Department Overview  

Copyright © 2001
Department of Transportation Studies
Texas Southern University

Using Real-time traffic data for transportation planning

Principal Investigator:    Dr. Lei Yu

Collaborator:    Dr. Hani Mahmassani, University of Texas at Austin

Sponsoring Agency:    Texas Department of Transportation 0-4054

Period:    September 1, 2000 ~ August 31, 2002

Research Objectives:

The problem addressed in this study therefore consists of characterizing typical real-time data streams to ATMS centers, identifying uses of that information to support transportation planning activities over varying time horizons (short, medium and longer term), and devising strategies for the systematic processing of that data for data warehousing and subsequent ready retrieval over time to meet various information needs, current and future, by transportation planning entities. The goal of this research is to maximize the usefulness of ITS-generated data, in the context of other available data sources, to facilitate and improve transportation planning methods, processes and activities performed in practice.  This goal entails the following objectives:

1. Identify in detail and characterize the real-time data available from advanced traffic management centers (TMC’s) such as TRANSGUIDE and TRANSTAR;
2. Identify what, and in which way, real-time traffic data may be needed/useful in various transportation planning activities by planning agencies in the selected study areas in Texas;
3. Specify the protocols for transforming ITS-generated data into planning-friendly formats, to reside in database management system for easy retrieval by transportation planning entities. 
4.  Perform selected applications in conjunction with one or two MPO's in Texas to demonstrate use of real-time data for travel demand model calibration and forecasting.

Research Abstract:

Continuing deployment of Intelligent Transportation Systems (ITS) technologies, especially recent state-of-the-art installations and traffic management centers (TMC’s) in Texas metropolitan areas (e.g. San Antonio TRANSGUIDE and Houston TRANSTAR), provide real-time data on prevailing traffic conditions quasi-continuously.  In addition to their intended use for traffic operational purposes, it is possible and desirable to use these data for various transportation planning purposes.  Real-time data can be combined with other sources to reduce sampling bias from estimates, and provide time-series information that allows the study of variability of key demand and supply characteristics of the transportation system.  MPO and State transportation planners can employ such real-time traffic data not only to support existing planning and modeling activities, but also to improve current practice, which is often limited by insufficient, inadequate or difficult-to-access data. The realm of such applications is vast, and encompasses virtually all areas of planning practice, including monitoring and diagnosis of the current system state to identify deficiencies and possible improvements (e.g. congestion monitoring), comparison against benchmarks, demand forecasting, system evaluation, policy impact analyses, compliance and conformity analyses, and so on.  One particular area that cuts across these applications is the development, calibration and application of models for travel demand forecasting and system performance assessment, including traffic simulation models. Real-time ITS data can be a particularly rich source of data for the calibration, enhancement and updating of travel demand forecasting models used in practice by MPO’s in Texas (e.g. TRANPLAN, EMME/2 and TransCAD). In addition, such data could facilitate and accelerate the transition to the next generation of demand forecasting tools, especially those that allow explicit modeling of congestion phenomena (e.g. dynamic traffic assignment tools such as DYNASMART-P), in conjunction with the deployment of advanced activity-based travel demand forecasting approaches (e.g. TRANSIMS). However, the nature and sheer amount of data requires careful and judicious processing to appropriate aggregation levels and sampling frames to make the real-time traffic data more meaningful to transportation planners.

 The intent of this research is to develop strategies for maximizing efficient use, retention and processing of real-time traffic data for transportation planning. The research will categorize data available from control centers and related ITS sources, identify possible planning uses by state and local agencies and research organizations in Texas, and define a program of systematic “data mining” to transform information into useful knowledge aimed at meeting transportation planning goals. A framework for mapping available data types and sources onto the needs and requirements of planning agencies will be developed.  The framework will recognize the characteristics of the data in terms of accuracy, reliability, coverage, frequency, temporal and spatial resolution, and so on.  It will also match these against functional requirements for various current and potential planning applications.  Issues of flexible data models, database architecture, and network (web) storage will be addressed to the extent required to meet the functional objectives of the study. A unified standard data interface is envisioned to allow maximum access of the real-time data and its derivatives by different applications and for different uses.  Potential technical and institutional impediments to fully leverage the ITS data for planning purposes will also be addressed.  The work will be conducted in conjunction with actual case study implementation with a selected MPO in Texas. 

Research Information

For further information about the research, please contact Dr. Lei Yu by telephone at (713) 313-7282 or by e-mail at yu_lx@tsu.edu.