CHAPTER 1: INTRODUCTION

1.1 BACKGROUND

Intelligent Transportation Systems (ITS) collect and use information about surface transportation conditions to improve system performance in such areas as safety, mobility, efficiency, and environment. Typically, ITS generates massive amounts of performance and condition data that characterize how the system is operating in real-time or near real-time. These data are used primarily by transportation agencies to implement operation and travel management strategies such as ramp metering, incident management, signal control, provision of traveler information, and commercial vehicle operations.

However, the increasing deployment of ITS and amount and variety of ITS-generated data throughout the Nation also offer great potential for longer-term transportation management purposes involving such tools as transportation trends, analysis, and predictive models. ITS-generated data are of the same basic nature as the data traditionally used in these applications, but ITS-generated data are typically collected at much higher levels of geographic and temporal resolution. These increased levels of resolution cannot only provide data for existing applications but also will enable the development of more sophisticated applications.

As ITS matures, there is a growing recognition by the transportation profession that the data generated by ITS can be used for multiple purposes beyond ITS-related control strategies. Recognizing this potential, the Federal Highway Administration embarked on a concentrated effort to advance the state of the practice in archiving ITS-generated data for future uses. The first activity was the addition of the Archived Data User Service (ADUS) to the National ITS Architecture in September 1999. During the process of incorporating ADUS into the Architecture, stakeholders identified a series of applications to which ITS-generated data are relevant, including:

• Transportation Planning - congestion management, corridor studies, long-range plan development (especially input to travel demand forecasting models)
• Traffic Operations - evaluation of control strategies, sort-range planning of operational strategies
• Transit Operations and Planning - routing and scheduling
• Air Quality Analysis - inputs to emissions models
• Safety Planning - crash verification and exposure estimation.

In addition to these applications, the Architecture paid particular attention to the ability of ITS to supply data to traditional transportation information systems, which are used to support all of the functions mentioned above. The Architecture defines a specific "equipment package" to deal with this function:

Government Reporting Systems Support
This equipment package selects and formats data residing in an ITS archive to facilitate local, state, and federal government data reporting requirements.

Prepare Government Reporting Inputs
This process shall support the preparation of inputs to reporting systems of the federal or state governments that require data from the ITS archive. This process shall respond to requests from the Government Reporting Systems terminator for data from the archive and generate the request in a form understood by the Manage Archive function. The data and any metadata necessary shall be returned from the Manage Archive function. This process shall receive the data and format it as requested and send it to the Government Reporting Systems terminator where it may be combined with other data before final submission.

1.2 STUDY PURPOSE

The overall objective of this study is to determine the potential of ITS to supply data to traditional transportation data systems by examining data element definitions from both realms in detail. Specifically, data elements in terms of both their basic definition and their valid values are examined to identify two types of matches:

• "Direct" matches - in which there is a 1:1 correspondence between an ITS data element and its traditional counterpart or the traditional data element can be directly derived from the ITS data element (the latter condition allows for ITS data elements to have a more detailed definition).
• "Near" matches - in which the data element definitions are close but not exact, and some form of degree of translation is required in order to derive the traditional data element from its ITS counterpart.

The main purpose of this exercise is to identify data elements that need to be reconciled ("harmonized") by the data owners. In addition to pointing out the specifics of the required harmonization, a discussion of the how ITS data can improve the timeliness of data submittals and overall quality of traditional data systems is included.

1.3 STUDY APPROACH

The approach taken was to obtain data dictionaries for ITS and traditional sources and match data elements one-by-one, looking for commonalities. The ITS sources considered for this study were:

• National ITS Architecture (NA) - A data dictionary is included as part of the "logical architecture". However, the data specifications are very general and meant to support the general data flows that tie together the Architecture's subsystems. This makes the Architecture less useful for this project than the other three "primary" data dictionaries listed below. In theory, the Architecture serves as a basis for the more detailed data dictionaries, which are being developed as standards. The Architecture itself is too general to be considered a standard.
• Traffic Management Data Dictionary (TMDD) developed by the Institute of Transportation Engineers (ITE).
• P1512 Incident Management Data Dictionary (P1512) being developed by the Institute of Electrical and Electronics Engineers (IEEE).
• Advanced Traveler Information System (ATIS) Data Dictionary being developed by the Society of Automotive Engineers (SAE).
• Commercial Vehicle Information Exchange Window (CVIEW) developed as part of the Commercial Vehicle Information Systems and Networks (CVISN) by the Federal Motor Carrier Safety Administration (FMCSA).
• National Transportation Communications for ITS Protocol (NTCIP) object definitions for Data Collection and Monitoring (DCM) devices developed by the working group.

The three primary data dictionaries are comprised of data elements used almost exclusively to construct the real-time message sets used to support traffic management center (TMC) operations, incident response, and traveler information, respectively. Therefore, they do not necessarily reflect the full amount of data available from ITS systems but rather only those data deemed to be necessary in messages.

The traditional transportation data systems examined were those maintained by the U.S. Department of Transportation (USDOT). Data dictionaries for the following government reporting systems were obtained:

• Highway Performance Monitoring System (HPMS)
• Traffic Monitoring Guide (TMG)
• Highway Safety Information System (HSIS)
• National Bridge Inventory (NBI)
• National Transit Database (NTD)
• Fatality Analysis Reporting System (FARS)
• General Estimates System (GES)
• Motor Carrier Management Information System (MCMIS)
• National Governors' Association (NGA) Truck Crash Data Elements
• Hazardous Materials Incident Reporting System (HMIRS)
• Grade Crossing Inventory System (GCIS)
• Transportation Surveys
  • Nationwide Personal Transportation Survey (NPTS)
  • Vehicle Inventory and Use Survey (VIUS)
  • American Travel Survey (ATS)
• EPA Air Quality Models

The last two entries in this list require explanation. After cursory review of their data dictionaries, the surveys were dropped from consideration because ITS data are not currently used to collect "snapshot" information on individual travelers (NPTS and ATS) and trucks (VIUS). Rather, ITS data in current and near-term deployments are geared to monitoring system conditions. Some of the P1512 and ATIS Data Dictionaries do collect data on individual trucks (those involved in hazmat crashes and issuing MayDay communications), but these data do not correspond to the data in the surveys.

EPA Air Quality Models, while not strictly a government reporting system, were deemed important enough to consider in the scope of the project. Specifically, the data inputs to the MOBILE6 vehicle emissions model were examined with regard to ITS data sources. The study proceeded by transferring all the data dictionaries into an Excel spreadsheet and developing a macro procedure for identifying matches and near matches. However, it became clear that the procedure was not capturing all of the possible matches - too many matches were going undetected. Therefore, it was decided to perform the matching manually, which proved to be very time consuming.