Real-time traffic flow management has recently emerged as one of the promising approaches to alleviating congestion. This approach uses real-time and predicted traffic information to develop routing strategies that attempt to optimize the performance of the highway network. A survey of existing approaches to real-time traffic management indicated that they suffer from several limitations. In an attempt to overcome these, the authors developed an architecture for a routing decision support system (DSS) based on two emerging artificial intelligence paradigms: case-based reasoning and stochastic search algorithms. This architecture promises to allow the routing DSS to ( a) process information in real time, ( b) learn from experience, ( c) handle the uncertainty associated with predicting traffic conditions and driver behavior, ( d) balance the trade-off between accuracy and efficiency, and ( e) deal with missing and incomplete data problems.


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    Title :

    Artificial Intelligence-Based Architecture for Real-Time Traffic Flow Management


    Additional title:

    Transportation Research Record


    Contributors:


    Publication date :

    1998-01-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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