ARCHIVED DATA USER SERVICE
(ADUS)
An Addendum to the ITS Program Plan
FINAL
Version 3
September, 1998
1.0 Archived Data User Service
1.1 Introduction
The Archived Data User Service (ADUS) describes the need for an Intelligent Transportation Systems (ITS) Historical Data Archive and expands the National ITS Architecture to encompass the needs of the stakeholder groups of this user service. Many of these stakeholder groups previously had little or no involvement in the National ITS Architecture. ADUS requires ITS-related systems to have the capability to receive, collect, and archive ITS-generated operational data for historical, secondary, and nonreal-time uses. ADUS prescribes the need for a data source for external user interfaces and provides data products to users. The goal is the unambiguous interchange and reuse of data and information throughout all functional areas.
ITS technologies generate massive amounts of operational data that are presently used primarily in real-time to effect traffic control strategies. Examples include the adjustment of ramp meter timing based on freeway flow conditions and the use of variable message signs (VMSs) to communicate traffic incidents to travelers. These data offer great promise for uses beyond the execution of ITS control strategies, such as applications in transportation administration, policy, safety, planning, operations, and research. In most cases, ITS-generated data are similar to data traditionally collected for these applications, but are much more voluminous in quantity and geographical and temporal coverage. ITS has the potential to provide data needed for planning, performance monitoring, program assessment, policy evaluation, and other transportation activities, including multimodal and intermodal applications. This user service describes the need for the collection, manipulation, retention, and distribution of data generated by ITS for use in other transportation activities.
1.2 Stakeholders' Needs
There is a broad spectrum of users who must rely on any and all available sources of data to feed the applicable planning models, simulations, and control strategies. The users' needs for the data are outlined below with a partial list of their primary transportation-related tasks:
Metropolitan Planning Organization (MPO) and State Transportation Planning Short- and long-range identification of transportation improvements and policies. Includes multimodal passenger transportation improvements, congestion management, air quality planning, airport access planning, and the development and maintenance of travel demand forecasting and traffic simulation models. Operation and management of multimodal transportation systems is becoming an important aspect of the transportation planning function.
Transportation System Monitoring Collection and analysis of transportation data for use by policy-making at all levels of government and other customers for policy analysis, performance monitoring, and investment analysis. An example is the Highway Performance Monitoring System (HPMS) which provides data for reporting to Congress on the condition, performance, and future investment requirements of the nation's highway system.
Traffic Management Day-to-day operations of deployed ITS; e.g., operation of traffic signal control systems.
Transit Management Day-to-day transit operations, including scheduling, route delineation, origin-destination surveys, passenger counts, fare pricing, vehicle maintenance, transit management systems, evaluation, and capital planning.
Air Quality Analysis Regional air quality monitoring, and transportation plan conformity with air quality standards and goals.
MPO/State Freight and Intermodal Planning Planning for intermodal freight transfer, goods movement, and port facilities.
Safety Identifying countermeasures for general safety problems or hotspots; automated collision notification; delivery of emergency medical services; automated crash investigation data entry; deployment planning for incident response; hazardous site identification.
Design, Construction, & Maintenance Planning for the rehabilitation and replacement of pavements, bridges, and roadside appurtenances and the scheduling of maintenance activities.
Transportation Research Development of forecasting and simulation models and other analytic methods and improvements in data collection practices. Transportation research encompasses many of the stakeholder functions.
Commercial Vehicle Operations Crash investigations; enforcement of commercial vehicle regulations, and HazMat response. ITS provides them reports of violations and patterns in movements.
Emergency Management (local police, fire, and emergency medical). Response to transportation incidents, crash investigations, and patrol planning.
Private Sector Provision of traffic condition data and route guidance (Information Service Providers); commercial trip planning to avoid congestion (carriers); and vehicle design (auto manufacturers).
Land Use Regulation and Growth Management Development of land use plans and zoning regulations; establishment of growth impact policies; and community economic development.
