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|Title:||Classification of freeway traffic patterns for incident detection using constructive probabilistic neural networks||Authors:||Jin, X.
Probabilistic neural network (PNN)
|Issue Date:||Sep-2001||Citation:||Jin, X., Srinivasan, D., Cheu, R.L. (2001-09). Classification of freeway traffic patterns for incident detection using constructive probabilistic neural networks. IEEE Transactions on Neural Networks 12 (5) : 1173-1187. ScholarBank@NUS Repository. https://doi.org/10.1109/72.950145||Abstract:||This paper proposes a new technique for freeway incident detection using a constructive probabilistic neural network. Incident detection is one of the important components in Intelligent Transportation Systems. It identifies traffic abnormality based on input signals obtained from traffic flow sensors. To date, the development of Intelligent Transportation Systems has urged the researchers in incident detection area to explore new techniques with high adaptability to changing site traffic characteristics. Recent works show that the basic probabilistic neural network is one of the best choices for this purpose. However, it suffers from high memory requirement and the lack of practical model adaptation and network pruning methods. Recent work in probabilistic neural network (PNN) research has led to the development of constructive probabilistic neural network (CPNN), which incorporates a clustering technique with an automated training process. CPNN has been presented in this paper to solve two problems in traffic network incident detection. 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 developed model achieved incident detection performance of 92.00% 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 here facilitated remarkable 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.||Source Title:||IEEE Transactions on Neural Networks||URI:||http://scholarbank.nus.edu.sg/handle/10635/50675||ISSN:||10459227||DOI:||10.1109/72.950145|
|Appears in Collections:||Staff Publications|
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