Fast Automatic Incident Detection on Urban and Rural Freeways Using Wavelet Energy Algorithm

Abstract

A comprehensive evaluation is presented of the single-station wavelet energy neural network freeway incident-detection algorithm of Karim and Adeli. Quantitative performance measures of detection rate, false alarm rate, and detection time as well as the qualitative measure of portability are investigated for both urban and rural freeway conditions. Further, the performance of the algorithm is compared with that of California algorithm 8. This research demonstrates the portability of the wavelet energy algorithm and its excellent performance for urban freeways across a wide range of traffic flow and roadway geometry conditions, regardless of the density of the loop detectors. Rural freeways present additional challenges in that flow rates are low and detector stations are spaced further apart. Considering the difficulty in automatic detection of incidents on rural freeways, the new wavelet energy algorithm performs well on such freeways. The algorithm is fast as it detects an incident on urban freeways in less than 2 min and on rural freeways in less than 3 min.