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The Effects of Age on the Driving Habits of the Elderly

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The Effects of Age on the Driving Habits of the Elderly 

Evidence from the 1990 National Personal Transportation Study



Final Report 

October 1994



Prepared by 

Xuehao Chu Center for Urban 

Transportation Research

University of South Florida 

4202 East Fowler Avenue, ENB 118

Tampa, Florida 33670- 5350



Prepared for 

Office of University Research 

and Education Research

and Special Programs Administration 

U.S. Department of Transportation 

Washington, D.C. 20590



Distributed in Cooperation with 

Technology Sharing Program

Research and Special Programs 

Administration U.S. Department of

Transportation Washington, D.C. 20590



DOT-T-95-12





TABLE OF CONTENTS



Table of Contents List of Tables iv Acknowledgments v Abstract vi

Chapter 1 : Introduction 1 Background 1 Issues and Hypotheses 2

Previous Studies 3 Approach and Organization of the Report 5

Chapter 2 : The 1990 Nationwide Personal Transportation Survey 5

Survey 5 Variables 6 Chapter 3 : The Effects of Age on How Much

the Elderly Drive 8



Number of Daily Vehicle Miles 8 Number of Daily Vehicle Trips 12

Distance of Daily Vehicle Trips 15 Chapter 4 The Effects of Age

on When the Elderly Drive 18 Driving at Night 18 Driving During

Peak Hours 21 Chapter 5 The Effects of Age on How the Elderly

Drive 24 Speed 24 Limited-Access Highways 27 Automobile Size 30

Number of Passengers Carried 34 Chapter 6 : Summary and Policy

Implications 37 Summary 37 Policy Implications 38 Endnotes 40



iii



LIST OF TABLES



Table 2.1 Definition of variables 7 Table 3.1 Average number of

daily vehicle miles by driver age group 8 Table 3.2 Tobit

analysis of daily vehicle miles 10 Table 3.3 Average number of

daily vehicle trips by driver age group 12 Table 3.4 Tobit

analysis of number of daily vehicle trips 14 Table 3.5 Average

distance of daily vehicle trips by driver age group 15 Table 3.6

Weighted regression of distance of daily vehicle trips 17 Table

4.1 Percent of miles driven at night by driver age group 18 Table

4.2 Logit analysis of driving at night 20 Table 4.3 Percent of

miles driven during peak hours by driver age group 21 Table 4.4

Logit analysis of driving during peak hours 23 Table 5.1 Average

speed on all roads by driver age group 24 Table 5.2 Average speed

on limited-access highways by driver age group 25 Table 5.3

Weighted regression of speed of daily vehicle trips 26 Table 5.4

Percent of miles driven on limited-access highways by driver age

group 28 Table 5.5 Logit analysis of driving on limited-access

highways 29 Table 5.6 Average size of automobiles by age group of

main drivers 31 Table 5.7 Weighted regression of automobile size

33 Table 5.8 Average occupancy of automobile trips by driver age

group 34 Table 5.9 Weighted regression of occupancy of automobile

trips 35



iv



ABSTRACT



The Effects of Age on the Driving Habits of the Elderly: Evidence

from the 1990 NPTS



This report examines the effects of age on the driving habits of

the elderly, using the 1990 Nationwide Personal Transportation

Survey (NPTS). Elderly is defined as persons 65 years or older.

Six aspects are considered: the amount of daily driving exposure,

driving by time of day, driving speed, driving by type of

roadways, vehicle size, and the number of passengers carried. The

scope of analysis is limited to the content of the 1991 NPTS and

those aspects of driving habits that are hypothesized to have

safety implications for the elderly. The scale of analysis is

limited to urban residents. Regression is used to isolate the

effects of being elderly while holding constant a set of

personal, household, and location characteristics of the drivers,

as well as a set of trip characteristics. Elderly drivers show an

increased effort of self-protection in their driving habits

relative to mid-aged drivers (persons between the ages of 25 and

64 years). Being elderly not only makes elderly drivers reduce

daily driving exposure, avoid driving at night, avoid driving

during peak hours, and avoid driving on limited-access highways,

but also make them drive at lower speeds, drive larger

automobiles, and carry fewer passengers. Despite their effort of

self-protection, however, the elderly still show a higher risk of

crash and injury per unit of exposure than the mid-aged. If

policies induce the elderly to further adjust their driving

habits to offset the external risks of their driving, their risk

of crash and injury would be reduced and society as a whole would

be better off. The elderly, however, are likely to be worse off

as a consequence of reduced mobility.The challenge to

policy-making is to balance these consequences of any policy

concerning the mobility and traffic safety of the elderly.



vi



ACKNOWLEDGMENTS



This project is made possible through a grant from the U.S.

Department of Transportation, University Research Institute

Program. Their support is gratefully acknowledged. Comments from

the following individuals are gratefully acknowledged: William L.

Ball, Michael R.Baltes, Patricia Henderson, Rosemary Mathias,

Steve Polzin, and Joel R. Rey.



v



Chapter 1 INTRODUCTION



The mobility and traffic safety of elderly drivers are of great

concern to the public.1 Much of this concern is due to the fast

growth in the number of elderly drivers and their driving. This

report examines the effects of age on the driving habits of the

elderly in the United States, as revealed in the 1990 Nationwide

Personal Transportation Survey (NPTS).2 Six aspects of driving

habits are considered that are hypothesized to have safety

implications for the elderly. A good understanding of the driving

habits of the elderly is essential not only to the provision of

public transportation to the elderly but also to the design of

policies that address the mobility and traffic safety of the

elderly.



BACKGROUND



Between 1985 and 1989, three national conferences were held to

discuss issues on the mobility and traffic safety of elderly

drivers.3 Initiated in 1986 by the Transportation Research Board

(TRB), the U.S. Congress requested in the Surface Transportation

Assistance Act of 1987 "a comprehensive study and investigation

of (1) problems which may inhibit the safety and mobility of

elderly drivers using the Nation's roads and (2) means of

addressing these problems.4 In 1987, Congress asked the U.S.

Department of Transportation to implement a pilot program of

highway safety improvements to enhance the mobility and traffic

safety of elderly drivers.5 In addition, elderly drivers

frequently make headlines in major magazines and newspapers

across the nation.6 The number of elderly drivers grew from 8.6

million in 1970 to 22.3 million In 1990, an increase of 148

percent, while the number of all drivers grew by 50 percent

during the same period. The number of elderly drivers as a

proportion of all drivers also increased from 8.0 percent in 1970

to 13.3 percent in 1990. 7 These increases reflect the growth in

the elderly population as well as in its licensure rate. The

elderly population grew from 20.0 million in 1970 to 31.1 million

in 1990, an increase of 56 percent, while the population of age

15 years or older grew by 34 percent during the same period.8 The

licensure rate of the elderly population increased from 45

percent in 1970 to 72 percent in 1990, while the licensure rate

of the population of age 15 years or older increased from 77

percent in 1970 to 86 percent in 1990. 9 The number of miles

driven by the elderly has grown more than the elderly population

and its licensure rate. The elderly drove 42.2 billion miles in

1969 and 153.7 billion miles in 1990, an increase of 264 percent.

The rate of growth for all drivers was 142 percent. The share of

miles driven by the elderly increased from 4.9 percent in 1969 to

7.1 percent in 1990.10 These trends are expected to continue. By

the year 2020, the elderly population is expected to reach 20

percent of all persons. The number of elderly drivers is likely

to exceed 20 percent of all drivers.11



1



ISSUES AND HYPOTHESES



This report considers six aspects of driving habits. These

aspects include the amount of daily driving exposure, driving by

time of day, driving speed, driving by type of roadways, vehicle

size, and the number of passengers carried. The scope of analysis

is limited to the content of the 1990 NPTS and to those aspects

of driving habits that are hypothesized to have safety

implications for the elderly. The scale of analysis is limited to

urban residents. In addition to age, other personal, household,

and location characteristics of the elderly also may influence

their drivingmhabits. Personal characteristics include

educational attainment and labor force participation. Household

characteristics include race, annual income, composition (size,

children), and vehicle ownership. Location characteristics

include the household location in an urban area (central city vs.

suburbs), the household location in the nation (the West vs.

other regions), the size of an urban area, and the population

density of an urban area. Many of these characteristics may

differ systematically between the elderly and others. Labor force

participation changes with aging. Household income may decline

with retirement from the labor force. Household composition may

change with aging. For example, the elderly are less likely to

live with young children than are younger persons. Vehicle

ownership may change with aging due to changes in household

composition and income. Household location may change with aging.