All ITS historical and nonreal-time data should be capable of being stored, disseminated, and/or manipulated to support users with pre-defined data products. These data include, but are not limited to the following categories: (1) freeway data, (2) toll data, (3) arterial (nonfreeway) data, (4) parking management data, (5) transit and ridesharing data, (6) incident management data, (7) safety-related data, (8) commercial vehicle operations data, (9) environmental and weather data, (10) vehicle and passenger information data, and (11) intermodal operations data.
1.3 Archived Data User Service Description
This user service will provide an ITS Historical Data Archive for all relevant ITS data and will incorporate the planning, safety, operations, and research communities into ITS. It will provide the data collection, manipulation, and dissemination functions of these groups, as they relate to data generated by ITS. The ITS Historical Data Archive will function as a data warehouse or repository to support stakeholder functions.
An example from transportation planning will illustrate the use of this service. (Many such example applications also exist for the other stakeholder groups.) AADTs (i.e., Annual Average Daily Traffic, the daily traffic count estimates for a highway) are one of the most essential data types used by planners and engineers. Nearly all AADTs used by planners are estimates based on 24- or 48-hour short counts that have been adjusted using areawide factors for daily and seasonal variability. Facility-specific data on the temporal distribution of traffic and its variability are extremely limited. ITS roadway surveillance equipment can provide detailed data on the actual average daily traffic and its variability. ITS data, as source data, would improve the accuracy and usefulness of one of the core performance measures (AADT) used by transportation planners. ITS data will also allow for direct measurement of congestion and permit separation of the recurring and nonrecurring components of congestion.
More detailed data will be required as the management paradigm becomes more widespread. Travel Demand Forecasting (TDF) models for predicting long-term demand characteristics (20-years into the future) use average values -- basically, one wants to make decisions about adding capacity to the nearest additional lane of accuracy (i.e., 2,200-2,300 vehicles per hour). Average peak hour traffic counts are precise enough for this purpose. However, for meeting the newer planning requirements which tend to be more short-range in nature -- such as congestion monitoring and microscale air quality modeling -- information on extreme events is important. For example, consider a freeway section where the only traffic data available provide an average AADT developed from a factored 48-hour short count and K- and D-factors (factors to convert a daily volume to a peak hour volume) borrowed from other urban sites. A capacity analysis on this section using these data would not only ignore days where volumes were higher than average, but is prone to the sampling bias inherent in using factored and borrowed data. Since delay is a nonlinear function of volume as volumes approach and exceed capacity, these rare but highly influential events would be missed if the short-term and borrowed data were the basis for congestion monitoring. Moreover, the impact of incidents on delay would be completely ignored in the current approach. On the other hand, ITS roadway surveillance data would directly measure congestion, including days with abnormally high volumes and incidents.
The information obtained from ITS sources in the above example is also valuable in a multimodal context. In addition to the highway surveillance data mentioned above, ITS technologies can also capture information about the movements of transit passengers and the performance of transit systems. When the highway and transit data are fused, effective multimodal planning B such as designing transportation strategies for key corridors B can be achieved. Further, because the data are collected constantly, a continuous multimodal performance evaluation program can be implemented. Such an effort would greatly aid transportation and transit planners in fulfilling Federal requirements and meeting local needs.
It is also possible to extend this example into the realm of traffic operations. Although ITS generally uses real-time data to implement control patterns, nonreal-time data can also be of use. Consider that ramp metering is also present for the hypothetical freeway segment mentioned above. The metering rates are generally pre-timed, actuated by mainline traffic flow, or a combination of the two. In the pre-timed case, data on historical volume and congestion patterns can be used to set metering rates by time of day. In advanced systems that are proactive (i.e., they predict traffic conditions in the very near future), historical patterns can be used in predictive algorithms. Finally, historical ramp metering rates and freeway traffic conditions are valuable to operators of traffic signal control systems in that pre-timed or proactive timing plans can be developed with that data. From an archival viewpoint, the needs of operators would tend to be more short-term (what happened yesterday or last week) than those of transportation planners.
