Heavy Flow-Based Incident Detection Algorithm Using Information From Two Adjacent Detector Stations

Abstract

In heavy traffic flow conditions, vehicles have limited manouverability which affects the magnitude of response to incident-induced traffic disturbances and how fast changes in these traffic variables can signal the occurrence of an incident. Such characteristics are usually used to formulate a loop-based algorithm. A recent study reported that some existing algorithms were not able to maintain a desired level of effectiveness when these algorithms were used to detect incidents with a video-based detector system. Two new video-based automatic incident detection algorithms, the INdividual Detection Evaluation (INDE) and COmbined Detection Evaluation (CODE) algorithms were, therefore, developed for the detection of lane-blocking incidents in heavy traffic flow conditions using the Central Expressway in Singapore as a case study. The algorithms detect incident-induced traffic speed and occupancy disturbances differently: INDE processes information at each individual detector station and CODE processes information at two adjacent detector stations. Both algorithms outperformed existing algorithms commonly used in incident management systems. Of these algorithms, the INDE algorithm raised fewer false alarms and gave slightly faster incident warnings. However, its performance was relatively less consistent when applied to a validation database.