For example, the elderly may be more likely to live in the

suburbs and in the South. The elderly have more time available

for travel during the day. The elderly also may differ from

others in their activity patterns. The elderly may choose to

participate in activities that occur less frequently (e.g., once

a month instead of once a week). They may choose to participate

in activities that are closer to their homes. Or they may move

closer to activities in which they choose to participate. They

also may choose to participate in activities that occur during

the day or off-peak hours. However, the literature provides no

evidence of these hypothetical changes in the activity patterns

of the elderly. It is important to control for the

characteristics that differ systematically between the elderly

and others in order to isolate the effects of age on the driving

habits of the elderly. It is also important to control for these

characteristics in order to draw conclusions about the driving

habits of the future's elderly from the driving habits of today's

elderly because many of these characteristics may change in the

future for the elderly. For example, the future's elderly may

have higher vehicle ownership than today's elderly. The future's

elderly also may be more likely to live in the suburbs than

today's elderly. The elderly differ from others in two other

important characteristics that have not been discussed. First,

the majority of the elderly are not employed and will remain

unemployed for the rest of their lives. The elderly, therefore,

would lose less than younger persons in future labor earnings

from an injury. According to the foregone-labor-earnings approach

to measuring motor vehicle crash costs, elderly drivers are

likely to have lower costs of injuries than younger drivers.12



2



Second, cognitive and physical abilities generally decline with

aging.13 One consequence of this decline is that the driving

skills of the elderly are reduced. As a result, elderly drivers

are more likely to be involved in crashes than all drivers,

except those under the age of 25 years. 14 In the majority of

crashes in which elderly drivers were involved, they were at

fault for failing to yield the right-of-way, turning improperly,

ignoring traffic signals, or starting improperly into traffic.15

Another consequence of the decline in their physical abilities is

that the elderly are more likely to be injured than younger

persons in a crash. These two important characteristics of the

elderly may have two opposite effects on their driving habits. On

the one hand, elderly drivers may be more willing than younger

drivers to take risks because of their reduced costs of injuries.

On the other hand, elderly drivers may compensate for their

increased crash and injury risks. This behavior of risk

compensation can manifest itself in many ways. The elderly may

drive fewer miles to reduce exposure. They may feel less

comfortable with carrying passengers. They may find certain

driving conditions difficult, such as driving at night, during

peak hours, at high speeds, or on limited-access highways. They

also may feel vulnerable to the low crashworthiness of small

vehicles. While this study controls for many of the personal,

household, and location characteristics of the elderly discussed

earlier, it does not, however, control for the two important

characteristics just discussed. It is hypothesized that the

relative strengths of these two characteristics determine the

effects of age on the driving habits of the elderly.



PREVIOUS STUDIES



No known previous study exists that looks at the size of vehicles

that the elderly drive or the number of passengers they carry.

Previous studies on the amount of driving exposure, driving

speed, driving by time-of-day, and driving on limited-access

highways by the elderly have one drawback: they often fail to

control simultaneously for many factors that may influence the

driving habits of the elderly. This drawback has two

implications. On the one hand, any observed difference in the

driving habits between the elderly and others may be a mix of the

differences in age and other personal, household, and location

characteristics of the drivers that are not controlled for in

these studies. On the other hand, any difference observed in the

driving habits of today's elderly and others is unlikely to hold

true in the future because those personal, household, and

location characteristics of the drivers that are not controlled

for may change in the future. The evidence from previous studies

is mixed. Studies have found "no evidence that elderly drivers

who exhibit poor performance on driving simulators make any

compensating adjustment in the amount of driving exposure."16 One

reason given is that elderly drivers are unaware of the changes

in their cognitive and physical abilities and those driving

conditions that become more difficult as age advances. The other

reason given is that elderly drivers are unwilling to admit lack

of driving competence or to significantly reduce exposure.

Several U.S. studies, however, find that elderly drivers reduce

exposure more as they age and tend to avoid



3



high-risk conditions, such as driving at night and during peak

hours.17 A Canadian study concludes that "increased driver risk

due to medical conditions among elderly drivers was more than

offset by their adoption of new, less risky driving patterns."18



APPROACH AND ORGANIZATION OF THE REPORT



This study uses regression analysis to isolate the effects of age

on the driving habits of the elderly. Regression analysis

accomplishes this isolation by including variables measuring the

age as well as a set of other personal, household, and location

characteristics of the drivers. It is important to control for

factors that aging may affect. It is also important to control

for factors that aging does not affect, such as gender and race.

Under this regression framework, this study attempts to determine

whether or not age affects the driving habits of the elderly and,

if so, what the size and nature of the effects are. This report

is organized into six chapters. Chapter 1 is this introduction.

Chapter 2 describes the 1990 NPTS and the variables that are used

in this study. Chapter 3 examines the effects of age on how much

the elderly drive. The aspects examined include the number of

daily vehicle miles driven by individual drivers, the number of

daily vehicle trips taken by individual drivers, and the distance

of individual vehicle trips. Chapter 4 examines the effects of

age on when the elderly drive. The aspects examined include

driving at night and during peak hours. Chapter 5 examines the

effects of age on how the elderly drive. The aspects examined

include driving speed, driving on limited-access highways,

vehicle size, and the number of passengers carried. Chapter 6

summarizes the main results and discusses policy implications of

these results.



4



Chapter 2 THE 1990 NATIONWIDE PERSONAL TRANSPORTATION SURVEY

(NPTS)



This chapter describes the 1990 NPTS and defines the variables

that are used in this study. The 1990 NPTS compiles data on a

cross-section of personal travel in the United States for all

purposes and surface modes of transportation in 1990-1991.



SURVEY



The 1990 NPTS was conducted between March 1990 and March 1991

using random-digit dialing and computer-assisted telephone

interviewing. The sample was stratified by geography, quarter-of-

year,month-of-quarter, and day-of-week. A total of 73,579

telephone numbers was randomly selected to identify 26,172

households. Each of the identified households was contacted for

an interview. A total of 21,869 households participated. Each of

the participating households was assigned a 24-hour "travel day"

and a 14-day "travel period." For each participating household, a

household-level interview was conducted with an adult resident of

the household. This interview obtained information on the number

of household vehicles, household location, and household income.

In addition, a roster containing person data for each resident of

the household was completed.



A person-level interview was attempted for each resident of the

participating households who was five years or older. The

person-level interview was completed for 47,499 household

residents. Each resident older than 13 years was asked to report

all trips they had taken during the travel day, as well as trips

of 75 miles or longer taken during the travel period. A

"knowledgeable" household resident, older than 13 years, was

asked to report all trips taken by household residents between

the ages of 5 and 13 years. The 1990 NPTS data for this study are

contained in four files in the Statistical Analysis System (SAS)

format. The four files are the Household File, Person File,

Vehicle File, and Travel Day File. The Household File contains

household characteristics for 22,317 observations. The

information collected includes household race, household income,

household size, and household location, such as census region,

the location in an urbanized area, the size of an urbanized area,

and the population density of a zip-code area. Also included are

the sunrise and sunset times associated with the travel day. The

Person File contains the person-level attributes for 48,385

esidents of the participating households. The information

collected includes the age, educational attainment, driver's

license status, and labor force participation of each household

resident. Participating in the labor force means being employed

or actively looking for employment. The Person File also ontains

the number of vehicle miles and the number of vehicle trips taken

by each resident on the travel day.



5



The Vehicle File contains the attributes for 41,178 vehicles in

the participating households. The information collected includes

the model year, make, model, and main driver of each vehicle. The

Travel Day File contains the attributes of 149,546 trips taken by

residents of the participating households on the travel day. The

information collected includes the purpose, mode, occupancy,

length (both duration and distance), time-of-day, day-of-week,

and month-of-year of each trip. The survey also randomly selected

a private-vehicle trip for each resident of the participating

households (if any) to collect information on the various types

of roadways that were used on this trip. A total of 31,015 such

trips was sampled. The distance for each of these trips was

broken down by roadway classification. Weights were developed in

the 1990 NPTS to reflect the sample design and selection

probabilities, and survey non-response or non- coverage. The

Household and Vehicle Files have the same weight variable. The

Person and Travel Day Files have separate weight variables. A

weight variable was also developed for the randomly selected

private-vehicle trips.



VARIABLES The variables used in this study are defined in Table

2.1. They are organized into five groups: personal, household,

location, trip, and vehicle characteristics.



6



Click HERE for graphic.
Table 2.1 Definition of variables



7



Chapter 3 THE EFFECTS OF AGE ON HOW MUCH THE ELDERLY DRIVE



This chapter examines the effects of age on the amount of driving

exposure by the elderly. Three measures of driving exposure are

considered. These measures are the number of vehicle miles driven

by individual drivers on the travel day, the number of vehicle

trips taken by individual drivers on the travel day, and the

distance of individual vehicle trips on the travel day. Each of

these measures is first tabulated by driver age group and labor

force participation. Regression analysis is then used to isolate

the effects of age on each of these measures.



NUMBER OF DAILY VEHICLE MILES DRIVEN



TABULATION



Table 3.1 tabulates the average number of vehicle miles driven on

the travel day by driver age group and labor force participation.

On average, elderly persons in the labor force drive about l9

miles a day and those not in the labor force drive about 10 miles

a day. In comparison, mid-aged persons in the labor force drive

about 29 miles a day, and those not in the labor force drive

about 16 miles a day; and young persons in the labor force drive

about 27 miles a day, and those not in the labor force drive

about 3 miles a day.



Click HERE for graphic. 

Table 3. 1 Average number of daily vehicle

miles driven by



Source: Computed from the Person File as the weighted average of

total vehicle miles driven by each responding driver on the

travel day.



REGRESSION Regression analysis is used to isolate the effects of

age on the number of vehicle miles driven by individual elderly

drivers on the travel day. Regression analysis isolates these

effects by including age and other personal, household, and

location characteristics of the elderly drivers as control

variables. The number of vehicle miles driven by individual

drivers is the dependent variable. The age and other

characteristics of individual drivers are the explanatory

variables.



