INTEGRATED APPROACH TO VEHICLE ROUTING AND CONGESTION PREDICTION FOR REAL-TIME DRIVER GUIDANCE

Abstract

The generation and dissemination of driver guidance that can be used for real-time diversion of traffic are expected to be implemented through the use of real-time traveler information systems. To implement these functions, a system structure consisting of a surveillance module, a congestion prediction module, and a control and routing (CAR) module is proposed, with the focus on the approaches that may be used for congestion prediction and the strategies that may form the basis for routing. It is argued that a congestion prediction capability is critical for the effectiveness of an on-line traveler information system. Such a capability is required to accurately forecast traffic conditions that may exist in the near future. The use of a dynamic traffic assignment model for congestion prediction is suggested. Such a model consists of dynamic driver behavior and network performance modules as well as origin-destination updating capability. Alternatively, statistical time-series methods may be necessary to generate predictions of future traffic conditions. The advantages and difficulties of adopting either approach are discussed. The predicted congestion information is passed to the CAR module to develop diversion strategies to alleviate both recurring and nonrecurring congestion. The role of routing strategies and update frequency in determining guidance effectiveness is discussed.

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