The discussion above cites only two examples of the uses and benefits of archived ITS-generated data. Examples for other stakeholder groups are just as meaningful and include:
Transit Management: Electronic Fare Payment allows continuous boardings to be collected. Computer-aided dispatch systems allow O/D patterns to be tracked. AVI on buses allows monitoring of schedule adherence and permits the accurate setting of schedules without field review.
Transportation System Monitoring: Traffic data can be collected as part of ITS operations and may eliminate the need for labor-intensive short counts on highways covered by ITS.
Air Quality Analysis: Roadway surveillance provides actual speeds, volumes, and truck mix by time of day. Modal emission models will require these data in even greater detail and ITS is the only practical source.
MPO/State Freight and Intermodal Planning: Electronic credentialing and AVI allows tracking of truck travel patterns, sometimes including cargo. Improved tracking of congestion through the use of roadway surveillance data leads to improved assessments of intermodal access.
Safety: Roadway surveillance data provide continuous volume counts, truck percents, and speeds, leading to improved exposure estimation and measurement of the actual traffic conditions for crash studies. ITS technologies (e.g., GPS) also offer the possibility of automating field collection of crash data, especially location, by police officers and others.
Design, Construction, & Maintenance: Roadway surveillance data provide continuous volume counts, vehicle classifications, and vehicle weights, making more accurate loading data and growth forecasts available.
Transportation Research: Traveler response to system conditions can be measured through system detectors, probe vehicles, or monitoring in-vehicle and personal device use. Travel diaries can be imbedded in these technologies as well.
Commercial Vehicle Operations: Electronic credentialing and AVI allows tracking of hazardous material flows, allowing better deployment of inspection and response personnel.
Emergency Management: Electronic credentialing and AVI allows tracking of truck flows and high incident locations, allowing better deployment of response personnel.
Private Sector: Roadway surveillance data and probe vehicles can identify existing congestion and can be used to show historical patterns of congestion by time-of-day. Incident location and status can be directly relayed.
Land Use Regulation and Growth Management: Improved identification of travel and congestion patterns provides a sound technical basis for establishing policies.
Finally, the functionality of the Archived Data User Service will be increased if opportunities for data fusion are pursued. The value of certain ITS-generated data is enhanced if additional information is added from other sources. One example is in the safety area. While locations of crashes (in terms of geo-coordinates) can be provided with high precision, safety agencies are concerned with matching crash locations with highway geometric features. This matching involves fusing the crash location data with other data (e.g., roadway characteristics inventories) and requires a common referencing system. Another example of data fusion potential is the collection of travel activity data. Traveler location data (such as those from automatic vehicle identification technologies) can identify where travelers are in time and space, again in terms of geo-coordinates. However, transportation planning agencies need to identify the origins, destinations, and lengths of trips by purpose of the trip. As the sophistication of systems and level of ITS deployment expands in the future, it may be possible to merge the location data with land use data, thereby inferring the type of trip without encumbering the traveler with an extra task.
1.4. Operational Concepts
1.4.1 ADUS Functions
The basic function for ADUS is to provide an ITS Historical Data Archive and to integrate user functional needs for data.
1.4.1.1 Basis for Development
The systems to support the Archived Data User Service should be based on, but not limited to, existing data flows within the National ITS Architecture. As new data flows are added to the National Architecture -- and as additional uses of existing data flows are identified -- they should be examined for their inclusion within the systems to support the Archived Data User Service. The systems should also be flexible enough to accommodate data flows unique to individual ITS deployments that may not warrant a change to the National Architecture. They should also be capable of handling data from existing data collection programs that may not be deemed as being in the ITS realm.
1.4.1.2 System Structures
To accommodate both existing (legacy) transportation systems and the incremental deployment of new ITS, the information management systems which support data archiving may utilize one of two concepts, or a hybrid of these:
a. Decentralized - Each ITS facility possesses its own archiving function with a minimum of interconnects with other ITS facilities but utilizing standardized data definitions.
b. Centralized - Relevant data from each ITS subsystem may be captured in a central repository either directly or "virtually" through the use of appropriate distributed technologies and standards.
1.4.1.3 System Functions
The following functions should be implemented to support the ITS Historical Data Archive system deployment.
