Simulation of freeway incident detection using artificial neural networks

A major source of traffic delay in many large urban areas in the United States is non-recurring congestion caused by incidents. In the last several decades, a number of incident detection algorithms have been developed for freeway surveillance and control systems. However, conventional algorithms have generally met with mixed success in terms of performance criteria, such as detection rate, false alarm rate, and the mean time to detect incidents. The need for improved techniques is pressing, particularly with the advent of intelligent vehicle-highway system concepts. These systems will rely heavily on the ability to detect non-recurring traffic congestion automatically. In this paper, the authors hypothesize that spatial and temporal traffic patterns can be recognized and classified by an artificial neural network, and they present an investigation of such models for the automated detection of lane-blocking incidents in a one-mile section of urban freeway. The artificial neural network was trained with data obtained from a microscopic freeway traffic simulation model that was specially calibrated for the actual freeway section. The neural network first classifies the traffic state of the freeway test section into either ‘incident-free’ or ‘incident’ conditions in every 30-second interval. The change in traffic state from incident-free to incident conditions is then used to trigger an incident alarm. Based on the results of an off-line test using simulated data, and comparisons with the well known California incident-detection algorithm and the recently developed modified McMaster algorithm, the results suggest that neural network models have the potential to achieve significant improvements in incident-detection performance.