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Title: Evaluation of Adaptive Neural Network Models for Freeway Incident Detection
Authors: Srinivasan, D. 
Jin, X.
Cheu, R.L. 
Keywords: Adaptive neural networks (ANNs)
Incident detection
Multilayer feed-forward neural network (MLFNN)
Network pruning
Probabilistic neural network (PNN)
Issue Date: Mar-2004
Citation: Srinivasan, D., Jin, X., Cheu, R.L. (2004-03). Evaluation of Adaptive Neural Network Models for Freeway Incident Detection. IEEE Transactions on Intelligent Transportation Systems 5 (1) : 1-11. ScholarBank@NUS Repository.
Abstract: Automated incident detection is an essential component of a modern freeway traffic monitoring system. A number of neural network (NN)-based incident detection models have been tested independently over the past decade. This paper evaluates the adaptability of three promising NN models for this problem: a multilayer feed-forward NN (MLFNN), a basic probabilistic NN (BPNN) and a constructive probabilistic NN (CPNN). These three models have been developed on an original freeway site in Singapore and then adapted to a new freeway site in California. In addition to their incident detection performance, their ability to adapt to new freeway sites, and network sizes have also been compared. A novel updating scheme has been used for adjustment of smoothing parameter of the BPNN. Results of this study show that the MLFNN model has the best incident detection performance at the development site while CPNN model has the best performance after model adaptation at the new site. In addition, the adaptation method for CPNN model is less laborious. The efficient network pruning procedure for the CPNN network resulted in a smaller network size, making it easier to implement it for real-time application. The results suggest that CPNN model has good potential for application in an operational automatic incident detection system for freeways.
Source Title: IEEE Transactions on Intelligent Transportation Systems
ISSN: 15249050
DOI: 10.1109/TITS.2004.825084
Appears in Collections:Staff Publications

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