California Partners for Advanced Transportation Technology

Dynamic All-Red Extension (DARE) has recently attracted research interest as a non-traditional intersection collision avoidance method. We propose a probabilistic model to predict red light running (RLR) hazard for dynamic all-red extension system. The RLR hazard, quantified by a predicted encroachment time, has contributory factors including the speed, distance and car-following status of the violator and the empirical distribution of the entry time of conflict traffic. An offline data analysis procedure is developed to set the parameters for RLR hazard prediction. Online-wise, a two-dimensional normal model is developed to predict the vehicle’s stop-go maneuver based on speeds at advanced detectors. Additionally, unlike most prediction models which are designed to minimize mean errors, our model identifies two types of errors, namely the false alarm and missed report. The capability of distinguishing these two types of errors is crucial to the effectiveness of dynamic systems. To quantify the trade-off between these two types of errors in the system design, a system operating characteristics (SOC) function is then defined. Performance of the proposed model and its prediction algorithm is evaluated using data collected from a field intersection. At a false alarm rate of 5%, the algorithm reach a correct detection rate of over 65% to over 90% for various legs of the test intersection. Performance evaluation results showed that the proposed models and algorithms within the DARE framework can effectively detect the RLR hazards.