing for Pedestrians and Transit - An important element in
designing the centers is the clustering of the buildings on the
site. The reason for doing so is that people, on average, will
only walk a maximum of 1000 feet to take transit or do midday
shopping. In addition, transit will be able to service the site
much more effectively if the activities are concentrated rather
than dispersed. Pathways should be established so that
pedestrian travel can take place safely and with minimal
disruption en route.
Case studies have shown that a substantial retail component
(900,000+ square feet) within 2,000 feet of a sizable office
component (2 million+ square feet) will generate anywhere from 6
to 17 percent of midday trips on foot, depending upon the
quality of pedestrian connections. It has been suggested that
moderate bus service can only be supported with a minimum of 10
million square feet within less than a square mile at the work
destination and a density greater than 7 dwelling units per acre
at the origin.
6. Transportation Management - Transportation management strategies
like ridesharing, flextime, and parking regulations can be
effective ways to reduce the demand on the road system during
peak periods. It has been found that charging for parking is
one of the most effective ways to get people to rideshare and
take transit. The optimal combination of factors for a
successful transportation management program is: frequent
transit service, a limited supply of moderate-to-high-priced
parking, preferential HOV (High-Occupancy Vehicles) spaces, and
an on-site transportation coordinator who promotes
transportation management strategies and provides a custom
carpool matching service.
7. Trip Generation Rates - There is some evidence that the ITE
(Institute of Transportation Engineers) trip generation rates
are not applicable for every use within a mixed-use center. One
study shows that observed rates for regional malls, hotels, and
office space per square foot were lower than ITE, while office
rates per employee and residential rates per resident were
higher. Another study concluded that peak hour rates should be
reduced by 2.5 percent when applied to mixed use centers.
8. Route 1 Corridor Region - Growth in this region's economy
through the end of the century will take place in business and
health services, and trade. The types of jobs which are
expected to be created are either "high tech," computer-oriented
positions or skilled service jobs like nursing and maintenance.
Any growth seen in the labor force supply to fill these jobs
will be comprised mostly of women and minorities; if the
potential labor shortage situation is critical enough, the
"young elderly" will be enticed to stay in the labor force
longer. These factors must be considered when designing future
centers so that appropriate housing, services, corporate
3
facilities, and transportation management strategies are
provided to accommodate the lifestyles of the workforce in
addition to encouraging more desirable travel behavior.
9. Proposed Center Prototype - Given what we have learned in our
study of the literature and the previous analytical and
consensus-building efforts which went into the REGIONAL FORUM
effort, we propose the FORUM's "regional center' standards as a
starting point for development of prototype "centers" for
testing in the Land/Use Transportation Project. These standards
are:
Acreage 400+
Employment 9,000+ jobs
Population 5,700+
Housing Units 2,700+
Net DU's/Acre 8-11
Net Nonres. FAR 1.10
Jobs/Housing 3.5
Height Range 4-10 stories
It is the purpose of the Land Use/Transportation Project to
determine appropriate densities, scales, location, and demand
management policies for central New Jersey. It must be strongly
emphasized, however, that these standards alone are probably
inadequate for achieving our transportation goals without the
consideration and incorporation of the elements set forth in items 1
through 8 above. Further, additional analysis may lead us to modify
any or all of the REGIONAL FORUM figures. The remainder of this
report describes research projects which have focused on the
relationship between various aspects of land use and travel patterns.
4
2. Reality Rolls Around - Demographics on Wheels
To better understand the commuting dynamics in question, it is
important to consider what has actually gone on in suburbia in recent
years. One of the richest, most often quoted sources of information
on suburban trends is Commuting in America, (ENO Foundation for
Transportation, 1987). We will draw on this source to provide some
fundamental information about the people and patterns we intend to
change. First, we offer several facts about recent suburban
demographics:
1. Most of the population growth (86 percent) occurring since 1950
has been in the suburbs. Correspondingly, from 1960 to 1980,
two-thirds of metropolitan region job growth took place in the
suburbs.
2. The female labor force participation rate has grown from about
33 percent in 1950 to 60 percent in 1980. This trend is
expected to continue through the end of the century.
3. The growth in households has been far greater than the growth in
population. This is due to a rapidly declining household size
resulting from a decreasing proportion of traditional married
couple families.
4. Vehicle ownership is estimated to be approximately one per
licensed driver.
What does this tell us? First it tells us that the suburbs are
filling up with people who both live and work there. This is borne
out by the fact that the suburb-to-suburb commute now represents the
largest segment of all types of commuter flows. Second, a large
portion of the households no longer has the woman free to run errands
and look after children during the day. In addition, housing
decisions are being made based on the workplace locations of two wage-
earners rather than just one. This has some major implications for
travel patterns.
Prevedouros and Schofer (1988) have examined the lifestyle
implications of the increasing population of unmarried people. Single
people tend to spend their money on vehicles and real estate, and are
more mobile. This has contributed to the decrease in average
household size and the increase in the number of households. More
housing units are demanded than would have been needed if these
individuals had merged households by marrying. This, of course, has
land use ramifications.
Finally, the force that is perhaps the strongest influence on
travel behavior is that, in the aggregate, everyone who is old enough
to drive, has a vehicle. This is related to the rise in personal
income in the last several decades and the increase in the need for
more cars per household resulting from the growth of the number of
women in the workforce. Because auto ownership is a key factor in
whether or not someone drives to work or shopping, the current
suburban accessibility to autos has removed a once built-in factor in
controlling traffic congestion (Ducca, 1989.)
5
What this adds up to is a lot of people driving their cars all over
the suburbs to get to work, childcare, entertainment and shopping. We
need to look at how this plays out in terms of commuting patterns.
The following are additional relevant facts from Commuting in America:
5. Since about 1960, the portion of work trips made with a private
automobile has grown from 70 percent to over 85 percent.
Transit use has fallen correspondingly.
6. Vehicle availability to workers has increased to 1.34 vehicles
per worker from .85 in 1960.
7. Average commuting auto occupancy is 1.15 nationwide and falling,
with little variation from region to region. This trend is
linked to increased vehicle availability and the dispersed
suburban pattern of origins and destinations.
8. There are indications that both commuting times and distances
are getting longer.
American suburbia appears wedded to the single-occupancy vehicle
commute. Ken Orski has very poignantly described the traffic effects
of this suburban auto-orientation. He identified the phenomenon of
congestion spreading across space. The traffic jams frequently
associated with the CBD and close-by suburbs have spread to the
outlying suburban fringes of metropolitan regions. While previously,
commuters in the 'burbs could "take the backroads," there aren't any
free backroads left -- all the roads are crowded. In addition, in
many areas the "rush hour" lasts all day (Orski, 1987).'
In the past, the suburban areas served as bedroom communities with
commuters jumping on the radial, CBD-bound transit system to get to
work. Today, the suburb-to-suburb commute pattern is characterized by
a wide dispersion of origins ("o's") and destinations ("d's") with
commuters crisscrossing all over the region. This is a, situation
that traditional transit services have been unsuccessful in dealing
with. Because of the dispersed nature of the o's and d's, Orski has
pointed out that "there simply is not enough mass to make mass transit
work effectively (Orski, 1987).
6
3. Fashioning a Suburban Prototype
In this section, important elements of project development density
and scale, land use mix, pedestrian and transit-friendly features are
discussed in terms of their effects on travel behavior.
A. Density and Size
As mentioned earlier, in the suburb-to-suburb trip, both the
origins and destinations are often dispersed in low density
development throughout a region. Table 3.1 compares the densities of
120 suburban office developments with those in various central
business districts.
Table 3.1: Comparison of Office Density Characteristics
Suburban Office Complexesa Approximate
Difference Ratio
Average Low High CBD Rangeb of Suburbs to CBD
Floor area ratioc 0.29 0.06 1.48 5.0-10.0 0.04:1
(varies widely)
Floor space per
employee (gross ft.2) 380 140 970 175-200 2:1
Total land per
employee (ft.2) 1,410 230 3,360 35-50 33:1
a Based on a national survey of 120 suburban office developments.
b See Reference 8 and 9 for sources
c Floor area ratio represents gross floor space of all buildings
divided by the total land area of the office development.
Source: Cervero, 1986A.
Not only is land used much less intensively in the suburbs, floor
utilization is much less intense as well. We might assume that
without a critical mass of people working within a short distance of
each other, it is difficult to fulfill the objective of transit
utilization and ridesharing.