8



Model The first candidate model for this regression analysis

would be the standard linear regression model. This model can be

defined as follows:



Click HERE for graphic.



where yi is the dependent variable; i indicates an observation in

the data; b is a column vector of unknown parameters; xi is a

column vector of known values of the explanatory variables for

observation i; and u, is a disturbance term for observation, that

is independently and normally distributed across observations

with a zero mean and common variance. If the assumptions of this

model are not met, parameters estimated from the ordinary least

squares method may not have properties such as consistency or

efficiency. The current problem violates the assumption that the

disturbance term has a zero mean. About 40 percent of the

responding drivers reported no vehicle miles driven on the travel

day. This situation fits the Tobit model, which originally was

formulated to analyze survey data of consumer expenditures on

durable goods. Most households report zero expenditures on major

durable goods during any year. Among those households that report

any such expenditures, however, the amounts vary widely. The

Tobit model can be defined as follows:



Click HERE for graphic. 



otherwise



where RHS refers to the right hand side and the other symbols are

as defined in the standard linear regression model in equation

(1). The ordinary least squares method in this situation leads to

inconsistent estimates of the unknown parameters. Consistent

estimates in the Tobit model can be obtained with the maximum

likelihood or Heckman two-stage method. The Heckman method is

easier to compute, but less efficient.1 Therefore, the maximum

likelihood method is used for this analysis.2



Results Many factors could affect the number of vehicle miles

driven on a given day by individual drivers. These factors

include the characteristics associated with the drivers as well

as the cost of driving. While the 1990 NPTS contains a set of

personal, household, and location characteristics of the drivers,

it does not, however, include information on the cost of driving.

As a result, the cost of driving is approximated by the statewide

average refiner/reseller sales price of motor gasoline plus state

gasoline tax in 1990.3 This cost of driving ignores any variation

in the refiner/reseller sales price of motor gasoline within a

state and in non-state local gasoline taxes. This cost of driving

also ignores other components of driving costs. This cost of

driving, in cents per gallon, will be referred to as gasoline

price. The results are shown in Table 3.2. The first column lists

the explanatory variables by category. The second column lists

the estimated coefficients, measuring the marginal effects



9



Click HERE for graphic. 

Table 3.2 Tobit analysis of daily vehicle

miles driven



Source: Estimated from the Person File using the maximum

likelihood method with the SAS LIFEREG procedure. The dependent

variable is total number of vehicle miles driven on the travel

day by each responding driver. Whether a coefficient differs from

zero is labeled as follows: n significant at the 5 percent level

; insignificant at the 10 percent level ; others significant at

the 1 percent level.



10



of an explanatory variable on the dependent variable while

holding constant other explanatory variables. The last column

lists the corresponding chi- square (X2) statistics, indicating

the statistical significance of the explanatory variables. At the

bottom are the log likelihood at convergence, the number of

observations used in the estimation, and the proportion of

observations with zero miles. 4



Two issues are involved in the interpretation of the results.

First, the sign of a coefficient in a Tobit model measures the

direction of changes in the dependent variable from a change in

the corresponding explanatory variable. But to compute the

magnitude of these changes in the dependent variable is not

straightforward. The interpretation here focuses on the signs. 5

The second issue involved in the interpretation of the results

concerns dummy variables. Since the model includes a constant

term, the dummy variable coefficients are interpreted relative to

the omitted category. For example, the dummy variable for male

drivers is included, but the dummy variable for female drivers is

omitted. The omitted category becomes a benchmark. The dummy

variable coefficients for the remaining categories tell whether

or not each of the remaining categories differ from this

benchmark and, if so, by how much. There are two types of dummy

variables: those involving two categories and those involving

more than two categories. The two-category dummy variables

include gender, educational attainment, labor force

participation, Hispanic status, single status, location in an

urbanized area, month-of-year, and day-of-week. The

multi-category dummy variables include age, race, and census

region. The omitted category for age includes those persons

between the ages of 25 and 64 years; the remaining categories

include those persons age 24 years or younger and those persons

age 65 years or older. The omitted category for race includes

those persons who are neither White nor Black; the remaining

categories include White persons and Black persons. The omitted

category for census region is the West; the remaining categories

include the North East, North Central, and South regions. The

results indicate that the coefficient of the elderly dummy

variable is -3.8392 and differs from zero at the 5 percent level.

Thus, other things being equal, the elderly drive fewer miles

than the mid-aged. The other variables are organized into two

groups for interpretation. The first group includes those

variables whose coefficients differ from zero at up to the 10

percent level. The results indicate that, other things being

equal, persons in the labor force drive more miles than those not

in the labor force; males drive more miles than females; Whites

drive more miles than drivers who are neither White nor Black;

Blacks drive fewer miles than Whites; persons with higher

household incomes drive more miles; and persons from households

with more children under five years old drive more miles. In

addition, the young drive fewer miles than the midaged; persons

from households without vehicles drive fewer miles than those

with vehicles; persons living in areas with higher population

densities drive fewer miles; persons living in central cities

drive fewer miles than those living outside central cities; and

the number of daily vehicle miles driven by individual persons

decreases with an increase in gasoline price.



11



The second group includes those variables whose coefficients do

not differ from zero at the 1 0 percent level. The results

indicate that, other things being equal, Blacks drive the same

number of daily vehicle miles as those who are neither White nor

Black; Hispanics drive the same number of daily vehicle miles as

non-Hispanics; the size of an urbanized area does not affect the

number of daily vehicle miles driven by individual persons; and

census region does not make a difference in the number of daily

vehicle miles driven by individual persons.



NUMBER OF DAILY VEHICLE TRIPS



The number of vehicle miles driven combines the number and

distance of vehicle trips. The previous section has shown that

the elderly drive fewer miles than the mid-aged. Does this result

imply that the elderly take shorter trips as well as make fewer

vehicle trips than the midaged? The literature provides mixed

evidence.6 The number of vehicle trips taken on the travel day by

individual drivers and the distance of individual vehicle trips

are examined separately using both tabulation and regression

analysis.



TABULATION Table 3.3 tabulates the average number of vehicle

trips taken on the travel day by driver age group and labor force

participation. On average, elderly persons in the labor force

drive 2.56 vehicle trips per day and those not in the labor force

drive 1.64 vehicle trips per day. Midaged persons in the labor

force drive 2.99 vehicle trips per day and those not in the labor

force drive 2.22 vehicle trips per day. Young persons in the

labor force drive 2.92 vehicle trips per day and those not in the

labor force drive 0.35 vehicle trips per day.



Click HERE for graphic.

Table 3.3 Average number of daily vehicle

trips by driver age group



Source: Calculated from the Person File as the weighted average

of the number of vehicle trips driven by each responding driver

on the travel day.



REGRESSION This regression analysis is similar to that for the

number of vehicle miles driven by individual persons in the

previous section. The unit of observation is individual drivers.

The



12



same set of explanatory variables are used. As mentioned in the

previous section, about 40 percent of the responding drivers

reported no vehicle miles on the travel day. Thus, the Tobit

model in equation (2) is used along with the maximum likelihood

method for estimation. The results are shown in Table 3.4. The

results indicate that the coefficient of the elderly dummy

variable does not differ from zero at the 10 percent level. Thus,

other things being equal, the elderly drive just the same number

of vehicle trips per day as the mid-aged. The other explanatory

variables are organized into three groups for interpretation. The

first group includes those variables whose coefficients differ

from zero at up to the 10 percent level. The results indicate

that, other things being equal, persons in the labor force drive

more vehicle trips than those not in the labor force; persons

with more than a high school education drive more vehicle trips

than those with less education; Whites drive more vehicle trips

than those who are neither White not Black; Blacks drive fewer

vehicle trips than Whites; persons living with children under

five years old drive more vehicle trips than those not living

with children under five years old; and persons from

single-resident households drive more vehicle trips than those

from multi-resident households. In addition, the young drive

fewer vehicle trips than the mid-aged; persons from households

without vehicles drive fewer vehicle trips than those from

households with vehicles; people drive fewer vehicle trips on

weekend days than on weekdays; the number of daily vehicle trips

taken by individual drivers decreases with an increase in the

number of adults in a household; the number of daily vehicle

trips taken by individual drivers decreases with an increase in

the population density of a zip-code area; and the number of

daily vehicle trips taken by individual drivers decreases with an

increase in the size of an urbanized area. The second group

includes those variables whose statistical significance changes

in explaining the number of vehicle miles driven and vehicle

trips taken by individual drivers on the travel day. The results

in Tables 3.2 and 3.4 indicate that, other things being equal,

males drive more miles than females, but not more vehicle trips;

household income affects the number of miles driven, but not the

number of vehicle trips; gasoline price affects the number of

miles driven, but not the number of vehicle trips; and living in

central cities affects the number of miles driven, but not the

number of vehicle trips taken. In addition, the size of an

urbanized area has no effect on the number of miles driven, but

affects the number of vehicle trips taken by individual drivers.

The third group includes those variables whose coefficients that

do not differ from zero at the 10 percent level in explaining

both the number of vehicle miles driven and the number of vehicle

trips taken by individual drivers on the travel day. The results

in Tables 3.2 and 3.4 indicate that, other things being equal,

Blacks drive the same number of miles and take the same number of

vehicle trips as those who are neither White nor Black; Hispanics

drive the same number of miles and take the same number of

vehicle trips as non-Hispanics; and census region does not make a

difference in explaining the number of miles driven or the number

of vehicle trips taken by individual drivers.