1.4.1.3.1 Data Processing
Data Processing is the receipt and processing of incoming data from other ITS functions. The Archived Data User Service should have the ability to perform the following data processing functions for designated users:
a. Store data in the same format as received from ITS subsystems.
b. Accommodate levels of aggregation and reduction of the data flows, depending on the type of data represented.
c. Sample raw data flows for permanent storage in accordance with user specifications. Permanent storage of the sampled data should be either online, offline, or both.
d. Apply quality control procedures to the data, including the flagging of suspect data and the editing of data.
e. Distinguish between the following data types: unprocessed (raw), edited, aggregated, and transformed (processed in conjunction with other data or methods).
Data processing functions may be assigned to different personnel. For example, data quality control and editing may be assigned to the group or agency responsible for the initial data collection. Subsequent data reduction may be assigned to personnel other than the original collectors of the data.
1.4.1.3.2 Data Storage
Data Storage encompasses both online and offline storage of raw and processed data. It includes:
a. Data Manipulation and Integrity -- Original, unaltered data must always be preserved in the master archive for some minimum amount of time. The original data stored in the Historical Data Archive should not be modified as the result of user-specified data requests or data manipulation. Rather, edited or otherwise transformed data can co-exist with the original data but must be linked to them. User-defined data manipulation should only be processed from copies of the master archives (e.g., editing, formatting, aggregation, reduction, or fusion of data) and preparation of data will be processed and archived for designated users separately from the master archives.
b. Metadata and Meta-Attributes -- The Archived Data User Service should include specifications of detailed metadata and meta-attributes about the data stored in the archive. Metadata and meta-attributes should provide a complete description of the data in terms of standard data dictionary characteristics as well as providing analysts an indication of collection and sampling conditions, variability, quality control procedures, edits, and transformations. These features will promote careful use of the data and will help analysts understand the nature of the data. Metadata and meta-attributes should be assigned whenever quality control, data reduction, or data aggregation procedures are applied. In addition to metadata and meta-attributes, which are formal parts of the data archive, the development of caveats on the limitations of the data should also be encouraged.
c. Location Referencing -- The Archived Data User Service should encompass a common location referencing system for linking data elements in the archive.
1.4.1.3.3 Data Retrieval
Data Retrieval provides the interface between the data repository and users.
1.4.1.4 Applicable Standards
Archived Data should be standardized to at least a minimum level by following all existing data standards. These include, but are not limited to:
1.4.1.5 Privacy
Permanent or temporary storage of data within the systems to support the Archived Data User Service should preclude the possibility of identifying or tracking either individual citizens or private firms and should follow the ITS Privacy Principles developed by ITS America ("Fair Information and Privacy Principles"). This means that even in the case where unprocessed data (i.e., data received directly from collection sources) are archived, privacy principles should be strictly followed. Identifiers of individual citizens or private firms should be stripped from all data before archiving unless full disclosure of the intended use is made and informed consent is obtained. Unique system-developed identifiers that do not allow identification of individual citizens or private firms may be assigned to stored data.
1.4.2 Vision
In the near term, ADUS should be incorporated into the National ITS Architecture to provide all stakeholder agencies with ITS data. ADUS should address the capability of collection and verification of data from local ITS center functions and archiving those data into a master historical database repository. The archive should be capable of providing database products to ITS agencies and other stakeholder agencies. Stakeholders may request user-defined data products. ADUS should accept transportation data inputs from stakeholders.
Ideally the near-term ITS Historical Data Archive will be a transportation data warehouse. The archives will receive and archive all incoming ITS Operational data. Later, voluminous real-time data will be stored in accordance with the local historical archiving practices. The warehouse would provide all data users with data archiving and data products toolboxes for predefining their archiving and retrieval processes. The data archiving toolbox will include data aggregation, data exploration, and data fusion technology to enable the users to predefine the gathering and archiving of their data. The data products toolbox will provide analysis, modeling, scheduling, and report writing processes to enable the users to predefine their desired data products.
