Using Detailed Signal and Detector Data to Investigate Intersection Crash Causation

Project summary:

Traffic crashes may not always result in severe or fatal injuries, but they can still have nontrivial impacts on system performance, particularly during heavy traffic conditions. One way toward reducing the frequency of such incidents is to first identify the necessary circumstances that resulted in the collision. However, road crashes--particularly intersection-related crashes--are complex phenomena and often result from different combinations of causal factors. Recently, methods for recording high-resolution arterial traffic data have been developed, and it is important for traffic safety engineers to explore such high-resolution data to understand the causes of crashes. In this research, one such integrated event-based system, known as SMART Signal, was used to identify the events leading to a crash or a potential crash and illuminate the mechanisms by which traffic conditions and driver decisions interact to produce those events. SMART Signal collects and stores detailed loop detector and signal activity. Two specific event types, a signal-violation crash and vehicle-pedestrian crash, were evaluated. For the signal-violation crash study, SMART Signal data were used to identify the incident and the vehicles involved in the crash, which showed how high-resolution data could support a traditional reconstruction of this crash. For vehicle-pedestrian interactions, detector and signal activity data were used to predict pedestrian crash risk in the absence of clearance intervals at three signalized intersections. A simulation-based method was used to first estimate crash probabilities, then a counterfactual approach was used to calculate the probability of the absence of the all-red phase as a necessary condition for the crash occurrence. This provided an alternate estimate of crash-reduction factors for the all-red phase.