Cervero concluded several things about suburban density in his
study of 57 "suburban employment centers (SECs)." The densest projects
in Cervero's study which exhibited the highest incidence of
ridesharing also tended to be somewhat large. These centers contained
from 3.6 million sq. ft. to 25.3 million sq. ft. of
commercial/industrial space, with acreages ranging from 330 to 19,700.
They employed from 5,000 to 59,500 individuals (Cervero, 1988). He
found that high densities were positively correlated with increased
pedestrian activities, transit usage, and ridesharing. Through
analyzing various centers, Cervero suggested that a floor area ratio
of at least 2.0 is required for successful ridesharing and transit
usage.
However, he also stressed a dilemma associated with the density
issue. While large, dense agglomerations may in fact support the
establishment of ridesharing and transit, they also generate more
total trips than parcels developed at low densities (Cervero, 1988).
A study of the Atlanta region found that its suburban centers compete
with through traffic on the highway system adjacent to the centers.
The network is often inadequate to handle both flows (Atlanta
7
Regional Commission, 1985). The challenge is to design and locate
centers so that a higher proportion of generated trips are intra-site,
the total number of trips are more concentrated in the immediate
vicinity of the center rather than dispersed throughout the region,
and the center is not placed at a point on the network which is
already overburdened.
Intensifying the use of land often requires removing the height
restrictions which are typically three to four stories maximum in many
suburban areas. This is often politically unpopular in these
communities. The tallest buildings in the centers discussed above
range from 6 to 28 stories (Cervero, 1988). Height restrictions, in
concert with lot coverage limitations and large set-back requirements,
have the effect of spreading centers out in a low density, horizontal
fashion. This exacerbates dependence on the automobile and
discourages pedestrian trips because of long walking distances between
activities (Cervero, 1986B). Design and scale are important factors
in solving this problem.
What can be concluded from this information is that the
prototypical center should be somewhat large and dense. However,
because of the wide disparity in the sizes and densities of the
centers studied and the inherent positive and negative traffic effects
associated with high density development, it is not clear what the
minimum criteria should be. Furthermore, as we proceed through the
other design and policy considerations, it will become apparent that
adequate size and density are necessary but not sufficient conditions
for achieving our transportation objectives.
B. Land Use Mix
Along with density and size, Cervero cited the land use mix as
being a major factor in employee travel behavior at the 57 centers he
considered. Because much of the suburban job growth explosion has
been due to the relocation of back-office, information-handling
functions, the centers Cevero studied tended to be dominated by office
space. However, unlike the centers comprised exclusively of office
space, those with a substantial retail component tended to have a
higher rate of ridesharing (Cervero, 1988). This correlation appears
to support the idea that providing shops and services on-site will
entice employees to carpool or vanpool.
Increased ridesharing is only one potential benefit of providing a
mix of uses within the suburban center. In the case of a
retail/restaurant component, there is also the possibility that those
who do drive alone to work will take care of personal business on foot
at lunch-time, or at the very least, more of the non-work auto trips
will be confined to the center rather than the regional network during
peak hours. Of course, there are other factors to be considered in
providing retail, such as supplying businesses appropriate for the
type of workforce present in the center and ensuring that the overall
design of the project provides reasonable walking distances and
amenities to promote pedestrian activities. (This will be discussed in
more detail in a later section.)
Determining the optimal amount of each use is somewhat difficult.
An initial determination must be made about the primary use to be
located at the site -- is it office space, residential, manufacturing
or retail? Then, a variety of other factors come into play such as
physical characteristics of the site, the market potential for the
various uses, and the financing position of the developers.
8
Phasing is also an issue. If a major component of the project is a
large build-to-suit complex, then it is easier to construct the retail
uses earlier in the project because there is a guaranteed level of
demand once the client company's workforce moves in (Urban Land Insti-
tute, 1987). However, phasing becomes more difficult when the primary
use is developed over an extended period of time to allow for
incremental market absorption. When looking at some case studies
later in this report, we will be able to see examples of various use
mixes in existing centers.
Perhaps the most difficult element to grapple with in discussing
the importance of mixing uses is the inclusion of housing. One of the
major forces contributing to the congestion on suburban roads is the
jobs-housing mismatch. It seems logical that if given a choice,
people will not choose a long commute. However, it is often the case
that there is little choice in places to live once a particular job is
secured. This is because of a spatial mismatch of jobs and housing,
often the result of fiscal and exclusionary zoning practices. Towns
frequently prefer to zone for more commercial development than
residential because of perceived tax benefits. In addition,
exclusionary zoning means that only expensive, large-lot residential
projects are allowed, restricting the supply of affordable housing
available for those who will work in the nearby employment centers
(Cervero, 1989). The net result of the jobs-housing mismatch is an
acute regional labor shortage and many workers with long commutes.
This adds trips to parts of the regional network which wouldn't be
there if a better balance of jobs and housing existed within
communities.
Robert Cervero conducted a regression analysis of the relationship
between providing on-site housing and traffic congestion at 26
suburban centers. His findings confirmed those in his previous study.
Large, dense, and in this case, housing-free centers tend to have the
worst local traffic congestion. He also concluded from a similar
analysis that a better balance of jobsto-housing provides marginal
increases in pedestrian and bicycle travel (Cervero, 1989).
Basing his calculations on recent figures showing that 90 percent
of the adult population lives in cohabitant households and that 70
percent of these households are comprised of at least two wage
earners, Cervero concluded that 1.5 is the maximum jobs/housing ratio
required for achieving a balanced community. However, he found that,
in many cases, even where housing was provided on-site, most of those
occupying the units did not work within the center. This may again be
related to a lack of units affordable to over 40 percent of the
workforce, employed in clerical and non-professional jobs. Cervero
suggests that having adequate housing within a three-to-five-mile
radius of the workplace is sufficient (Cervero, 1989).
Thus, the challenge we are facing when determining the character of
our mixed-use center prototype is to provide an appropriate supply of
housing near the job sites. This means understanding the kind of
workforce to be accommodated so that the right types of units will be
furnished. To do so requires an analysis of both current and future
economic development trends, and occupational and income information.
The Association of Bay Area Governments in California established a
comprehensive program for achieving a jobs-housing balance to mitigate
traffic in the region. In the first phase, an assessment of the
regional labor force and housing needs was conducted, and a model for
9
predicting future needs was developed. A series of measures to be
promoted by local governments was then developed:
1. Increase the supply of housing close to employment centers;
2. Encourage production of affordable housing;
3. Phase housing construction with job growth;
4. Improve access to transit for home-to-work trips;
5. Encourage developers to locate near existing affordable housing;
and,
6. Increase employment of local residents in the new jobs.
Each of these measures is promoted with specific suggestions on how
to carry it out (ABAG, 1985). Strategies like these should be
considered when designing the suburban prototype to be tested in our
region.
C. Pedestrian Encouragement
One of the primary objectives in designing a prototype center is to
induce people to walk more and drive their automobiles less. To do
this we must provide certain physical amenities. Earlier we mentioned
providing on-site retail, services and housing. However, merely
providing these features is not enough. If people have abandoned
their automobiles to rideshare or take transit, we must make sure that
facilities are within a reasonable and comfortable walking distance.
When designing a center with our objectives in mind, the pedestrian
trip must be given a very high priority. If the buildings are widely
dispersed over the site, people will not be motivated to walk and the
auto will dominate. Figure 3.1 shows the difference between designing
for the auto (Plan A) and designing for the pedestrian (Plan B). One
of the key elements in pedestrian-friendly environments is to cluster
the buildings so that walking distances are minimized and interaction
between uses can be more easily facilitated (Jackson and Kulash,
1988). This clustering approach also better accommodates transit, to
be discussed in the next section.
10
Figure 3.1: Land Use Options
Click HERE for graphic.
Source: Jackson, Timothy T. and Walter Kulash, "Land Use and
Transportation Engineering Measures to Support Clustered Development,"
ITE, 1988.
There is a rule of thumb that walking distances from the parking
lot should not exceed 300 feet (Urban Land Institute, 1987). Since we
are focusing on how to encourage pedestrian travel of all sorts, we
have to search further for some standards. A recent survey showed
that 70 percent of all walk trips generated from suburban workplaces
are 0.2 miles (1,056 ft.) and 90 percent of the trips are 0.4 miles
(2,112 ft.) or less (Barton-Aschman,1989). If we consider that one
study showed an average walking speed of 265 feet per minute (Fruin,
1971), this means that 1.056 feet would take about 4 minutes to walk
and 2,112 feet would take about 8 minutes to walk. Given that most
people have only an hour for lunch, it is reasonable to assume that
walking much more than a 16-minute roundtrip would consume too much
time to justify the journey. Similar distances have been cited by
others, with one study concluding that only 15 percent of Americans
are willing to walk 2,000 feet for non-leisure trips and another
suggesting that the maximum acceptable walking distance in suburban
areas is 1,000 feet (Cervero, 1988). This 1,000 feet should serve as
a guideline in determining the proximity of the various uses within a
mixed-use center.