13



Click HERE for graphic

 Table 3.4 Tobit analysis of number of daily

vehicle trips



Source: Estimated from the Person File using the maximum

likelihood method with the SAS LIFEREG procedure. Whether a

coefficient differs from zero is marked as follows: n significant

at the 5 percent level; insignificant at the 10 percent level:

others significant at the 1 percent level.



14



DISTANCE OF DAILY VEHICLE TRIPS



TABULATION Table 3.5 tabulates the average distance of vehicle

trips taken on the travel day by driver age group and trip

purpose. For elderly drivers, the average distances are 6.55

miles for all trips, 8.30 miles for work trips, and 6.43 miles

for non-work trips. For mid-aged drivers, the average distances

are 9.25 miles for all trips, 11.54 miles for work trips, and

8.22 miles for nonwork trips. For young drivers, the average

distances are 8.91 miles for all trips, 9.98 miles for work

trips, and 8.54 miles for non-work trips. For all drivers, the

average distances are 8.98 miles for all trips, 11.23 miles for

work trips, and 8.10 miles for non-work trips.



Click HERE for graphic.

Table 3.5 Average distance of daily vehicle

trips by driver age group



Source: Calculated from the Travel Day File as the weighted

average of distances of individual vehicle trips on the travel

day in miles.



REGRESSION As with the models developed for the number of vehicle

miles driven and the number of vehicle trips taken by individual

drivers on the travel day, the purpose of this regression

analysis is to isolate the effects of age on the distance of

individual vehicle trips taken by elderly drivers on the travel

day.



Model The regression analysis in this section differs from those

in the previous sections in two important aspects. First, while a

large proportion of responding drivers reported no vehicle trips

on the travel day, the variable measuring the distance of vehicle

trips does not have this problem. Instead of the Tobit model in

(2), the standard linear regression model in (1) is used along

with the weighted least squares method for estimation. Second,

while the unit of observation in the previous sections is

individual drivers, the unit of observation in this section is

individual vehicle trips. As a result, an additional set of

explanatory variables measuring trip characteristics is also

included. These additional variables include time-of- day,

whether the driver carried any passengers, day-of-week,

month-of-year, and the purpose of a vehicle trip.



15



Results The results are shown in Table 3.6. The interpretation of

the standard linear model is straightforward. The coefficient of

an explanatory variable measures the expected change in the value

of the dependent variable from one unit change in the explanatory

variable, while holding other explanatory variables constant.

Another issue of interpretation is the set of dummy variables

that measures trip purposes. The 1990 NPTS classifies trip

purposes into ten categories. Four of these categories are

omitted from the model: trips for school or church, trips for

vacation, trips for pleasure driving, and trips for other

purposes. The remaining six categories are included in the model.

As a result, the coefficients of the dummy variables for these

remaining categories are interpreted relative to the omitted

categories. The results indicate that the coefficient of the

elderly dummy variable is -1.0471 and differs from zero at the

0.01 percent level. Thus, other things being equal, the elderly

drive about one mile shorter per trip than the mid-aged. The

other variables are organized into two groups for interpretation.

The first group includes those variables whose coefficients

differ from zero at up to the 10 percent level. The results

indicate that, other things being equal, male drivers take longer

trips than female drivers; drivers in the labor force take longer

trips than those not in the labor force; White drivers take

longer trips than those who are neither White nor Black; Blacks

take trips of shorter distances than those taken by Whites;

drivers with higher household incomes take longer trips; and

drivers living in larger urbanized areas take longer trips. In

addition, drivers living in central cities take shorter trips

than those living outside central cities; the distance of vehicle

trips decreases with an increase in gasoline price; drivers

living in areas with higher population densities take shorter

trips; trips for work- related purposes and for visiting friends

or relatives are longer than trips for those purposes that are

omitted from the model; and trips for other remaining purposes

are shorter than trips for those purposes that are omitted from

the model. The second group includes those variables whose

coefficients do not differ from zero at the 1 0 percent level.

The results indicate that, other things being equal, young

drivers take trips that are just as long as those taken by

mid-aged drivers; winter trips are just as long as nonwinter

trips; night trips are just as long as day trips; peak trips are

just as long as off-peak trips; Black drivers take trips that are

just as long as those taken by drivers who are neither White nor

Black; Hispanic drivers take trips that are just as long as those

taken by non-Hispanic drivers; and drivers in the North East or

South regions take trips that are just as long as trips taken by

those in the West.



16



Click HERE for graphic. 
Table 3.6 Weighted regression of distance of

daily vehicle trips



Source:Estimated by Author from the Travel Day File using the

weighted least squares method. Whether a coefficient differs from

zero is labeled as follows: n significant at the 5 percent level,

u significant at the 10 percent level; insignificant at the 10

percent level: others significant at the 0.01 percent level.



17



Chapter 4 THE EFFECTS OF AGE ON WHEN THE ELDERLY DRIVE



This chapter examines the effects of age on driving at night or

during peak hours by the elderly. Night includes the hours after

sunset and before sunrise. Peak hours include 6:30-9:00 a.m. and

3:30-6:00 p.m. Whether a vehicle trip was taken at night or

during peak hours is determined by its start time. Driving at

night is examined first, followed by an examination of driving

during peak hours. For each analysis, the percent of vehicle

miles driven by time of day is first tabulated by driver age

group and trip purpose. Logit analysis is then used to isolate

the effects of age on the elderly's probability of driving at

night or during peak hours.



DRIVING AT NIGHT



TABULATION Table 4.1 tabulates the percent of vehicle miles

driven at night by driver age group and trip purpose. The elderly

drive about 18 percent of their miles at night for both work and

nonwork trips, while the mid-aged drive about 29 percent of their

miles at night for work trips and 23 percent for non-work trips.

The young drive about 29 percent of their miles at night for work

trips and 25 percent for non-work trips.



Click HERE for graphic. 
Table 4. 1 Percent of miles driven at night

by driver age group



Source: Calculated from the Travel Day File. Each number

represents total miles driven by drivers of a given group at

night as a percentage of total miles driven by these drivers all

day.



REGRESSION The purpose of this regression analysis is to isolate

the effects of age on driving at night by the elderly, while

holding constant a set of the elderly's personal, household, and

location characteristics.



18



Model Similar to the regression analysis of the distance of

vehicle trips in the previous section, the unit of observation is

individual vehicle trips. This regression analysis, however,

differs from that for the distance of vehicle trips in four

aspects. First, the dependent variable here is binary, indicating

whether a vehicle trip on the travel day started at night. One

commonly used regression model for a binary choice problem is the

logit model, in which the probability of choosing to drive at

night has the logit form. If P is the probability of driving at

night, x is a column vector of the values of explanatory

variables, and b is a column vector of parameters, then:



Click HERE for graphic. 
e b1x (3) P = 1 = e b1x



Second, speed may differ systematically by time of day. In

addition to a similar set of explanatory variables used in the

model for the distance of vehicle trips, speed is also included

in this analysis. Third, the ordinary least squares method does

not apply here. Instead, the maximum likelihood method is used

for estimation. Fourth, several variables are excluded because

convergence could not be reached when these variables are

included. These excluded variables are Black, Hispanic, and the

census regions. The reason that these particular variables are

chosen to be excluded is that they are thought to be less

important than others in the decision of driving by time of day.



Results The results are shown in Table 4.2. The coefficients in

this model are interpreted differently from those in a standard

linear or Tobit model. First, an increase in a variable with a

negative coefficient decreases the odds ratio of driving at

night. The odds ratio of driving at night is PI(1-P), where P is

the probability of driving at night. Second, the exponential

value of the coefficient of an explanatory variable determines

the percent change in the odds ratio of driving at night from one

unit change in that explanatory variable. For example, the dummy

variable for male drivers has a coefficient of 0.3070. Its effect

on the odds ratio of driving at night is 100*(e 0.3070 - 1) =36

percent. That is, males' odds ratio of driving at night is 36

percent higher than females' odds ratio of driving at night. The

results indicate that the coefficient of the elderly dummy

variable is -0.2183 and differs from zero at the 0.01 percent

level. Thus, other things being equal, the elderly are less

likely to drive at night than the mid-aged. In fact, the

elderly's odds ratio of driving at night is 20 percent lower than

the mid- aged's odds ratio of driving at night. The other

variables are organized into two groups for interpretation. The

first group has positive coefficients. The results indicate that,

other things being equal, the young are more likely to drive at

night than the mid-aged; males are more likely to drive at night

than females; persons in the labor force are more likely to drive

at night than those not in the labor force;



19



Click HERE for graphic. 
Table 4.2 Logit analysis of driving at

night



Source: Estimated from the Travel Day File using the maximum

likelihood method with the SAS LOGISTIC procedure. Whether a

coefficient differs from zero is marked as follows: n significant

at the 1 percent level; others significant at the 0.01 percent

level.



20



persons living in central cities are more likely to drive at

night than those living outside central cities; the probability

of driving at night increases with an increase in household

income, the size of an urbanized area, and the population density

of a zip-code area; and trips for work-related purposes, visiting

friends or relatives, and other social or recreational purposes

are more likely to be taken at night than trips for those

purposes that are omitted from the model. The second group has

negative coefficients. The results indicate that, other things

being equal, persons with more than a high school education are

less likely to drive at night than those with less education;

Whites are less likely to drive at night than non-Whites; and

trips for shopping, other family or personal business, and

medical purposes are less likely to be taken at night than trips

for those purposes that are omitted from the model. Note that the

omitted category for race in this analysis is non- Whites.



