This study presents a warning information system based on an innovate methodology to estimate accident likelihood in real time. Bayesian modeling approach implemented by the probabilistic neural network (PNN) is conducted to identify hazardous traffic conditions leading to potential accident occurrence. The proposed system displays warning signs to call drivers' attention for safer and careful driving once hazardous traffic conditions are observed by evaluating accident likelihood. It is believed that the proposed system to produce effective warning information for real-time safety enhancement could be a valuable tool to highway users and operators.


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    Title :

    Real-time hazardous traffic condition warning system: framework and evaluation


    Contributors:
    Cheol Oh, (author) / Jun-Seok Oh, (author) / Ritchie, S.G. (author)


    Publication date :

    2005-09-01


    Size :

    338067 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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