Traffic Safety Evaluation Based on Vision and Signal Timing Data

Abstract

With the advancement of recent image processing technologies numerous imaging applications have been developed to collect traffic information. In this study, image processing technology is applied to roadway safety to detect and measure the conflict between pedestrians and vehicular traffic and investigate the correlations between the conflict and signal timing data. By using an image processing technique, pedestrian and vehicular movements are detected and the distance between these two are calculated to detect conflicts between them. In this study, Machine Learning techniques are utilized to facilitate more accurate and efficient detection. It is assumed that the intersection is accident-prone if conflicts between pedestrian and vehicular traffic appear more frequently than other intersections and correlations exist depending on the status of signal timing, for example at the end of green signal for a particular movement. This approach is expected to identify accident-prone intersections without actually experiencing crashes; therefore, this would potentially reduce social costs associated with traffic accidents.