DRIVING DURING PEAK HOURS



TABULATION Table 4.3 tabulates the percent of vehicle miles

driven during peak hours by driver age group and trip purpose.

The elderly drive about 28 percent of their miles during peak

hours for non-work trips, 57 percent for work trips, and 30

percent for all trips. The mid-aged drive about 31 percent of

their miles during peak hours for non-work trips, 59 percent for

work trips, and 39 percent for all trips. The young drive about

38 percent of their miles during peak hours for nonwork trips, 50

percent for work trips, and 40 percent for all trips.



Click HERE for graphic. 
Table 4.3 Percent of miles driven during

peak hours by driver age group



Source: Calculated from the Travel Day File. Each number

represents total miles driven by drivers of a given group during

peak hours as a percentage of total miles driven by these drivers

all day.



REGRESSION The regression analysis of driving during peak hours

is similar to that for driving at night. Again, the dependent

variable is binary, indicating whether a vehicle trip on the

travel day started during peak hours. The same set of explanatory

variables are included as in the regression



21



analysis for driving at night. The logit model is used along with

the maximum likelihood method for estimation. The results are

shown in Table 4.4. The results indicate that the coefficient of

the elderly dummy variable is -0.1251 and differs from zero at

the 1 percent level. Thus, other things being equal, the elderly

are less likely to drive during peak hours than the mid-aged. In

fact, the elderly's odds ratio of driving during peak hours is

about 12 percent lower than the odds ratio of driving during peak

hours by the mid-aged. This difference in the odds ratio of

driving during peak hours between the elderly and mid-aged is

smaller than that for the odds ratio of driving at night. This

change in the difference is consistent with that the elderly find

driving at night more problematic than driving during peak hours.

The other variables are organized into three groups for

interpretation. The first group includes those variables whose

coefficients are positive and differ from zero at the 10 percent

level. The results indicate that, other things being equal,

persons in the labor force are more likely to drive during peak

hours than those not in the labor force; persons with more than a

high school education are more likely to drive during peak hours

than those with less education; weekend trips are more likely to

be taken during peak hours than weekday trips; and work trips are

more likely to be taken during peak hours than trips for those

purposes that are omitted from the model. The second group

includes those variables whose coefficients are negative and

differ from zero at the 1 0 percent level. The results indicate

that, other things being equal, the young are less likely to

drive during peak hours than the mid-aged; males are less likely

to drive during peak hours than females; trips for shopping,

other family-or personal business, medical, visiting friends or

relatives, and other social or recreational purposes are less

likely to be taken during peak hours than trips for those

purposes that are omitted from the model. The last group includes

those variables whose coefficients do not differ from zero at the

10 percent level. The results indicate that, other things being

equal, Whites are just as likely as non-Whites to drive during

peak hours; household income or the size of an urbanized area

does not affect the probability of driving during peak hours;

persons living in central cities are just as likely as those

living outside central cities to drive during peak hours; and

winter trips are just as likely as non-winter trips to be taken

during peak hours.



22



Click HERE for graphic. 
Table 4.4 Logit analysis of driving during

peak hours



Source: Estimated by from the Travel Day File using the maximum

likelihood method with the SAS LOGISTIC procedure. Whether a

coefficient differs from zero is labeled as follows: n

significant at the 10 percent level; insignificant at the 10

percent level; others significant at the 1 percent level.



23



Chapter 5 THE EFFECTS OF AGE ON HOW THE ELDERLY DRIVE



Chapters 3 and 4 have shown that age affects how much, as well as

when the elderly drive. This chapter examines the effects of age

on how the elderly drive. Four aspects are considered. These

include driving speed, driving on limited-access highways,

vehicle size, and the number of passengers carried.



SPEED



This section examines the effects of age on the driving speeds of

the elderly. Do the elderly drive at lower speeds than others? If

they do, do they drive on roads with lower speed limits? Or do

they drive slower than others on roads with the same speed

limits? The 1990 NPTS can be used to shed light on whether the

elderly drive slower than others on limited access highways. The

1990 NPTS does not, however, include the information necessary to

test whether the elderly drive on roads with lower speed limits

than others. In the following analysis, speed is first tabulated

by driver age group and trip purpose. Regression is then used to

isolate the effects of age on the driving speeds of the elderly.

This analysis is done separately for all roadways combined and

for limited-access highways.



TABULATION



Table 5.1 tabulates the average speed for vehicle trips using all

roads by driver age group and trip purpose. The elderly drive at

an average speed of 22 mph for all trips, 24 mph for work trips,

and 22 mph for non- work trips. The mid-aged drive at an average

speed of 29 mph for all trips, 31 mph for work trips, and 28 mph

for non-work trips. The young drive at an average speed of 32 mph

for all trips, 34 mph for work trips, and 31 mph for non-work

trips.



Click HERE for graphic. 
Table 5.1 Average speed on all roads by

driver age group



Source: Calculated from the Travel Day File as the weighted

average of the speeds of individual vehicle trips. The speed of a

trip is measured as the ratio of its reported distance and

duration in miles per hour (mph).



24



Table 5.2 tabulates the average speed for vehicle trips using

limited-access highways by driver age group and trip purpose. As

expected, the average speeds for trips using limited access

highways are higher than those for trips using all roadways. On

average, the elderly drive at about 34 mph for all purposes, 36

mph for work trips, and 33 mph for non-work trips. The mid-aged

drive at about 39 mph for work trips, non-work trips, and all

purposes. The young drive at about 44 mph for all trips, 44 mph

for work trips, and 42 mph for non-work trips. All persons as a

group drive at about 39 mph for both work and non-work trips.



Click HERE for graphic. 
Table 5.2 Average speed on limited-access

highways by driver age group



Source: Calculated from the sample of private-vehicle trips in

the Travel Day File as the weighted average of the speeds for

individual trips in this sample. The distance of each trip in

this sample is broken down by roadway classification.



REGRESSION This regression analysis is similar to that for the

distance of vehicle trips in Chapter 3. The unit of observation

is individual vehicle trips. The dependent variable is the speed

of individual vehicle trips, measured as the ratio of reported

distance and duration in miles per hour. The same set of

explanatory variables are included as in the analysis of the

distance of vehicle trips except gasoline price. The standard

linear regression model in equation (1) is used along with the

ordinary least squares method for estimation. The results are

presented in Table 5.3. The model for trips using limited-access

highways is shown in the second and third columns. The model for

trips using all roadways is shown in the last two columns. The

results indicate that the elderly drive at lower speeds than the

mid-aged for trips using all roads as well as for trips using

limited-access highways. The model for all roadways indicates

that, other things being equal, the elderly drive 3.9 mph slower

than the mid-aged for trips using all roadways. The model for

limited-access highways indicates that, other things being equal,

the elderly drive 3.7 mph slower than the mid-aged for trips

limited-access highways. The other variables are organized into

four groups for interpretation. Those in the first group have a

positive effect in both models. The results indicate that, other

things being equal, the young drive at higher speeds than the

mid-aged for both trips using all roadways and trips



25



Click HERE for graphic.

Table 5.3 Weighted regression of speed of

vehicle trips



Source Estimated from the Travel Day File using the weighted

least squares method. Whether a coefficient differs from zero is

labeled as follows: n significant at the 5 percent level; u

significant at the 10 percent level ; insignificant at the 10

percent level : others significant at the 1 percent level.2



using limited-access highways. Similarly, males drive at higher

speeds than females; persons with higher household incomes drive

at higher speeds; weekend trips have higher speeds than weekday

trips; and trips for medical and visiting friends or relatives

have higher speeds than trips for the purposes that are omitted

from the models. The variables in the second group have a

negative effect in both models. The results indicate that, other

things being equal, persons living in areas with higher

population densities drive at lower speeds for both trips using

all roadways and trips using limited-access highways. Similarly,

peak trips have lower speeds than off-peak trips. The variables

in the third group have a positive effect in the model for all

roadways, but have no effect in the model for limited-access

highways. The results indicate that, other things being equal,

persons with more than a high school education drive at higher

speeds than those with less education for all roadways, but at

similar speeds on limited-access highways. The size of an

urbanized area increases the speeds for trips using all roadways,

but has no effect for trips using limited-access highways. Since

limited-access highways generally have higher speeds than local

roadways, the positive relationship between the size of an

urbanized area and the speeds for trips using all roadways may

imply that trips in larger urbanized areas are more likely to use

limited-access highways. In fact, the analysis of driving on

limited-access highways in the next section confirms this

implication. Similarly, night trips have higher speeds than

day-time trips on all roadways, but have similar speeds on

limited-access highways; and work trips on all roadways have

higher speeds than trips for those purposes that are omitted from

the models, but have similar speeds on limited-access highways.

Also, carpool trips have higher speeds than single- occupant

trips on all roadways, but have similar speeds on limited-access

highways. It is reasonable that carpool trips have higher speeds

than single- occupant trips on all roadways because carpool trips

may be more likely to use limited-access highways. The variables

in the last group have a negative effect in the model for all

roadways, but have no effect in the model for limited-access

highways. The results indicate that, other things being equal,

persons living in central cities drive at lower speeds than those

living outside central cities for all roadways, but drive at

similar speeds on limited-access highways. Similarly, persons in

the North East or North Central regions drive at lower speeds

than those in the West on all roadways, but drive at similar

speeds on limited-access highways. Also shopping trips and trips

for other family or personal business have lower speeds than

trips for the omitted trip purposes on all roadways, but have

similar speeds on limited-access highways.



