This study proposes a flow-rain-dependent algorithm for the automatic detection of traffic incidents under both rain and no-rain conditions. To overcome the shortcomings of discrete detection thresholds in previous automatic incident detection (AID) algorithms, continuous detection thresholds are used. These thresholds are generated by calibrated generalized detection threshold functions, in which both pre-incident traffic and conditions in rain are explicitly modeled. The volume/capacity ratio, which can better describe the degree of congestion, is used to indicate the pre-incident traffic conditions in the proposed algorithm. A case study is carried out on a territory-wide road network in Hong Kong to demonstrate the performance of the proposed AID algorithm. Traffic data for journey time estimation is collected from the Hong Kong urban road network. The results show that incorporating the rain effect when determining the detection threshold can improve the overall performance of traffic incident detection.
2016 Eastern Asia Society for Transportation Studies