The application of artificial intelligence (AI) technology to advanced traffic management systems has been a focus at Georgia Tech for several years. Specifically, an advanced traffic management system (ATMS) blackboard architecture has been developed as the core for AI traffic management research. Knowledge sources in the system address problems in traffic control, monitoring, congestion prediction, adaptive communication, and incident management. The knowledge sources exploit algorithm, production rule, amd neural network representations to solve individual traffic management problems that appear on the blackboard data structure. The resulting traffic management decisions are then implemented and evaluated through simulation. This paper presents the knowledge-based ATMS system with emphasis on knowledge source interaction. Results of system operation and performance are shown using a traffic simulation model of the city of Atlanta, venue for the 1996 summer Olympic Games.


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    Title :

    A distributed blackboard system for traffic control


    Additional title:

    Ein verteiltes Blackboard-System für die Verkehrssteuerung


    Contributors:


    Publication date :

    1994


    Size :

    8 Seiten, 4 Bilder, 8 Quellen



    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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