LIMITED-ACCESS HIGHWAYS



This section examines the effects of age on the elderly's choice

of driving on limited-access highways. It is unclear, at the

outset, how age may affect the elderly's use of limited-access

highways. Limited-access highways have the lowest fatal crashes

per mile driven.' But they are also likely to have higher injury

risks from crashes due to the high speeds. As



27



discussed in Chapter 1, however, driving on limited-access

highways is one of the commonly mentioned conditions that the

elderly find difficult. The percent of vehicle miles driven on

limited-access highways is first tabulated by driver age group

and trip purpose. Logit analysis is then used to isolate the

effects of age on the elderly's probability of driving on

limited-access highways.



TABULATION



Table 5.4 tabulates the percent of vehicle miles driven on

limited-access highways by driver age group and trip purpose. The

elderly drive 21 percent of their miles on limited-access

highways for work trips and 15 percent for non-work trips. The

mid-aged drive 28 percent of their miles on limited-access

highways for work trips and 26 percent for non-work trips. The

young drive 22 percent of their miles on limited-access highways

for work trips and 24 percent for non-work trips.



Click HERE for graphic.

 Table 5.4 Percent of miles driven on

limited-access highways by driver age group



Source: Calculated from the Travel Day File. The 1990 NPTS

randomly selects a private-vehicle trip for each respondent (if

any), and breaks down its distance by roadway classification.



REGRESSION



This regression analysis is similar to that for driving at night

or during peak hours. The dependent variable is binary,

indicating whether a vehicle trip uses any mited-access highways.

The logit model is used along with the maximum likelihood method

for estimation. Two models are estimated in order to examine how

controlling for speed affects the elderly's choice of driving on

limited-access highways. The results are shown in Table 5.5.

Model 1 includes speed; Model 2 does not include speed. The

results in both models indicate that, other things being equal,

the elderly are less likely to drive on limited-access highways

than the mid- aged. The coefficients of the elderly dummy

variable are -0.5618 in Model 1 and -0.7364 in Model 2 and both

differ from zero at the 0.1 percent level. Thus, when speed is

not held constant (Model 2), the elderly's odds ratio is 52

percent lower than the mid-aged's odds ratio of driving on

limited-access highways. When speed is also held constant (Model

1), the elderly's odds ratio is 49 percent lower than the mid-



28



Click HERE for graphic.

 Table 5.5 Logit analysis of driving on

limited-access highways



Source: Estimated from the sample of trips for which distances

are broken down by roadway classification. Whether a coefficient

differs from zero is labeled as follows: n significant at the 5

percent level . u significant at the 10 percent level u

insignificant at the 10 percent level: others significant at the

0.1 percent level.



29



aged's odds ratio of driving on limited-access highways. So, the

elderly's odds ratio of driving on limited-access highways

decreases slightly (from 52 to 49 percent) when speed is

controlled. This slight decrease seems to indicate that the

elderly avoid driving on limited-access highways mainly for

reasons other than high speeds. The other variables are organized

into three groups for interpretation. The first group includes

variables whose coefficients differ from zero at the 10 percent

level in both models. The results indicate that, other things

being equal, males are more likely to drive on limited-access

highways than females; persons with more than a high school

education are more likely to drive on limited-access highways

that those with less education; the probability of driving on

limited access highways increases with an increase in the size of

an urbanized area; limited-access highways are more likely to be

used for carpool trips than for non-carpool trips; limited-access

highways are more likely to be used for works trips than for

trips for purposes that are omitted from the models. In addition,

the probability of driving on limited-access highways decreases

with an increase in household income; persons in other census

regions are less likely to drive on limited-access highways than

those in the West; limited-access highways are less likely to be

used for peak trips and for off-peak trips; and limited- access

highways are more likely to be used for shopping and other family

or personal business than for trips for the purposes that are

omitted from the models. The second group includes those

variables that do not differ from zero at the 10 percent level in

either models. The results indicate that, other things being

equal, race makes no difference in the choice of driving on

limited-access highways; limited-access highways are more likely

to be used for night trips than for day trips; limited-access

highways are as likely to be used for weekend trips as for

weekday trips; limited-access highways are as likely to be used

for trips for medical, visiting friends or relatives, and other

social or recreational purposes as for trips for those purposes

that are omitted from the models. The last group includes

variables whose statistical significance changes between the two

models. The results indicate that, other things being equal,

greater population density increases the probability of driving

on limited-access highways when speed is held constant, but shows

no effect when speed is not held constant; living in central

cities increases the probability of driving on limited-access

highways when speed is not held constant, but shows no effect

when speed is also held constant; and persons in the labor force

are more likely than persons not in the labor force to drive on

limited-access highways when speed is not held constant, but are

as likely to drive on limited-access highways when speed is also

held constant.



AUTOMOBILE SIZE



This section examines the effects of age on the size of

automobiles that the elderly drive. Do the elderly drive larger

automobiles than others? The answer is not straightforward. As

discussed in the introduction, the increased injury risk and

reduced injury costs of the elderly may have two opposite effects

on the elderly's choice of automobile size. In addition, if one



30



assumes that the elderly value comfort or prestige more than

others, one may argue that the elderly may drive larger

automobiles for these reasons rather than for their

crashworthiness. The literature, however, provides no evidence

that the elderly value comfort or prestige more than others.

Also, the fact that elderly drivers take trips that are shorter

in distance, as shown in Chapter 3, suggests that the comfort of

an automobile is less important for the elderly than for others.

The 1990 NPTS associates each vehicle used on the travel day with

a main driver. This association allows one to link the

characteristics of the main drivers with the attributes of the

vehicles that they drive. The 1990 NPTS measures vehicle size

according to the National Accident Sampling System.1 The size of

an automobile is based on its wheelbase length and is coded on a

scale from one to six. For example, the size of a Ford Escort is

one and the size of a Toyota Camry is three. Only automobiles are

included in the analysis. Non-householdowned automobiles are

excluded because they cannot be related to household attributes

of the main drivers. The following analysis starts with a

tabulation of automobile size by age group of the main drivers

and labor force participation. Regression is then used to isolate

the effects of age on the size of automobiles that the elderly

drive.



TABULATION Table 5.6 tabulates the average size of automobiles by

age group of the main drivers and labor force participation. For

persons not in the labor force, the average sizes of the

automobiles they drive are 3.16 for the elderly, 2.85 for the

mid-aged, 2.52 for the young, and 2.88 for all. For those in the

labor force, the average sizes are 2.90 for the elderly, 2.61 for

the mid-aged, 2.35 for the young, and 2.58 for all.



Click HERE for graphic.

Table 5.6 Average size of automobiles by

age group of main drivers



Source: Calculated from the Vehicle and Person Files as the

weighted average of automobile sizes. The size of an automobile

is based on its wheelbase length, and is on a scale from one to

six.



31



REGRESSION The dependent variable is the size of an automobile

measured on a scale from one to six. Unlike the regression

analyses so far, where the unit of observation is either

individual drivers or vehicle trips, the unit of observation here

is individual automobiles. This analysis is similar, however, to

those for the distance and speed of vehicle trips in that the

standard linear regression model in equation (1) is used along

with the weighted least squares method for estimation. The

results are shown in Table 5.7. Two models are estimated. Model 1

includes a set of personal, household, and location

characteristics of the main drivers. In addition to these

characteristics, Model 2 also includes two vehicle attributes:

vehicle age and import status (whether a vehicle is

foreign-made). The results indicate that the coefficients of the

elderly dummy variable are 0.4039 in Model 1 and 0.2574 in Model

2, and both differ from zero at the 0.01 percent level. Thus,

other things being equal, the elderly drive larger automobiles

than the mid-aged. The other explanatory variables are organized

into three groups for interpretation. The first group includes

variables whose coefficients differ from zero at the 10 percent

level in both models. The results indicate that, other things

being equal, the young drive smaller automobiles than the mid-

aged; persons with more than a high school education drive

smaller automobiles than those with less education; persons in

the labor force drive smaller automobiles than those not in the

labor force; the size of an automobile increases with an increase

in household income, but decreases with an increase in the size

of an urbanized area; and persons in the South drive larger

automobiles than those in the West. The second group includes

variables whose coefficients do not differ from zero at the 1 0

percent level in either model. The results indicate that, other

things being equal, living in central cities does not affect the

size of an automobile one drives and persons in the South East

drive automobiles that are as large as those driven by persons in

the West. The third group includes variables whose statistical

significance changes between the two models. The results indicate

that, other things being equal, males are shown to drive larger

automobiles than females when vehicle age and import status are

not held constant (Model 1). But once vehicle age and import

status are held constant (Model 2), males drive automobiles that

are the same size as those driven by females. Similar changes in

statistical significance are also observed for Whites, Blacks,

household size, and persons living in the North Central region.

On the other hand, when vehicle age and import status are not

held constant (Model 1), Hispanics are shown to drive automobiles

that are the same size as those driven by nonHispanics. Once

vehicle age and import status are given (Model 2), however,

Hispanics are shown to drive smaller automobiles. Two

qualifications are in order. First, these models do not include

owning and operating costs as an explanatory variable, though

there is no reason to believe that including such a cost variable

would necessarily change the results. It is possible to estimate

these costs using other sources with the information on vehicle

make and model. However, estimating these costs would require

additional resources and is beyond the scope of this study.