In the mid-term, archived data should be fully automated and able to support local, state and federal DOT data archiving requirements. Metadata and meta-attributes of all ITS-generated data should be standardized. Demand management may evolve to real-time traffic demand management and use ADUS databases as the source for the predictive traffic demand model. Information Service Providers (ISP) may utilize archived data to provide travelers with route guidance and travel planning and to provide traveler planning data to ITS. Many planning functions may be automated, including data analyses and reports.
In the long term, ITS Traffic Management Systems may be fully integrated with the Demand Management and Route Guidance User Services. ADUS may provide a fully automated central ITS data warehouse; capable of collecting, verifying, and archiving ITS data at local, state, and Federal agencies. ADUS may provide agency-unique ITS data products to ITS centers and designated users. The ADUS data warehouse may be integrated with user functions so that the data products are provided in a seamless fashion. For example, data from the warehouse may be sent directly to models rather than being subjected to intervention by user analysts.
1.5 Technologies
The Archived Data User Service is essentially embodied in an information management system. Therefore, several information technologies apply to its development. System designers should make full use of these technologies when implementing the Archived Data User Service. These include, but are not limited to: (1) relational, distributed, and object-oriented data base design (among others); (2) data warehousing; (3) data mining; (4) expert systems, and (5) geographic information systems (GIS). Relational, distributed, and object-oriented designs and data warehousing are information technology concepts that aid in the management and retrieval of data. Data mining can be applied to mine specific nuggets of critical source data required for analysis to identify unusual trends in transportation networks, that might be buried in the huge volume of information that exists in transportation information systems. Expert systems can be used as an aid in data quality control. GIS is a technology that can be used for managing and displaying data generated by ITS.
1.6 Potential Costs and Benefits
1.6.1 Potential Costs
Implementing the Archived Data User Service will involve development, operation, and maintenance costs. Operating agencies such as state DOTs and local traffic engineering agencies are the purveyors of ITS. As an information management system, an implemented archived data system requires data base administration, backup procedures, routine operation of quality control and summarization programs, responding to special users, maintaining existing code, and developing new code for new applications. Even if the data "owners" are convinced of the data's value, staff resources for operation and maintenance may be slim. In fact, operating costs for the data archive may be higher over time than the initial capital costs of constructing it. Moreover, because there is little precedent in the field, the costs of building, operating, and maintaining an ITS archival system are largely unknown. Finally, the distribution of costs among stakeholder groups, including the formation of public/private partnerships, could help defray costs to any one group. It is therefore crucial that these costs be explicitly addressed in future funding of ITS deployments. System owners and operators should also be free to pursue innovative approaches to paying for archived data systems, including the use of non-ITS funds (e.g., state and local planning allocations).
1.6.2 Benefits
For the most part, data generated by ITS are similar to data collected by other transportation agencies via traditional means (e.g., traffic counts), but ITS-related data are collected continuously and at a very detailed level. For example, roadway surveillance data can be used in many stakeholder applications, including development and calibration of travel demand forecasting and simulation models; congestion monitoring; transit route and schedule planning; intermodal facilities planning; and air quality modeling. Users will benefit from ITS-generated data because those data will supplement, and in some cases replace, existing data collection programs. Example benefits to specific stakeholders were outlined in Section 1.3. In general, these benefits relate to:
a. Removal of temporal sampling bias from estimates and allowing the study of variability. Nearly all of the data currently collected for planning, operations, administration, and research applications are through the use of sample surveys (e.g., household travel surveys, short-duration traffic counts). Although attempts are made to adjust or expand the sample, the procedures are imperfect. With continuous data, there is no need to perform adjustments to control sample bias. (Equipment or nonresponse errors are still present, though). Further, continuous data allow direct assessment of variability, which is becoming an important factor in the study of personal travel habits and the effect of extreme events (e.g., days with very high volumes or severe incidents).
