Development of Decision Support Systems for Real-time Freeway Traffic Routing: Volume II

Report No: 99-R10

Published in 1998

About the report:

Real-time traffic flow routing is a promising approach to alleviating congestion. Existing approaches to developing real-time routing strategies, however, have limitations. This study explored the potential for using case-based reasoning (CBR), an emerging artificial intelligence paradigm, to overcome such limitations. CBR solves new problems by reusing solutions of similar past problems. To illustrate the feasibility of the approach, the research team developed and evaluated a prototype CBR routing system for the interstate network in Hampton Roads, Virginia. They generated cases for building the system's case-base using a heuristic dynamic traffic assignment (DTA) model designed for the region. Using a second set of cases, the research team evaluated the performance of the prototype system by comparing its solutions with those of the DTA model. The research team found that CBR has the potential to overcome many of the limitations to existing approaches to real-time routing and a CBR routing system is capable of producing high-quality solutions with reasonable a case-base size. In addition, the research team found that real-time traffic flow routing will likely lead to significant user cost savings.

Disclaimer Statement:The contents of this report reflect the views of the author(s), who is responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the Virginia Department of Transportation, the Commonwealth Transportation Board, or the Federal Highway Administration. This report does not constitute a standard, specification, or regulation. Any inclusion of manufacturer names, trade names, or trademarks is for identification purposes only and is not to be considered an endorsement.

Authors

Other Authors

Adel W. Sadek, Brian L. Smith, Michael J. Demetsky, Ph.D., Catherine C. McGhee, P.E.

Last updated: December 12, 2023

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