This paper proposes a new technique for freeway incident detection using a constructive probabilistic neural network (CPNN). The CPNN incorporates a clustering technique with an automated training process. The work reported in this paper was conducted on Ayer Rajah Expressway (AYE) in Singapore for incident detection model development, and subsequently on I-880 freeway in California, for model adaptation. The model developed achieved incident detection performance of 92% detection rate and 0.81% false alarm rate on AYE, and 91.30% detection rate and 0.27% false alarm rate on I-880 freeway using the proposed adaptation method. In addition to its superior performance, the network pruning method employed facilitated model size reduction by a factor of 11 compared to a conventional probabilistic neural network. A more impressive size reduction by a factor of 50 was achieved after the model was adapted for the new site. The results from this paper suggest that CPNN is a better adaptive classifier for incident detection problem with a changing site traffic environment.


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    Title :

    Classification of freeway traffic patterns for incident detection using constructive probabilistic neural networks


    Contributors:
    Xin Jin (author) / Srinivasan, D. (author) / Cheu, Ruey-Long (author)

    Published in:

    Publication date :

    2001


    Size :

    15 Seiten, 17 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English





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