Evaluation of Adaptive Neural Network Models for Freeway Incident Detection

Automated incident detection is an essential component of a modern freeway traffic monitoring system. A number of neural network-based incident detection models have been tested independently over the past decade. This paper evaluates the adaptability of three promising neural network models for this problem: multi-layer feed-forward neural network (MLF), basic probabilistic neural network (BPNN) and constructive probabilistic neural network (CPNN). These three models have been developed on an original freeway site in Singapore and then adapted to a new freeway site in California. Apart from their incident detection performances, their adaptation strategies and network sizes have also been compared. Results of this study show that the MLF 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 relatively more automatic. 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 the highest potential for use in an operational automatic incident detection system for freeways