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Parameter Decision for Efficient Automatic Incident Detection System

Incidents on the freeway disrupt traffic flow and the cost of delay caused by incidents is significant. To reduce the impact of an incident a traffic management center needs to quickly detect and remove it from the freeway. Quick and efficient automatic incident detection has been a main goal of the transportation research for many years. Also many algorithms based on loop detector data have been developed tested for the Automatic Incident Detection (AID). However, many of them have a limited success in their overall performance in terms of detection rate, false alarm rate, and the mean time to detect an incident. Recently, the neural network models are known as the one of the popular and efficient approach for real-time automatic incident detection and many researches have shown that the neural network models were much more efficient than various other previous models. More importantly, Support Vector Machine (SVM) which is based on the statistical learning theory, has been shown that it is more efficient than the most popular neural network model, Backpropagation. The important element in the performance of SVM and the neural network is input vectors. However, there has been little research on the performance according to the attribute of input vectors. Normally, the three parameters of volume, speed, occupancy from the field detectors are available for the application of the SVM. The performance of automatic incident detection system is effected by the training data on the number of detectors and time slices. The purpose of this research is to determine a proper number of time slices in order to provide the best performance in automatic incident detection system. The experiments have been done with real world freeway data and the results showed that the 8 time slices could provide the best prediction performance in terms of DR (Detection Rate) and FAR (False Alarm Rate).