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

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.