New Automatic Incident Detection Algorithm Based on Traffic Data Collected for Journey Time Estimation

Abstract

A new automatic incident detection algorithm based on the available data originally collected for journey time estimation in Hong Kong is proposed in this paper. Instead of installing a greater number of expensive detectors, the proposed algorithm has proved feasible in effective traffic incident detection, with the available data collected by both video traffic detectors and automatic vehicle identification readers. The proposed algorithm extends the previous standard normal deviate algorithm in the aspects of mathematical model, input data, and detection logic. Two new traffic parameters are proposed as indicators of incidents. They are the coefficient of variation of speed at the upstream detector and the correlation coefficient of speeds of two adjacent detectors. Historical traffic and accident data on an urban road in Hong Kong are used for calibration and validation of the proposed algorithm. This proposed algorithm outperforms five existing algorithms based on the available data for journey time estimation in Hong Kong. It is expected that the proposed algorithm could be used for incident detection in cities even when data are collected only for journey time estimation.