32



Click HERE for graphic.

Table 5.7 Weighted regression of automobile

size



Source: Estimated from the Vehicle and Person Files with the

weighted least squares method. Whether a coefficient differs from

zero is labeled as follows: n significant at the 1 percent level;

u significant at the 10 percent level; insignificant at the 10

percent level; others significant at the 0.01 percent level.



33



NUMBER OF PASSENGERS CARRIED



This section examines the effects of age on the number of

passengers that the elderly carry. Given that the elderly show

increased crash involvements per unit of exposure, one might

hypothesize that they feel less comfortable with carrying

passengers than younger persons. The following analysis first

tabulates the average automobile occupancy by driver age group

and trip purpose. Regression is then used to isolate the effects

of age on the number of passengers carried in each vehicle trip

on the travel day.



TABULATION Table 5.8 tabulates the average occupancy of

automobile trips by driver age group and trip purpose. The

elderly's average ccupancies are 1.39 for all purposes, 1.08 for

work trips, and 1.41 for non-work trips. The mid-aged's average

occupancies are 1.54 for all purposes, 1.14 for work trips, and

1.71 for non-work trips. The young's average occupancies are 1.44

for all purposes, 1.10 for work trips, and 1.56 for non-work

trips.



Click HERE for graphic.

Table 5.8 Average occupancy of automobile

trips by driver age group



Source: Calculated from the Travel Day File as the weighted

average of occupancies of individual automobile trips on the

travel day.



REGRESSION The dependent variable is the number of occupants in

an automobile trip on the travel day. This regression analysis is

similar to those for the distance and speed of vehicle trips in

two ways. First, the unit of observation is individual vehicle

trips. Second, the standard linear regression model in equation

(1) is used along with the weighted least squares method for

estimation. This analysis differs, however, from those for the

distance and speed of vehicle trips in that this analysis

includes additional variables that measure household composition

and vehicle ownership. The results are shown in Table 5.9. The

results indicate that the coefficient of the elderly dummy

variable is -0.0558 and differs from zero at the 1 percent level.

Thus, other things being equal, the elderly carry fewer

passengers than the mid-aged.



34



Click HERE for graphic.

Table e 5. 9 Weighted regression of

occupancy of automobile trips



Source: Estimated from the Travel Day File with the weighted

least squares method. Whether a coefficient differs from zero is

labeled as follows: n significant at the 1 percent level; u

significant at the 10 percent level : insignificant at the 10

percent level ; others significant at the 0.01 percent level.



35



The other variables are interpreted by category of

characteristics. Among the personal characteristics, the young

carry fewer passengers than the mid-aged and persons in the labor

force carry fewer passengers than those not in the labor force.

In addition, males carry just as many passengers as females.

Among the household characteristics, automobile occupancy

decreases with an increase in household income and vehicle

ownership; persons from household with more children between the

ages of 5 and 22 years carry more passengers; persons from

single-resident households carry fewer passengers than those from

multi-person households; and Blacks carry fewer passengers than

non-Blacks. Also, Whites carry as many passengers as those who

are neither White nor Black; and Hispanics carry as few

passengers as non-Hispanics. Among the location characteristics,

automobile occupancy increases with an increase in population

density, but decreases with an increase in the size of an

urbanized area; automobile occupancy is lower in the other census

regions than in the West. In addition, living in central cities

does not affect automobile occupancy. Gasoline price, as measured

in this analysis, has a positive but statistically insignificant

effect on automobile occupancy. Among the trip haracteristics,

night trips have higher occupancies than day trips; weekend trips

have higher occupancies than weekday trips; and long distance

trips have higher occupancies than short distance trips. In

addition, trips for other social or recreational purposes have

higher occupancies than trips for those purposes that are omitted

from the model; and trips for the other remaining purposes

included in the model (work-related, shopping, other

family/personal business, medical, and visiting

friends/relatives) have lower occupancies than trips for the

omitted purposes. The omitted purposes include trips for school

or church, trips for vacation, trips for pleasure driving, and

trips for other purposes.



36



Chapter 6 SUMMARY AND POLICY IMPLICATIONS



This report has examined the effects of age on six driving habits

of the elderly (persons age 65 years or older). This chapter

summarizes the main results and discusses the implications of

these results to policy-making in areas concerning the mobility

and traffic safety of the elderly.



SUMMARY



Elderly drivers show an increased effort of self-protection in

their driving habits relative to mid-aged drivers (persons

between the ages of 25 and 64 years). Elderly drivers not only

reduce daily driving exposure, avoid driving at night, avoid

driving during peak hours, and avoid driving on limited-access

highways, but also drive at lower speeds, drive larger

automobiles, and carry fewer passengers. The following summarizes

the results for each of the six driving habits examined.



* Daily Driving Exposure. The elderly reduce their daily driving

exposure by reducing not the frequency but the istance of vehicle

trips. The elderly drive fewer vehicle miles than the mid-aged.

They take as many vehicle trips as the mid-aged, but their

vehicle trips are shorter in distance than those taken by the

mid-aged.



* Driving By Time of Day. The elderly are less likely to drive at

night and during peak hours than the mid-aged. In addition, the

elderly are lesser likely to drive at night than to drive during

peak hours. This is consistent with the fact that the elderly

find driving at night more problematic than driving during peak

hours.



* Driving By Roadway Type. The elderly are less likely to drive

on limited-access-highways than the mid-aged. This avoidance

behavior by the elderly can be due to many characteristics of

limited-access-highways, such as high speeds. When speed is held

constant, however, the elderly still are found to be less likely

to drive on limited-access highways. In addition, the elderly's

likelihood of driving on limited-access-highways decreases only

slightly when speed is held constant. This slight decrease seems

to suggest that the elderly avoid driving on

limited-access-highways mainly due to characteristics of

limited-access-highways other than high speeds.



* Driving Speed. The elderly drive at lower speeds than the

mid-aged. They drive about 4 miles per hour (mph) slower than the

mid-aged for all trips. This is either because the elderly are

more likely to drive on roadways with lower speed limits or

because they drive slower on roadways with the same speed limits.

The evidence indicates that both



37



possibilities occur with the elderly. When only Vehicle trips

that use limited-access highways are considered, the elderly are

found to drive about 4 mph slower than the mid-aged. As indicated

earlier, the elderly also are less likely to drive on

Lmited-access-highways.



Automobile Size. The elderly drive larger automobiles than the

mid-aged. When the size of an automobile is measured by wheelbase

size on a scale from one to six, the average size of automobiles

driven by the elderly is 0.40 smaller then that by the mid-aged

when automobile age and import status are not held constant and

is 0.26 smaller when automobile age and import status are held

constant.



Number of Passengers Carried. The elderly carry fewer passengers

than the mid-aged. In fact, the elderly carry an average number

of passengers that is about 0.05 lower than the mid-aged.



These differences in the driving habits between the elderly and

mid-aged reflect the marginal effects of age difference between

the two groups. These differences do not reflect any effects of

the differences between the two groups in other personal,

household, location, and trip characteristics that are held

constant in this study.



POLICY IMPLICATIONS



Despite their increased effort of self-protection in their

driving habits, as summarized above, the elderly still show a

higher risk of crash and injury per unit of exposure than the

mid-aged.1 When the elderly adjust their driving habits, they

consider the risks they face, but not the external risks they

impose on others when they drive. If the elderly are forced to

adjust their driving habits further to offset the external risks

of their driving, their risk of crash and injury would be reduced

and society as a whole would be better off. Any further

adjustment in the elderly's driving habits, however, is likely to

make the elderly worse off due to reduced mobility. The challenge

to policy-making is to balance these consequences of any policy

concerning the mobility and traffic safety of the elderly. The

following discusses four existing policy options.



Removing Hazardous Elderly Drivers from Roadways.2 Removing

elderly drivers through the use of stricter licensing laws is

controversial. First, the removed drivers are forced to pay a

large price-loss of automobile mobility. Second, elderly drivers

have the lowest severe crash nvolvement per driver. If the

purpose is to reduce the maximum number of severe crashes per

removed driver, then removing younger drivers would be far more

effective than removing elderly Drivers. Third, the physical and

cognitive abilities vary widely among the elderly. Forth, such

removal has the appearance of discriminating against elderly

drivers. As a result, the higher the proportion of elderly

drivers that a state has, the harder to implement such an option.

The best example is Florida, where



38



the elderly population as a share of the total population is the

highest in the nation. Three attempts by Florida's legislature to

pass stricter licensing laws for elderly drivers have failed in

the past several years.3



Making Alternatives to Driving Available. 4 This option

accommodates the option of removing elderly drivers from

roadways. Alternatives to driving include walking, public

transit, specialized transportation, and the use of taxis. As

more elderly persons live in suburbs where the population density

is low, these alternatives become less feasible. Walking is

difficult for elderly persons in low density areas, and it is

extremely costly to expand public transit for the elderly in

these areas. Expanding specialized transportation to low density

areas is also expensive. Subsidizing the use of taxis may be more

expensive than specialized transportation.



Improving Vehicle and Roadway Design and Operation. 5 This option

attempts to accommodate the reduced physical and cognitive

abilities of elderly drivers. There is, however, strong evidence

that drivers become more risk-taking when the driving environment

becomes safer. 6 There is no reason to believe that elderly

drivers do not have such a behavior. This behavior would off-set

many of the intended benefits of improving vehicle and roadway

design and operation.



Re-Educating Elderly Drivers. 7 Re-educating elderly drivers

would be an appropriate policy if elderly drivers were not fully

aware of their reduced cognitive and physical abilities and the

consequences to their traffic safety.



As the number of elderly drivers continues to grow, the welfare

of the society as a whole becomes increasingly dependent upon the

mobility and traffic safety of elderly drivers. While this study

has implications to policy-making, policy recommendation is

beyond the scope of this report. Future research needs to examine

the impacts of existing policies, as well as to develop new

policy options that would better balance the effects on the

elderly and society as a whole.



39



ENDNOTES CHAPTER 1



1. Elderly is defined as age 65 years or older. This is the most

   commonly used definition in the literature on the mobility and

   Safety of elderly persons.



2. Federal Highway Administration, 1990 Nationwide Personal

   Transportation Survey.- User's Guide for the Public Use Tapes,

   Advance Copy (Washington, 1991).



3. Summary of Findings and Recommendations: Highway Mobility and

   Safety of Older Drivers and Pedestrians (Washington, D.C.:

   Highway Users Federation for Safety and Mobility, 1985);

   Transportation Research Board, "Executive Summary," in

   Transportation in an Aging Society. Improving the Mobility and

   Safety of Older Persons, Vol. 1, Committee Report and

   Recommendations (Washington, D.C.: National Research Council,

   1988); and Conference on Research and Development Needed to

   Improve Safety and Mobility of Older Drivers (Washington, D.C.:

   National Highway Traffic Safety Administration, 1990?).



4. The TRB effort and Congressional request resulted in a

   two-volume report by TRB, Transportation in an Aging Society.-

   Improving Mobility and Safety for Older Persons, Special Report

   218, Vol. 1: Committee Report and Recommendations, Vol. 2:

   Technical Papers (Washington, D.C.: National Research Council,

   1988).



5. The result is a report BY THE U.S. Department of

   Transportation, Older Driver Pilot Program: Report of the

   Secretary of Transportation to the United States Congress

   (Washington, D.C.: Federal Highway Administration, 1990).



6. For example, Max Israelite, "Take Away My License: I Would

   Rather Stop Driving Too Soon Than Too Late (Elderly Automobile

   Drivers)" in Newsweek (May 9, 1994): 1 1; Joan E. Rigdon, "Car

   Trouble: Older Drivers Pose Growing Risk On Roads As Their

   Numbers Rise; They Crash More Than Many, Yet Taking Away Wheels

   Leads To Isolation, Anger; A Man Runs Over His Wife" in Wall

   Street Journal (October 29, 1993): Al; Lisa J. Moore, "Drive on

   Miss Daisy (older automobile drivers)" in U.S. News & World

   Report (June 22, 1992): 8384; Alan L. Otten, "Older Drivers

   Appear Safer But More Frail (National Institute On Aging Study

   Reveals Older Drivers More Likely To Die In Auto Accidents Than

   Younger Drivers)" in Wall Street Journal (June 1, 1992): Bl;

   "Safety And The Older Driver: When Difficult Issues Collide

   (Federal And State Authorities Struggle To Identify Aged Drivers

   Who Pose A Hazard While Not Discriminating Against Those Who Do

   Not)" in New York Times (May 4, 1992): Al; Sandy Rovner, "Driving

   Difficulties Increase With Age" in Washington Post



40



   (October 30, 1990): WH 1 6; and James Camey, "Can A Driver Be Too

   Old? Fender Benders And Fatalities Raise Fears Over Elderly

   Motorists" in Times (January 16, 1989): 28.



7. U.S. Federal Highway Administration (FHWA), Highway

   Statistics, 1990 (Washington, D.C.: FHWA, 1991), Table DL-20; and

   FHWA, Highway Statistics, Summary to 1985 (Washington, D.C.:

   FHWA, 1987), Table DL-220.



8. U.S. Bureau of the Census, Statistical Abstract of the United

   States, 1992 (Washington, D.C.: U.S. Government Printing Office,

   1992), Table 14.



9. FHWA, Highway Statistics, 1990, Table DL-20; and FHWA, Highway

   Statistics, Summary to 1985, Table DL-220.



10. Ruth H. Asin, Characteristics of 1977 Licensed Drivers and

   Their Travel.- Report 1, 1977 NPTS (Washington, D.C.: FHWA,

   1980), Table 16; and Ezio C. Cerrelli, Crash Data and Rates ffor

   Age- Sex Groups of Drivers, 1990 (Washington, D.C.: National

   Center for Statistics & Analysis, 1992), Table C.



11. The elderly population is expected to reach 20 percent of all

   persons by the year 2020, according to Census Bureau, Projections

   of the Population by Age, Sex, and Race for the United States,

   1983-2080 (Washington, D.C.: Government Printing Office, 1984),

   No. 952, Series P-25, cited by TRB, Transportation in -an Aging

   Society, Vol. 1: 22. In 1990, the elderly population was 12.5

   percent of all persons, while the number of elderly drivers was

   13.3 percent of all drivers.



12. Finn Jorgensen and John Polak, "The Effect of Personal

   Characteristics on Drivers' Speed Selection," Journal of

   Transport Economics and Policy, 27 (September 1993): 237-252.



13. TRB, Transportation in an Aging Society, Vol. 1: 61, 72.



14. Ibid.: 39-40.



15. J. Peter Rothe, The Safety of Elderly Drivers: Yesterday's

    Young in Today's Traffic (New Brunswick:Transaction Publishers,

      1990), p. 64.



16. S.J. Flint, K.W. Smith, and D.G. Rossi, "An Evaluation of

   Mature Driver Performance," paper presented at the 14th

   International Forum on Traffic Records Systems, San Diego (1988),

   cited by J. Peter Rothe, The Safety of Elderly Drivers, 127.



41





17. P.A. Brainn, Safety and Mobility Issues in Licensing and

   Education of Older Drivers (Washington, D.C.: NHTSA, U.S.

   Department of Transportation, 1980), cited by Sandra Rosenbloom,

   "The Mobility Needs of the Elderly," in Transportation in an

   Aging Society., improving Mobility and Safety for Older Persons,

   Special Report 218, Vol. 2, Technical Papers (Washington, D.C.:

   National Research Council, 1988), 40.



18. R. Risser and C. Chaloupka, "Elderly Drivers: Risks and Their

   Causes," in Proceedings of the Second International Conference on

   Road Safety, ed. by J.A. Rolhengafter and R.A. de Bruin (Assen,

   Netherlands: Van Gorcum, 1987), cited by Sandra Rosenbloom, "The

   Mobility Needs of the Elderly," 40.



CHAPTER 3



1. G.S. Maddala, Limited-Dependent and Qualitative Variables in

   Econometrics, Econometric Society Monographs, No. 3 (Cambridge,

   Mass.: Cambridge University Press, 1983): 149- 165.



2. SAS/STAT User's Guide, Version 6, Fourth Edition (Cary, NC:

   SAS Institute Inc., 1989): 1005-6.



3. Bureau of Census, Statistical Abstract, 1991 (Washington,

   D.C.: U.S. Department of Commerce, 1992), No. 762.

   Refiner/Reseller Sales Price of Motor Gasoline, by Grade and

   State: 1989 to 1991; and No. 998. State Gasoline Tax Rates, 1990

   and 1991, and Motor Fuel Tax Receipts, 1990.



4. The SAS procedure used for estimation, LIFEREG, does not

   report the log likelihood at zero (i.e., when all explanatory

   variables are excluded).



5. For more on the interpretation of Tobit models, see John F.

   McDonald and Robert A. Moffift, 'The Uses of Tobit Analysis," The

   Review of Economics and Statistics 62 (1980): 318-321.



6. Rosenbloom, "The Mobility Needs of the Elderly," Vol. 2:

   33-34.



42



CHAPTER 5



1 FHWA, User Guide to the 1990 Nationwide Personal Transportation

   Survey, Appendix J: National Accident Sampling System Vehicle

   Make and Model Coding Dictionary (Washington, D.C.: Department of

   Transportation, 1991).



2. A more appropriate tool would be grouped data regression or

   ordered probit regression (William H. Green, Econometric

   Analysis, New York: MacMillian Publishing Company, 1990).



3. Kenneth Train, Qualitative Choice Analysis: Theory,

   Econometrics, and an Application to Automobile Demand, MIT Press

   Series in Transportation Studies, Marvin L. Manheim, ed.

   (Cambridge, Mass.: M.I.T. Press, 1986): 143-144.



    CHAPTER 6



1. See Chapter 1.



2. TRB, Transportation in an Aging Society, Vol. 1: 76-103.



3. A.D. Burch, "Bill Targets Old, Young For Added Driving Tests"

   in The Orlando Sentinel (March 3, 1994): C-1.



4. TRB, Transportation in an Aging Society, Vol. 1: 76-103.



5. Ibid.



6. Sam Peltzman, "The Effects of Automobile Safety Regulation,"

   Journal of Political Economy, 83 (June 1975): 677-725.



7. TRB, Transportation in an Aging Society, Vol. 1: 76-103.



43



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are essential to the content of the report.



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