An appropriate path system is necessary to encourage both
pedestrian and bicycle trips. These pathways must be designed with
sensitivity to the needs of these individuals and with the objective
of spatially linking the various uses. Often when sidewalks are
provided, they are located along wide boulevards designed to
facilitate optimal automobile flows. However, pedestrians seek the
shortest distance between two points, not always conforming to the
street configuration (Cervero, 1986B). Furthermore, the scale of
these auto-oriented streets may make pedestrian travel dangerous as
walkers try to cross the street. The optimal approach would be to
provide a pathway system that includes crossing signals at the points
where the pathway intersects the street and design it so that the
pedestrian has a safe, direct way to move from building to building.
Another feature to include in this clustered, linked environment is
outdoor green space plazas. While many office "parks" currently
provide expanses of open space, they are frequently
11
only large front and side yards created out of compliance with zoning
regulations. These areas have no design relationship to one another,
lack a central focus, and offer absolutely no pedestrian facilities
like benches. To encourage people to get out of the buildings and
walk, outdoor spaces should be inviting, providing a "central place"
and enhancing the human scale rather than the automobile scale.
D. Transit-Friendly Features
We have briefly discussed reorienting toward the pedestrian, but
now we should go one step further and think about accommodating
transit at suburban centers.
To illustrate the conflict between auto-friendly and transit-
friendly designs, Stephen Potter studied British new towns. Figure
3.2 shows optimal designs for both automobile and transit
accommodation. To prevent congestion from developing at various
points in the autooriented town, it is necessary to distribute various
uses at low densities throughout. However, in the transit-oriented
scenario, there are benefits to creating high density clusters close
to the transit line so frequent service can be maintained and evenly
spread along the route. Thus, the auto and the bus require two very
different operating environments (Potter, 1984).
Figure 3.2: Optimal Urban Structures for Public and Private
Transport
Click HERE for graphic.
Source: Potter, Stephen, "The Transport Versus Land Use Dilemma," TRB
#964,1984.
Potter looked at the effects of adopting these opposing designs in
several new towns. Table 3.2 summarizes the characteristics of
several of the new towns considered:
12
Table 3.2: Key Characteristics of the New Towns Under Study
Milton
Keynes Washington Reddich Runcorn Peterborough
Population 107,000 55,000 68,000 65,000 124,000
Current gross density
(ppha)a 12 24 23 32 19
Planned gross density
(ppha)a 20 27 25 34 23
Development costs to state
per person housed 10,200 11,000 4,100 7,000 5,300
Average bus Frequency
(min) 30 20 10 5 15
Cost of bus season ticket
per week 2.40 1.65 3.50 2.50 3.50
Subsidy as percent of bus
running costs 42 na 6 5 14
Average number of shops at
local center 5 9 15 7 23
Note: This table includes two new towns in addition to those
considered in the text. Washington (in northeast England) is of
comparable size to Redditch and Runcorn but was designed
similarly to Milton Keynes. Peterborough is comparable in size
to Milton Keynes but was designed to promote public transport.
a Persons per hectare.
Source: Potter, Ibid.
Milton Keynes and Washington were designed to accommodate the
automobile, while the Redditch, Runcorn and Peterborough plans tried
to strike a balance between transit priorities and the presence of
autos. Although the original Milton Keynes plan called for frequent
transit service, once the auto-oriented, low density land use plan was
established, the planners realized they had made transit-provision
very difficult. The original intention of having 2.5 to 5-minute
headways for bus service became impossible without an inordinately
high subsidy. As Table 3.2 shows, even with headways of 30 minutes,
the Milton Keynes bus system required an operating subsidy of 42
percent.
The contrast between this situation and that in Redditch and
Runcorn is quite striking. Not only are these towns able to provide
headways of 10 and 5 minutes, respectively, they are able to maintain
the service for a very low subsidy. Furthermore, Potter reports that
the capacities of the Redditch and Runcorn road systems have been
quite adequate in serving the autos which are present on the system.
In addition, the orientation toward a transit environment has made the
town pedestrian and bicycle-friendly.
As an aside, the other aspect to note about the differences between
these new towns is the cost of construction. By concentrating the
majority of the activities in denser areas of the town near the
transit line, the areas at the periphery do not have to be crossed by
water pipes, electric cables, etc. and so provision of all types of
infrastructure is more efficient than in the case of the dispersed
land patterns. Table 3.2 shows the contrast in the development costs
of the auto versus transit-oriented new towns. Figure 3.3 shows the
land use plans for Milton Keynes, Runcorn and Redditch.
13
Potter summarized the basic design principles of Runcorn, Redditch
and Peterborough as follows:
1. Public transport and car flows are on separate networks, making
it possible to concentrate travel flows for public transport
while dispersing car traffic.
2. The size of residential areas is determined by the population
needed to maintain a frequent public transport service.
3. Residential densities are zoned so that they increase toward
public transport routes.
4. Low-density uses (e.g., open space, warehousing, major roads,
and parks) are zoned away from public transport routes so as not
to increase walking distance to routes.
5. Residential areas, employment, shopping, and other major travel-
generating land uses are arranged so that they provide corridors
of public transport movement conducive to high service
frequencies.
6. The overall density of development is changed little, but land
uses are rearranged to provide a pattern of development that is
conducive to public transport operations.
14
Figure 3.3: Comparative Land Use Patterns
Click HERE for graphic.
15
Because we rarely have the opportunity these days to establish
large-scale new towns, the challenge is to take these transit design
principles and incorporate them into the suburban fabric in some
effective way. As mentioned previously, current suburban development
patterns are often too dispersed and lacking in density to support a
transit system with a reasonable level of service. Pushkarev and
Zupan concluded that nonresidential downtowns, if spread over an area
less than one square mile, must contain at least 10 million square
feet to support a moderate bus service. However, they also commented
that suburban clusters of nonresidential space can only occasionally
support minimal bus service and even this is usually only possible if
they contain retail centers or are surrounded by housing in densities
greater than 7 dwelling units per acre (Pushkarev and Zupan, 1977).
These conclusions must be explored further because there are
examples of suburban centers with good bus systems. One example is
Bellevue, WA, a suburban center located near Seattle. Bellevue
contains approximately 4.7 million sq. ft. of office space and 3
million sq. ft. of retail, enabling it to support enough bus service
to achieve about a 7 percent transit work trip mode share, considered
quite good in suburban terms (NCHRP, 1989). Bellevue will be studied
in more detail in Section 5. As we continue to increase our
information base to prepare a suburban mixed-use prototype, we will
have to further define the feasibility of supporting a reasonable
level of transit service.
16
4. Transportation Demand Management Strategies
Demand management is a part of a broad spectrum of policies and
engineering strategies called Transportation Systems Management (TSM).
Demand management devises strategies to decrease the number of
vehicles demanding capacity on the roads during the peak period. We
will use demand management strategies in concert with the mixed-use
center design principles discussed above. Note that our study assumes
that the capacity of our transportation system will increase only by
those improvements which are already planned through 2010.
The Federal Highway Administration conducted a study to determine
the effectiveness of using supply and demand management strategies.
In this work, travel demand management strategies included:
ridesharing, scheduling techniques, access management, reduction in
the need to travel, land use and zoning laws, and vehicle restrictions
such as traffic ordinances, congestion and road pricing, and goods
movement. It was found that applying these measures to the highway
and secondary road system could reduce VMT anywhere from 3 to 8
percent. This was calculated using a high and low scenario approach.
The high scenario assumed that one in five SOV (single-occupancy
vehicle) drivers could be induced to rideshare or take public transit.
The low scenario assumed a rate of one in ten SOV drivers choosing
alternative travel means (Lindley and McDade, 1988). In this section
we will look at the aspects of demand management which are most
applicable to our centers.
Transportation Management Associations (TMAs)
Transportation management associations (TMAs) are organizations
created to promote demand management strategies. Membership can be
either voluntary or mandatory, depending upon local statutes, and the
membership is usually comprised of private sector participants and/or
government entities. In some cases, the organization may be entirely
a private sector initiative serving a particular office complex or
group of businesses. In most instances, TMAs emerge in suburban areas
with high concentrations of white collar workers and low levels of
transit service (Cervero, 1986B).