b. Provision of detailed data needed to meet emerging requirements and for use in new modeling procedures. The next generation of travel demand forecasting models and air quality models (modal emission models) will operate at a much higher level of granularity than existing models. Traditional data sources are barely adequate for existing models and it will be extremely difficult to support the next generation of models with them. Much data generated by ITS are collected at the levels of detail necessary to support these models. For example, roadway surveillance data (volumes, speeds, and occupancies) are typically reported every 20 seconds and GPS-instrumented vehicles can report positions and activity at time intervals as small as one second. Also, GPS-derived locations can pinpoint incident locations to within a few meters. This level of detail will be required for the input and calibration data used by the new models.
c. Supplementing, and in some cases replacing, existing data collection programs. Many state DOTs have extensive traffic count programs that entail the collection of data with portable equipment. ITS roadway surveillance equipment can provide this same function automatically and continuously and avoids the problems associated with installing and removing portable equipment on heavily traveled urban roadways. In the transit area, electronic fare collection systems can circumvent the need to conduct periodic boarding surveys. In the freight planning area, ITS technologies may permit tracking commodity flows without the need for special surveys. In the safety area, accurate crash location identification will aid the cross-linking of highway attributes to crashes.
d. Stimulating the support of other users for ITS initiatives. If groups besides those involved in ITS development see value in data generated by ITS, they will be inclined to learn more about ITS and to support deployment. In other words, mutual interest in data generated by ITS will stimulate cooperation among stakeholders. This could prove to be extremely valuable in the "main streaming" of ITS into standard transportation practice, particularly among transportation planners.
e. Complementing the integration of transportation systems in general. To a very large degree, integration of ITS components can be viewed as the sharing and use of data between individual ITS components, usually in real- or near real-time. (For example, the transfer of freeway surveillance data for purposes beyond control of the freeway such as for traffic signal control and traveler information is a form of integration.) For integration to occur, system linkages must be established. It is precisely these linkages that can be tapped to archive data under this user service. Therefore, the Archived Data User Service can be thought of as another form of ITS integration -- the linking of ITS components with the rest of the transportation world.
f. Evaluating and monitoring the benefits and impacts of ITS products and services. ITS-generated data can provide a valuable basis for evaluating the deployment of ITS within an area.
As the focus of transportation policy shifts away from large-scale, long-range capital improvements and toward better management of existing facilities, ITS-generated data can support the creation and use of new system performance measures that are required to meet this new paradigm. Also, as states and metropolitan areas develop regional architectures based on the National ITS Architecture, the specification of this user service will foster consideration of the data archiving function. Finally, as data generated by ITS are used more frequently for nonreal-time purposes, it is likely that additional uses not currently foreseen will emerge.
1.7 Assessment of Roles
1.7.1 Public Benefit
The potential for public benefit is related to the myriad uses of archived data. Because such a wide variety of users could take advantage of archived ITS data, both planning and operation of transportation facilities can be improved. Further, improved data for performing transportation research and evaluation of transportation programs will lead to more cost-effective expenditure of public funds.
1.7.2 U.S. DOT Role in Developing Service
The role of U.S. DOT in developing ADUS will be significant. Simply defining the functions and the data that should be maintained in the Archived Data User Service is insufficient to achieve successful implementation. Therefore, direct involvement of the U.S. DOT will promote development and use of the Archived Data User Service in the field. This involvement can take several forms:
1.7.2.1 Data Quality Control and Editing
Data management goals for ITS are the unambiguous interchange and reuse of data and information throughout all functional-areas of ITS. The approach for achieving this goal is the standardization of meta-attributes within ITS Data Dictionaries that facilitate data interchange within ITS and between ITS and external systems. Meeting the goal also requires an ITS Data Registry as a particular kind of a logically centralized repository for all ITS data elements and other data concepts that have been formally specified in the National ITS Architecture. The standardization of ITS communication message protocols will result in the goal of unambiguous data interchange. These standardization efforts are currently in progress and seeking consensus within the ITS standards domain. The ADUS stakeholder agencies need to participate in the standardization procedures to define any unique data concepts within their domain.