The focus of a particular TMA depends upon its membership and the
transportation problems specific to its region. The TMA can become
involved in anything from lobbying for transit improvements, to
providing computerized carpool matching services, to actually broker-
ing vans and buses. The developer and private sector-supported TMAs
tend to shy away from promoting legislation which requires developer
contributions for road improvements or mandatory traffic reduction
programs.
The central issue for this report is how effective these TMAs might
be in reducing traffic associated with the mixed-use centers we are
studying. Much of this effectiveness depends upon how successful the
organization is in applying demand management strategies appropriate
to the particular problems of its region. There are moderately
successful cases like the one in Tysons Corner, VA, where 70,000
workers converge daily on this large office/retail center. The Tysons
Corner Association initiated a vanpool program and shuttle bus system
which got 5,000 vehicles off the area's clogged roads (Cervero,
1986B). As discussed below, the most successful efforts tend to be
carried out for and by large, single-tenant projects like Pacific
Northwest Bell with
17
1,200 employees in Bellevue, WA. Through a combination of incentives
and disincentives, PNB recently reported a mere 25 percent rate of
solo commuting (UMTA,1989).
On the other hand, there is the Newport Center Association in
Southern California which closed down after a year of promoting
ridesharing to 10,000 employees in an area of Newport Beach. The
whole program failed because of inadequate top-level management
interest and commitment among the target corporations. The most
difficult situation for a TMA to surmount is one with a multitude of
small office developments with many different tenants (Cervero,
1986B). To further assess the potential effectiveness of
transportation management initiatives, we will look at individual
strategies below.
Ridesharing
In an attempt to reduce the number of vehicles on the road,
programs are often instituted to encourage people to either carpool or
vanpool. It has been concluded, however, as evidenced in Newport
Beach, that employers must get involved for ridesharing programs to
succeed. Some employers have actually designated on-site
transportation management coordinators to provide matching services
and promote the program. There is some evidence that the presence of
a coordinator does help to increase ridesharing participation. In a
survey of 120 sites, those without a coordinator were found to have an
average ridesharing of 5 percent, compared to 11 percent at those with
coordinators (Cervero, 1986B).
As mentioned previously, ridesharing programs tend to be less
successful at sites with multiple establishments. Even places with
active TMAs like Tysons Corner have reduced SOVs by about three or
four percent primarily because of this multi-tenant constituency.
Firm size and type of labor force also affect ridesharing rates. The
greatest success has been seen at large firms with relatively sizable
portions of clerical and data processing staff. One survey showed
that non-SOV shares at firms with over 1,000 employees range from 30
to 40 percent, while those under 1,000 average around 20 percent
(UMTA, 1989).
Design incentives are an important consideration. Designating
priority parking near the building for carpools and vanpools is an
inexpensive way to encourage ridesharing. Providing pedestrian-
accessible, on-site restaurants and stores encourages employees to
give up their autos. If stores and services are not within a
reasonable and comfortable walking distance, which is the preferred
situation, then excellent shuttle service connecting these uses must
be furnished. These elements also encourage transit usage, a topic
which was considered in more detail in Section 3-D).
There are other factors which affect the success of ridesharing
programs. In the discussion on the jobs-housing mismatch, it was
proposed that having a substantial portion of the workforce living
within three to five miles of the job site was adequate to overcome
the problem. While this will reduce vehicle-miles traveled (VMT), it
will also most likely thwart ridesharing efforts if SOV disincentives
are not also employed. Commuters with long trips tend to rideshare
more readily than those living nearby. However, because we are
concerned with the regional road system, the localized congestion
caused by a more proximate workforce may be the price we pay to see a
decline in VMT.
18
Flextime, discussed in the next section, also might act to undo
ridesharing efforts. While flextime might serve to spread out the
arrival and departure times of employees so that peak congestion is
reduced, it also makes matching people for ridesharing more difficult
because the starting times might vary widely. However, there is
conflicting evidence on this point. In the San Francisco Bay Area,
those having flextime privileges were able to be matched for rideshar-
ing 30 percent of the time compared to 16 percent for those not on
flextime. On the other hand, in Pleasonton, CA, only 7.9 percent of
the employees with flextime rideshare compared to the 11.4 percent
rate for the entire workforce (UMTA, 1989). Again, the key to
applying transportation management techniques is understanding the
needs and priorities of the population being targeted.
Time Scheduling Techniques
Time scheduling refers to flextime and staggered hours programs.
The main objective is to avoid exacerbating peak period congestion by
extending the period of time over which employees arrive and depart.
Flextime is implemented on an individual company basis and involves
establishing windows of time in the morning and evening within which
employees can choose their work hours. Usually, an employee can
choose to arrive at work between 7:00 a.m. and 10:00 a.m., work the
required number of hours and then depart between 3:00 p.m. and 6:00
p.m. The net effect is that all employees are not converging on the
site between 8:45 a.m. and 9:00 a.m.
The same effect can be achieved through staggering work hours in a
multi-tenant complex. This requires businesses to establish work
hours starting at various times, with each business maintaining a set
daily work schedule. For example, company A may have an 8 to 4 day,
while B has an 8:30 to 4:30 day, and C works 9 to 5. Another approach
to staggering hours carried out within a particular firm is to have
shifts with several different starting times in the morning, instead
of allowing individuals to choose their arrival times as is the case
under flextime.
As mentioned earlier, there is some skepticism about the
effectiveness of flextime in achieving regional traffic reduction
objectives. In some cases it has been shown to interfere with
ridesharing programs unless the two programs are linked. On the other
hand, this flexibility is certainly a blessing to working parents and
those who have long commutes both in cars and on transit. As with all
policies, time scheduling techniques will only be effective if applied
in appropriate situations.
Parking Management
Probably the single most effective means of getting SOV commuters
to change their behavior is through regulating the parking supply at
the workplace. The Pacific Northwest Bell case in Bellevue, WA, is a
prime example of this. When the project was built, there were only
440 parking spaces supplied for 1,200 employees. Of these spaces,
over half were designated for ridesharing vehicles. In addition,
those having a vehicle occupancy of less than three were required to
pay $60 per month to park. The net effect has been a decline of SOV
commuting to 25 percent (NCHRP, 1989).
19
It must be kept in mind that parking disincentives cannot be
imposed without presenting some ridesharing or transit incentives.
Otherwise, it may become difficult to hire employees. In the PNB
case, there is an in-house ridesharing coordinator who provides
rideshare matching services, a good bus system serving the area, the
use of flextime, and reduced parking rates for those who manage to
form a carpool with only two people (UMTA, 1989).
Another example of the effectiveness of combining parking
disincentives with alternative incentives is the Twentieth Century
Corporation at Warner Center in West San Fernando, CA. This company,
with 1,150 employees, reduced the solo driving rate from 95 percent to
65 percent by having a ridesharing coordinator who provides matching
services and transit passes, by giving free parking to carpools, and
by charging SOVs. It was noted that when the company began charging
for parking, the carpool rate jumped from 6 to 31 percent (UMTA,
1989).
One of the problems with restricting parking supply is the strong
opposition of many developers, particularly those who build
speculative projects. Currently, developers expect to be able to
supply between three and four parking spaces for every 1,000 square
feet of office space, claiming the market will not accept anything
less. This results in a sea of parking that caters to the SOV.
Furthermore, recent calculations show that a standard at-grade parking
space costs $4,972 on average for development and constructions costs
with additional operating expenses of $955 per year. For a
freestanding multi-level parking structure, the figure jumps to
$20,125 per space plus $2,756 annually for operating costs (Urban Land
Institute, 1989). Current practices actually subsidize people who
drive, while those who take transit often get nothing. Parking policy
is something that both developers and local regulators must seriously
reassess.
Traffic Reduction Ordinances
We have mostly been talking about getting the SOV drivers to change
their behavior. However, as mentioned previously, transportation
management programs do not work without the support of upper
management. Therefore, sometimes it is necessary to take measures to
get executives and developers to change their behavior as well. These
measures have recently been taking the form of traffic reduction
ordinances.
Generally speaking, a traffic reduction ordinance is a law enacted
by a local government which requires companies to undertake programs
to reduce SOV trips by some specified amount. The most notable
example is Pleasanton, California. Its ordinance applies to employers
with 10 or more employees, with stricter requirements imposed on
larger companies and developments. The broad goal is a 45 percent
reduction in SOV trips over a specified period of time. The company
is given free reign to achieve this goal within this period, and if it
does not, the city may impose a specific program. Then, if this plan
is not implemented, fines of $250 per day can be levied until the
company complies (UMTA, 1989).
Other such ordinances are being enacted all over the country. Some
areas like the South Coast Air Quality Management District in
California are taking such measures with the ultimate goal of reducing
air pollution from auto emissions. In New Jersey, a bill has been
introduced in the State Legislature requiring all municipalities to
develop traffic reduction ordinances. We can expect to see an
increasing number of these ordinances in the next several years.
20
Summary
To sum up the implementation of transportation management programs,
UMTA has prepared the table presented below. This concise synopsis of
transportation management will be referenced again in the process of
designing our mixed-use center prototypes.
Best and Worst Cases for Transportation Management Programs
Click HERE for graphic.
Source: UMTA, "An Assessment of Travel Demand Approaches at Suburban
Activity Centers," 1989.
21
5. Travel Behavior at Existing Mixed-Use Centers
Trip generation and modal split rates are typically assigned
standard values which have been calculated using information from
existing places. However, because there is not a great deal of
experience with the mixed-use suburban prototype we are studying, the
standard values may not be appropriate. Thus, we must look at case
studies of existing mixed-use centers to help us understand how to
model behavior accurately for our prototype. (Note: No center studied
has all the characteristics we have determined would be needed in our
suburban prototype. Therefore, figures derived from existing places
must be considered of limited significance.)
There are two noteworthy studies for us to draw upon. The first is
a study in progress being conducted by the National Cooperative
Highway Research Program (NCHRP) of the Transportation Research Board:
"Travel Characteristics at Large-Scale Suburban Activity Centers." and
the second, "Trip Generation for Mixed-Use Developments," was
published in 1987 by the Colorado/Wyoming Section of ITE. Both
projects utilized survey instruments to gather actual data on travel
patterns associated with mixed-use centers. The conclusions are
presented below.
"Travel Characteristics at Large-Scale Suburban Activity Centers"
The NCHRP consultants chose six recently-developed "suburban
activity centers," each with at least 5 million square feet of office
and retail, with the retail component being at least 600,000 square
feet. These centers are between 5 and 45 miles from the regional
central business district: Bellevue (Seattle), South Coast Metro (Los
Angeles), Parkway Center (Dallas), Perimeter Center (Atlanta), Tysons
Corner (Washington, DC), and Southdale (Minneapolis-St. Paul). More
detailed characteristics of each center can be found in Table 5.1
below.
The team produced a comparison, by land use, of observed trip
generation and trip generation which would result from the application
of published ITE rates. This assessment was conducted for both AM and
PM peak periods. The detailed trip generation tables included in the
NCHRP report are presented in the Appendix. Following are the general
conclusions drawn from the comparison:
1. Office - On a per square foot basis, the observed rates were
lower than ITE. However, the observed rates per employee were
generally higher than the published ITE rates.
2. Retail - The majority of the regional malls surveyed showed
rates lower than the ITE rates. The results varied, however,
among the specialty, community and neighborhood centers.
3. Residential - On a per occupied square foot basis, the observed
rates are comparable to the ITE published rates. Per resident,
however, the observed rates are actually higher.
4. Hotel - The majority of the hotels had a lower observed Tate
than the ITE rate.
22
"Trip Generation for Mixed-Use Developments"
The ITE Colorado Section Technical Committee on Trip Generation
conducted its survey at mixed-use sites in Colorado only. Compared to
the NCHRP centers, the Colorado centers chosen were rather small,
ranging from 95,104 to 1,000,000 square feet. The only criterion for
use mix was that the site include two or more different uses. The
general conclusions reported in an article in the February 1987 ITE
Journal were:
1. Published ITE rates can be used to estimate total daily trip
generation for mixed-use centers.
2. The peak hour ITE rates should be reduced by 2.5 percent when
applied to mixed-use developments.
3. Studies should be conducted in other states to determine if the
results of this study are valid.
Given the somewhat inconsistent nature of the conclusions of these
two studies, the specific trip generation rates used in the evaluation
phase of this study will have to be carefully assessed.
A Comparison of the NCHRP Study and the Rice Center Study
A research project conducted by the Rice Center for the Houston-
Galveston Area Council in 1987, "Houston's Major Activity Centers and
Worker Travel Behavior," looked at travel characteristics associated
with the Houston CBD, and three suburban centers in the Houston
region: Greenway, City Post Oak and the Energy Corridor.
Table 5.1 presents the general characteristics of the Houston CBD, the
three Houston suburban centers and the six centers covered by the
NCHRP study. These centers range in size from Bellevue, which is 440
acres, to Parkway Center near Dallas, which is 1,870 acres. Each
center contains some amount of office, retail, hotel and residential
uses, although data is not available in detail for each of these items
in every center (see notes on Table 5.1). Because average FAR's were
not always available, commercial space per total acreage was
calculated for each center as a rough means of comparing development
intensity. Houston CBD and City Post Oak are the most dense centers
when evaluated using this measure.
23
Table 5.1: Characteristics of Case Study Centers
Click HERE for graphic.
* The employment figures for the NCHRP centers include only workers
associated with the office and retail space.
** The Houston study did not focus directly on the travel
characteristics of residents in the centers and so no counts of
residential units were done. The figures given for Bellevue and
Tysons Corner represent only those surveyed and met total units in
the centers.
The NCHRP study looked at employees per acre to also get some sense
of the intensity of use of floor space. This calculation yields the
following based on office and retail employees and total acreage:
emp./acre
Bellevue 43.2
S. Coast Metro 29.9
Parkway Center 25.9
Perimeter Center 29.3
Tysons Corner 30.6
Southdale 20.7
When evaluated in these terms, Bellevue clearly is the most
intensively utilized center of these six.
Employee Work Trips
One of the first elements to assess is the work trip patterns of
the employees of a center. A major aspect of the journey-to-work is
modal split. Table 5.2 shows the mode choice determined through the
administration of a travel survey at the NCHRP centers; the data for
the Houston centers has been taken from the 1980 Census journey-to-
work information because mode information was only gathered for all
trips in aggregration by the survey team.
24
Table 5.2: Work Trip Modal Split
Click HERE for graphic.
Note: Modal Statistics were gathered for all of the centers through
the administration of travel surveys. However, the Houston
surveys obtained only information on mode split for all trips,
not just work trips. Therefore the information presented here
for the Houston centers is taken from 1980 Census journey-to-
work data.
Although we must be somewhat guarded in drawing conclusions from
the Houston 1980 data, there are several points that seem fairly
apparent about the modal choices among all ten of the centers. First,
Houston CBD and Bellevue have substantially higher bus utilization
than the other centers. In the case of the Houston region, over 90
percent of the transit routes are CBD-oriented, which may partially
explain for why the bus utilization is much lower in the suburban
centers despite of the fact that City Post Oak is a fairly large and
dense location.
The Bellevue bus share of 8.8 percent is remarkable given the
relatively small size of this center compared with most of the others.
Like Houston, this is partially explained by the differences in
transit supply between Bellevue and the other five NCHRP centers.
None of the other five centers has fixed-route transit serving it as
an end-of-the-line destination. However, Bellevue has 17 Seattle
Metro routes delivering commuters to the Bellevue Transit Center,
which has bus bays, covered seating areas and information booths.
Thus, while demand for transit certainly is a crucial element, the
supply side is equally important. The destinations can be very large
and dense, but if there is not adequate service available to the
workforce, obviously there is no means of inducing use of transit.
Another element is the rate of carpooling and vanpooling. Because
the data on ridesharing was collected differently in the two studies,
a comparison cannot readily be made. However, Table 5.3 shows the
average automobile occupancy for all of the centers. There is no
qualitative information in the Houston report to explain why the least
dense center, W. Houston Energy Corridor, has one of the highest
vehicle occupancy rates. While it makes intuitive sense that the
Houston CBD has a relatively higher occupancy rate, it is not
immediately apparent why the moderately-sized Greenway Center has the
highest rate. It is neither the largest nor the densest of the ten
centers. The report may fail to mention area TMA's which are
affecting these rates.
25
Table 5.3: Average Auto Occupancy - Work Trips
Average
Auto
Occupancy
-------------
Houston CBD 1.21
City Post Oak 1.13
Greenway 1.26
W. Houston Energy Corridor 1.21
Bellevue (Seattle) 1.16
S. Coast Metro (Los Angeles) 1.07
Parkway Center (Dallas) 1.06
Perimeter Center (Atlanta) 1.07
Tysons Comer (Washington, DC) 1.11
Southdale (Minneapolis) 1.07
A clue to the success of ridesharing is found in the case of
Bellevue. Bellevue's auto occupancy rate of 1.16 is not remarkable
when compared to the other centers. However, when one office building
is removed from the figure, the rate drops to 1.10. This particular
building, PNB Plaza has an auto occupancy rate of 1.74 and a transit
usage rate of 12 percent. This anomaly is due to a very stringent
parking management system at the PNB building described in Section 4.
With 1,200 employees in the building, there are only 402 on-site
parking spaces and over half are reserved for HOV's. In addition,
vehicles arriving with three or more persons can park for free;
otherwise, the fee is $60 per month.
Intermediate Trips
Another influence on modal split and the overall regional traffic
congestion level is the rate at which people take trips for purposes
other than to get to and from work. Earlier in this report, we
discussed the importance of understanding the lifestyles of the
current workforce so that we may better influence the commuting
patterns. Looking at why people stop on the way to and from work, and
what they do on their lunch hours may assist us in determining how to
design centers which will take some of the strain off the regional
transportation network.
The NCHRP study did an excellent job of capturing the patterns of
intermediate stops made during the work trip and the midday. The
results are summarized in Table 5.4. Bellevue has a significantly
higher proportion of employees making stops to and from work than the
other five centers. The NCHRP study team attempted to determine a
reason for this and could not. They posed the hypothesis that
Bellevue is far more dense and compact than the other centers, but no
support for this theory was readily apparent. Bellevue employees show
midday rates similar to the other centers.
26
Table 5.4 Characteristics of Trips Made By Suburban Activity Center
Employees
Click HERE for graphic.
Source: NCHRP, 1989.
Excluding Bellevue, the two centers with slightly higher rates of
employee stops en route are South Coast Metro and Parkway Center. It
was determined that this is due in part to the presence of greater
proportions of female and secretary/clerical workers in these two
centers. These groups tend to have more intermediate stops than
others.
Important to examine in these patterns is the proportion of those
who make intra-center stops. We proposed early on that to reduce
trips on the regional network, more trips would have to be captured
within the center. The NCHRP team identified a possible causal factor
for centers having lower than average intra-center stop rates. The
four centers with lower rates are South Coast Metro, Parkway Center,
Southdale, and Tysons Corner. The one factor these centers have in
common is the proximity of external retail trip generators. Thus,
more people will be attracted to stop outside these centers than in
the case of Bellevue and Perimeter Center which are relatively
isolated in terms of activity concentration in their region. The
NCHRP team proposed the following relationship:
1. For centers with relatively little retail activity immediately
adjacent, about 13 percent of the employees will stop within the
center on their way to work and approximately 15 percent will
stop there on the way home.
27
2. Centers with a significant amount of retail immediately adjacent
will have approximately 8 percent of the workforce stopping in
the center on the way to work and about 10 percent stopping on
their way home.
Table 5.4 also shows the patterns of midday trip-making. The NCHRP
team determined that there is a correlation between occupation and the
proclivity for making a midday trip, with professional/technical staff
more likely to go out at lunchtime. Given the data gathered from the
six centers, the following relationships were suggested:
1. For centers with at least 60 percent professional, technical,
manager, or administrator positions, the proportion of office
employees making midday trips within the center ranges from 29
to 33 percent.
2. For centers which have lower proportions of these professional
categories, the expected internal midday trip rate is between 20
and 23 percent.
Another factor which influences the midday internal trip patterns
is the availability of eating establishments. The fact that Perimeter
Center has the highest midday intra-center trip rate is probably due
to the availability of various restaurants within the center and a
corresponding lack of lunch opportunities in the largely residential
area surrounding the center.
Intermediate Stop Trip Purposes
The NCHRP study also surveyed intermediate stop trip purposes. The
results are presented in Table 5.5. The most frequently cited reason
for a stop on the way to work is to drop a child at childcare or
school -- an average of 34 percent of the office workers stop for this
purpose. In second place, an average of 21 percent said they stop on
work-related business on the way to the office. On the way home, 21
percent stop to shop, 14 percent pick up a child at school or
childcare, 15 percent stop for social or recreation reasons such as
health clubs, and 13 percent stop at the grocery store.
It is rather clear given these intermediate trip purposes that
there is ample opportunity to shape travel patterns by providing
needed services within the center. If there were childcare services
on-site, perhaps more people would be free to carpool by bringing the
child along. If there were shops, restaurants and supermarkets within
the center, workers might be enticed to remain in the center for a
longer period of time, thus spreading the peak demand for regional
highway capacity. These factors must be considered in the design
phase of this project.
28
Table 5.5: Intermediate Stop Trip Purposes
Distribution of Trip Purposes by Time Period
Along Trip To Work Midday Trips Along Trip Home
Trip Purpose
Work Related 21% 25% 6%
Meal/Snack 10 35 4
Shopping 3 13 21
Childcare/School 34 * 14
Pick Up/Drop Off Passenger 5 1 3
Education *1 * 2
Social/Recreation2 3 3 15
Home * 4 03
Banking 7 9 6
Medical 2 2 3
Dry Cleaners 9 1 7
Gas Station 04 1 04
Grocery store 2 1 13
Other 3 3 6
100 100 100
1 *indicates less than 1 percent
2 Health club trips have been included under the Social/Recreation
category
3 By definition, trips to home from work cannot have an intermediate
stop at home
4 Intermediate stops at gas stations along the way either to work or
from work have been excluded in this distribution. During the trip
to work, the survey indicates that roughly 11 percent of all
intermediate stops are at a gas station. Along the trip home,
roughly 9 percent of all intermediate stops are at gas stations.
Source: NCHRP, 1989.
Table 5.5 also shows Midday trip purposes. An average of 35
percent of the midday trips are for a meal or snack, 13 percent are
shopping trips, and 9 percent are for banking. This again shows the
opportunities which exist to shape travel behavior by locating
appropriate services within the center.
Midday Walking Trips
The NCHRP study also identified a rather direct relationship
between the proximity of the services to the office space and the
propensity of the workers to walk to their midday destinations. The
Galleria Mall in the Parkway Center showed a 17 percent walk share for
midday trips. The Galleria, containing 970,000 square feet, is
connected by enclosed walkways to approximately 1 million square feet
of office space and has a total of 2.1 million square feet of office
space within 2,000 feet of the mall. Bellevue Square Mall, also with
2.1 million square feet of office space within 2,000 feet, generates a
midday peak hour walk mode of 6 percent and
29
contains 1,066,300 square feet of retail space. Bellevue has a
pedestrian pathway system as well. Perimeter Mail in Perimeter Center
has 1,436,000 square feet, receives a 7 percent midday walk trade, and
has 2.8 million square feet of office space within 2,000 feet.
Residential Travel Characteristics
Various residential areas within the six NCHRP mixed-use centers
were surveyed to determine their travel characteristics. Residents
were asked specifically about the work location and the trips they
made within the center. Table 5.6 summarizes the findings.
The percentage of those living and working within the center ranges
from 13 to 50. It was determined that, on average, owner-occupied
households have "internal" workers more often (31 percent) than
renter-occupied units (28 percent). In addition, the larger the
center, the more likely it is that the residents will also work there.
Those classified as large, Tysons Corner and Parkway Center, had an
average of 33 percent of their residents employed within the centers,
while the smaller centers averaged 27 percent.
The denser centers of Bellevue and South Coast Metro exhibited a
higher walk mode share for trips internal to the center. Shorter walk
distances and Bellevue's pedestrian path system contribute to
increased walking trips. While these walking trips represent only a
very small proportion of the intra-site trips, perhaps if larger
residential components were studied and/or provided on-site, a
significant impact on travel patterns could be made.
Table 5.6: Intra-Center Trips Made by Residents
Click HERE for graphic.
Source: NCHRP, 1989.
30
Pleasanton Study
Pleasanton, California, enacted a traffic reduction ordinance
requiring employers to reduce peak hour trips by 45 percent. This
program has been in force for several years; Cervero and Griesenbeck
(1987) conducted a study of the travel patterns occurring as a result
of the regulations. The general conclusions drawn from the study are
as follows:
1. In 1986, 62 percent of those employed in Pleasanton were female.
2. Over 26 percent were classified as management/administration,
21.1 were clerical, 21.0 were service, and 17.6 were
professional/technical.
3. The share of professional employees commuting more than 15 miles
was much, higher than that of the non-professionals. This
suggests that the long average commuting distance of 15 miles is
more a function of higher-income workers choosing to live
farther away, rather than lower-income workers being pushed out
by rising housing prices.
4. Analysis of travel data showed that those most likely to
rideshare have long commuting distances, work for a large
company in a single-tenant site, and are in non-professional,
non-management positions.
5. People are more likely to "flex" their working hours if they
commute relatively long distances, work for a small firm in a
multi-tenant complex, and have a professional/management
position. This may reflect in part the difficulty of
implementing ridesharing for smaller firms, which leaves them
with flex-time as the other option for fulfilling the TSM
ordinance requirements.
6. Flex-time privileges discourage ridesharing. Most of
Pleasanton's trip reduction requirements have been achieved
through flex-time.
7. The most effective approach to demand management may be to
encourage staggered hours across firms so that ridesharing
within firms can be accomplished in concert with spreading the
trips over a longer time period.
31
6. New Jersey: Route 1 Corridor Region
While the purpose of this study is to further our understanding of
the relationship between suburban land use and transportation in
general, the laboratory we will be using to test our ideas is the
Route 1 Corridor region in central New Jersey. This region includes
Mercer County and southern portions of Middlesex and Somerset
Counties. To establish a foundation for the analytical portion of
this project, we will begin by assessing some of the attributes of the
Route 1 region which are pertinent to issues discussed throughout this
report. In addition, the efforts of the REGIONAL FORUM and the State
Planning Commission will be discussed in terms of their
recommendations for establishing mixed-use centers. It should be
understood, however, that this section will be somewhat cursory in
nature, with a substantial amount of data and analysis to be provided
in a subsequent phase of this project.
Economic and Demographic Characterics of the Route 1 Corridor
The Route 1 Corridor Region, comprised of 32 municipalities, had an
estimated population of 616,766 in 1987. Table 6.1 shows the change
in population by municipality since 1980. Growth has clearly been
taking place in the suburban and more rural municipalities like West
Windsor, Franklin, Plainsboro and South Brunswick, while older
localities and cities like Manville, Milltown, Trenton and New
Brunswick have been losing population. However, this losing trend is
expected to turn around by 2010, with every municipality in the region
experiencing some level of growth, albeit with the suburban areas
continuing to capture a greater share. The task is to determine how
much of this growth is already accounted for in existing development
proposals and how much can be shaped by our mixed-use center land use
approach.
Table 6.2 shows projected jobs/housing ratios for each
municipality. While the regional figure shows a nice balance of 1.56,
some municipalities have rather low ratios, indicating that their
resident labor force is commuting somewhere else to work. Without
current travel data, however, it is difficult to know the extent of a
spatial mismatch between jobs and housing within the region. The
jobs-housing factor is one important consideration when deciding upon
the potential future location for our prototype centers.
The State Department of Labor recently prepared an analysis of
labor demand versus supply in New Jersey through the end of the
century. Most of the labor force growth within the next decade will
be accounted for by women and minorities, with a declining overall
proportion of white males relative to the total. There may be a labor
shortage because of the baby-bust (a decline in the 16 to 24 age
cohort), skills mismatch and a lack of affordable housing.
Unemployment is expected to be 3.5 percent in 2000 if the economy
continues to grow as projected. Retraining efforts will be needed
because a major portion of the new jobs will be in the service sector,
requiring higher levels of education and skills to meet "high tech"
information-processing needs or to fill specialized positions such as
nursing and computer maintenance. Raising the retirement age may be
considered to keep older workers in the workforce longer. In
addition, if the affordable housing issue is not addressed, it will be
very difficult to attract workers from other areas (Department of
Labor, June 1989).
32
Table 6.1: Municipal Population Trends and Projections
Click HERE for graphic.
Sources: 1980 - US Census; 1987 - NJ Dept. of Labor; 2010 - Mercer
County Planning Board, Somerset County Planning Board,
Middlesex County Planning Board.
33
Table 6.2: Projected Jobs/Housing Ratios - 2010
Click HERE for graphic.
Source: MSM Regional Council, Mercer County Planning Board, Somerset
County Planning Board, Middlesex County Planning Board.
34
If we look at the specific labor market areas which include the
Route 1 Corridor region, it is apparent that the regional trends are
expected, in large part, to be the same as those predicted for the
entire state. In the Middlesex-Union labor area, approximately 77
percent of the new jobs projected through the year 2000 will be in the
non-production industries of wholesale trade, retail trade and
services. Of this portion, half of the jobs are expected to be in
business and health services. Similarly, in Mercer, 68 percent of the
new jobs are projected to be in trade and services, with legal,
business and health services as the leaders. Finally, in the
Somerset/Hunterdon labor area, the trend is the same, with 72 percent
of the new jobs in trade and services, particularly business and
health services (Department of Labor, Feb. 1989). A more thorough
look at the attributes of the region's employment structure may also
help us to understand how to approach the location of the future
mixed-use centers.
As mentioned above, these points will be expanded upon in a
subsequent analysis, but we can draw some preliminary implications.
As we saw in the NCHRP case studies, women are more likely to have the
responsibility for dropping a child at school or daycare and for doing
the household's shopping. Because a large portion of the labor force
growth will be women, childcare and shopping facilities should be
offered on-site in our centers of the future. In addition, while many
of the new jobs are high tech, many of the service jobs are lower-
paying positions, making affordable housing in or near the centers a
very important issue. Finally, if we are going to increasingly call
on the retirement-age workers to remain in the workforce, their needs
will have to be accommodated as well.
REGIONAL FORUM and State Plan Standards for Mixed-Use Centers
Two ongoing land use planning efforts in New Jersey are MSM's
REGIONAL FORUM and the State Planning Commission's State Development
and Redevelopment Plan. The REGIONAL FORUM was initiated in 1985 to
address growth management issues in what we have designated in this
report as the Route 1 Corridor region. Through an extensive
consensusbuilding effort, bringing together 250 individuals
representing various interests in the region, the REGIONAL FORUM
produced a growth management agenda for the Route 1 Corridor region.
The State Planning Commission was created by legislative action in
1986 with the mandate to establish a growth management plan for all of
New Jersey. The Commission is currently in the process of revising
the Preliminary State Development and Redevelopment Plan, an interim
document which will eventually be crafted into the Final State
Development and Redevelopment Plan. The Final Plan will present a set
of policies and guidelines for future land use throughout the State.
REGIONAL FORUM and State Planning efforts are being considered in
this report because they both advocate the establishment of mixed-use
centers as an alternative to the current patterns of suburban growth.
The Preliminary State Plan uses an approach called the Regional Design
System, which sets out standards for a hierarchy of centers ranging
from traditional central cities to rural hamlets. The REGIONAL FORUM
discussed a similar hierarchy of centers. The Preliminary State
Plan's "corridor center" and the FORUM's "regional center" criteria
are relevant to our work.
35
Some of the questions we have asked regarding the optimal design of
mixed-use suburban centers have been addressed by both the Preliminary
Plan and the REGIONAL FORUM. Table 6.3 presents suggested standards
for centers:
Table 6.3: Standards for Mixed-Use Centers
Regional Center Corridor Center
Acreage 400+ 640-6,400
Employment 9,000+ jobs 4,000-30,000jobs
Population 5,700+ 5,000-40,000
Dwelling Units 2,700+ 2,000-15,000
Jobs/Housing Ratio 3.5 2.0-5.0
Net DU's per Acre 8-11 4-20+
Nonresidential FAR 1.10 1-4+
Open Space 13% 20%-35%
Height Range 4-10 stores
Modal Split 85:15-60:40*
* Modal Split = % auto travel: % all other modes
Sources: "An Action Agenda for Managing Regional Growth," REGIONAL
FORUM, MSM Regional Council, 1987. "The Preliminary State Development
and Redevelopment Plan." Vol. III, New Jersey State, Planning
Commission, 1988.
Both the Preliminary State Plan and the REGIONAL FORUM recommend
that these centers be located proximate to the places on the
transportation infrastructure that are most appropriate for supporting
them, namely highway interchanges and transit stops. The Preliminary
Plan suggests that the best approach to siting these centers is
through the establishment of corridor plans focused on particular
highway and transit corridors. No recommendations have been made,
however, as to where specific corridor centers should be located. The
counties and municipalities have been given the responsibility for
determining appropriate locations.
As we have seen in our case studies, it is difficult to conclude
that merely providing a mix of uses and a relatively high density and
large size will achieve our transportation objectives. One of our
most successful case studies from a transportation perspective is also
one of the smallest -- Bellevue. Bellevue is 440 acres in size, with
a total of 7.7 million square feet of commercial space, and employment
of 19,030. Part of Bellevue's ability to achieve a greater than 25
percent non-SOV share is the relative intensity of the activities,
43.2 employees per acre compared with the next highest of 30.6 percent
in Tysons Corner with 37,650 employees and a non-SOV mode split of
only slightly greater than 10 percent. Bellevue also has a pedestrian
walkway system, a relatively good transit service, and some
corporations with aggressive parking management programs. In short,
both the REGIONAL FORUM and the State Plan guidelines may be
necessary, but not sufficient conditions for transportation success.
The REGIONAL FORUM has suggested generalized locations for possible
mixed-use centers throughout the Route 1 Corridor region. These
include:
36
- Proposed Monmouth Junction Station Area
- I-287/Franklin Twp.
- I-95/Mercer Airport
- NJ Turnpike Exit 7/I-95
- NJ Turnpike Exit 8/Hightstown
- NJ Turnpike Exit 8A/Forsgate
- I-95 Quakerbridge Area
Two other centers have been growing since 1980: the Princeton
Junction area including Carnegie Center and the Forrestal Center area.
These two areas are mixed-use in nature, but are not dense enough, nor
adequately integrated in design to achieve the transportation
objectives we hope to realize. These centers will be considered in
our location analysis, however, because there may be possibilities to
improve them as they continue to expand.
Figure 6.1 shows the location of the existing and prospective
centers throughout the Route 1 region. The locations of future
centers must be assessed not only in terms of their ability to absorb
growth, but also from the perspective of their locations relative to
other regional activities. If there is already a great deal of
pressure on the highways and train lines which would serve the
centers, there may be a resulting congestion problem when the centers
compete with through traffic for capacity. In addition, as the NCHRP
study showed, it is easier to capture intra-site trips if the center
is relatively isolated from other retail and service activities.
Within the past six months, there have been two proposals for
centers at the proposed Monmouth Junction train station and the I-95
Mercer Airport area. The former was brought forth by a development
firm and the latter effort is being carried out by the Mercer County
Division of Planning in conjunction with a variety of development
interests in that area. As mentioned above, both of these locations
were included in the REGIONAL FORUM recommendations.
The center proposed for the Mercer Airport area is included in a
plan for what has been designated the Mercer County I-95/295 Corridor
(Mercer County Planning Board, October, 1989). The Mercer County
Division of Planning is currently working with a team of consultants
to prepare this plan. The draft plan calls for:
square feet acres
Office/Research 5,463,874 505
Light Industrial 72,000 11
Retail 239,500 28
Hotel 160 rms 10
Residential 2,719 du's 1,712
37
Figure 6.1: Existing and Proposed Centers in the Route 1 Corridor
Region
Click HERE for graphic.
Source: "An Action Agenda for Managing Regional Growth," REGIONAL
FORUM, MSM Regional Council, 1997.
38
On most of the nonresidential parcels the FAR is .15 and the total
new employment estimated for this area is 19,328. Residential
densities per parcel vary from .5 to 8 dwelling units per acre, with a
total gross residential density of 1.6 units per acre for the entire
residential area. While the total employment and housing is within
the parameters put forth by the Preliminary State Plan and the
REGIONAL FORUM, the overall density of development is quite low and
the balance is off.
If this area were developed according to previous individual
proposals, there would eventually be 30,651 jobs and 1,687 dwelling
units with a jobs/housing ratio of 18.16. Under the draft corridor
plan, the jobs/housing ratio has been reduced to 7.11, obviously a
great improvement, but still over four times the 1.5 ratio recommended
in the literature. We cannot forget, however, that the county is
dealing with a large group of developers, some of whom have already
submitted plans for local approval based on existing zoning
conditions.
Should this corridor planning effort be successful in achieving its
proposed levels of development, the center will certainly represent a
laudable example of improved land use through collaboration and
compromise. In addition, the county is planning to apply for a Trans-
portation Development District designation for this area which would
help to assure that necessary transportation improvements will be made
to accommodate the growth, and transportation management programs will
be carried out.
To be sited adjacent to the future Monmouth Junction Train Station,
the Jersey Center Metroplex has been proposed (Rieder Land Technology,
1989). This development has generated quite a lot of controversy
because of its size, the height of the proposed buildings and density.
The target build-out year is 2002, at which point there would be 6.5
million square feet of office space under the proposed plan. This
translates into employment of over 20,000. With a total site area of
506 acres, there would be over 40 employees per acre, a level
approaching that of the Bellevue case study we examined. The retail
component of 180,000 square feet is relatively minor when compared
with the amount of office space. In addition, there are only 700
units of housing proposed, which would yield a jobs/housing ratio of
over 29.
In addition to the proposed height of 14 stories for the tallest
building, there are many questions about the underlying transportation
assumptions of this development. A shuttle bus is proposed to connect
the uses with each other and the train station, which, in absence of a
walking scale could be an acceptable alternative. However, the
developer has calculated that over 20 percent of the workers will
commute using transit. This assumes that reverse-flow commuting will
occur on the westbound Northeast Corridor Rail Line and that there is
adequate capacity for the rail system to handle additional eastbound
peak flow. In addition, the local road system is still left to handle
the trips of the remaining 16,000+ employees who don't travel by
transit. While the proposed size and density is at a level advocated
by the Preliminary State Plan and the REGIONAL FORUM, the
transportation issues and mix of uses need to be addressed more
adequately.
39
7. Proposed Center Prototype
Throughout this report, various relationships between land use and
transportation characteristics have been examined. While certain
factors such as increased size, density and mix of land uses have been
shown to favorably impact travel patterns, no clear standards or
minimum thresholds have emerged from the literature. On the other
hand, we know there are some basic design parameters like clustering
buildings within the center and providing approximately 1,000-foot
walking distances to effectively facilitate pedestrian and transit
travel. Furthermore, we also know the optimal components for
transportation management programs such as parking management and
custom rideshare matching programs.
We are now faced with making a leap to propose a prototype center
which can be tested in the Route 1 Corridor region. Given what we
have learned, the REGIONAL FORUM standards, with some additional
stipulations, seem to be reasonable minimum thresholds for designing
the prototype. These figures have the added advantage of having been
developed through a consensus-building process specific to the Route 1
Corridor region. The Preliminary State Plan standards might also be
appropriate, but the ranges given are quite wide; they have been pre-
pared for use in many types of areas throughout the state, and have
not yet been completely through the public scrutiny and amendment
process. Therefore, open for modification as our study proceeds, the
REGIONAL FORUM standards shall be our starting point:
Acreage 400+
Employment 9,000+ jobs
Population 5,700+
Housing Units 2,700+
Net DU's/Acre 8-11
Net Nonres. FAR 1.10
Jobs/Housing 3.5
Height Range 4-10 stories
In addition, the prototype should incorporate the following:
- Relatively intensive use of the nonresidential land, perhaps at
least 40 employees per acre
- Ample supply of retail and services, possibly a relationship of
.5 square feet of retail for every square foot of office
- A housing supply which accommodates all anticipated employee
income levels
- A phasing and marketing plan which would promote the opportunity
for people to both live and work within the center
- The inclusion of services such as childcare, grocery stores,
restaurants, health clubs, medical offices, movie theaters and
banks
40
- Location of the center so that it does not excessively compete
with through traffic for what would become an inadequate amount
of road capacity
- Location of the center in an area relatively remote from other
commercial developments
- A transportation management coordinator on-site who implements
parking management and programs appropriate for the demographics
of the workforce
- Possible parking supply restriction to 2 spaces per 1,000 square
feet of office space
- A design which clusters activities and provides a pathway system
to encourage pedestrian and transit trips
As the study proceeds and the actual sites are selected for testing
the effects of the regional mixed-use centers, there will certainly be
a variation in the application of the standards. Most likely, we will
attempt to make the centers as large and dense as political, economic
and physical constraints will allow. The final configuration of the
test centers will be determined through careful analysis, and review
and modification by local and national experts.
41
APPENDIX
NCHRP Trip Generation Rates
The following tables have been taken directly from the National
Cooperative Highway Research Program report "Travel Characteristics at
Large-Scale Suburban Activity Centers," prepared by JHK & Associates,
1989. These figures were collected through the administration of a
survey at each of the listed sites. This data is important because it
speaks to the question of whether or not the ITE trip generation rates
are applicable for large suburban mixed-use centers. Each entry in
the table is compared with the corresponding ITE rate. A summary of
this comparison is presented in Section 5-A.
42
Click HERE for graphic.
43
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44
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45
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46
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47
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48
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49
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50
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51
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52
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53
Click HERE for graphic.
54
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