"Accepted Practice" procedures for performing quality control and editing on Archived Data User Service data should be developed. Because ITS operations are a new phenomenon, little is known about how to identify and adjust questionable data received from field equipment. Data quality is a particular concern for those data elements that are aggregations of raw data: what should be the "rules" for handling not only questionable but missing data in the aggregation process. An ongoing research effort jointly funded by several states is examining quality control and editing procedures for vehicle classification and weight data. Consideration should be given to performing similar studies for other forms of traffic surveillance data (e.g., volumes, speeds, densities) and also for other types of archived data as well. Once the procedures are established, there is an additional need to develop automated tools to facilitate quality control and editing.
1.7.2.2. Data Analysis Techniques
For some data, accepted procedures would be used to summarize archived data generated by ITS for the convenience stakeholders and would promote the use of the Archived Data User Service. Demonstration of analytic methods (including graphical displays) will be extremely valuable to stakeholder groups. This is especially important because the sheer volume of data may be daunting to some groups not accustomed to working with large data sets. Assistance could take many forms: providing software and sample data, case studies of how other stakeholders use data, or documenting analysis techniques for specific applications (e.g., congestion monitoring, bus route planning, TDF model input preparation).
1.7.2.3 Coordination With Other Efforts
Coordination with ongoing data dictionary efforts is crucial to the future development of the Archived Data User Service. There are several ongoing efforts to develop data dictionaries for various sub-components of the National ITS Architecture (e.g., Traffic Management Data Dictionary). Because these efforts are specifying data structures, they are highly relevant for the Archived Data User Service, and it would be advantageous if the users identified here should have input to these efforts. Consideration should also be given to developing an Archived ITS Data Dictionary in accordance with the guidance provided by this document and the input of users. The endeavor of creating a data dictionary will enable users and system architects to resolve many of the technical issues raised here.
Similarly, the needs of stakeholder groups can be represented through participation in the standards development process that may have an impact on the relevant data. Other efforts have the potential for influencing the nature and compatibility of data used in ITS technologies and are relevant to the Archived Data User Service.
The Archived Data User Service program should be integrated into other Federal, state, and local data collection programs. Although the Archived Data User Service is only one source of data for users, it can be used to supplement or replace many existing data programs. Examples include submittals of certain data to the Highway Performance Monitoring System and statewide traffic monitoring. Full consideration should be given to how the Archived Data User Service fits into a comprehensive data collection program, including data sharing and standards.
1.7.2.4 Training and Outreach
Training the various users in each others' needs is seen as an ongoing requirement. Personnel not directly involved in ITS operations may have a limited working knowledge of the National ITS Architecture and of ITS in general. Likewise, personnel who come from a systems engineering background rather than a transportation background do not typically have an appreciation for the breadth of traditional transportation functions. Education and outreach activities need to be increased for all associated professionals. Several immediate options are available including: training under the Professional Capacity Building effort and recruitment of all users for Regional Architecture Workshops. Finally, key decision makers will need to be convinced of the value of developing an ITS archive. It is therefore crucial that the benefits be clearly articulated and presented to them, perhaps through the use of case studies.
1.7.2.5 Field Demonstration
A concentrated field effort to demonstrate the implementation and use of the Archived Data User Service should be undertaken. Similar to the concept of ITS Field Operational Tests, the Archived Data User Service demonstration would provide a model for how to perform system development as well as how the data may be put to use. A key part of the demonstration would be to document the value of the increased information provided by the Archived Data User Service to local decision making and operations. Full documentation on the institutional issues as well as the technical hurdles to developing such a system must also be addressed.
1.7.2.6 Institutional and Legal
At the Federal level, U.S. DOT will take an active role in fostering the necessary institutional arrangements required for deployment of the ADUS. Successful implementation will require resolution of numerous issues that have been identified to date, including: development, operating, and maintenance costs; system access; ownership; data quality; data management; data and communications standards; privacy concerns; data analysis; coordination with other data collection efforts; liability; confidentiality of privately collected data; incremental and uncoordinated ITS deployments; retrofitting vs. new development of systems; conformance with metric conversion standards; and training and outreach.
1.7.2.7 Deployment
The U.S. DOT role in deployment of this service is to highly encourage adoption and use of advanced systems by states and local government agencies.
1.8 Milestones and Activities
The key near-term activities are: