Event-Based Modeling of Driver Yielding Behavior at Unsignalized Crosswalks

This research explores factors associated with driver yielding behavior at unsignalized pedestrian crossings and develops predictive models for yielding by using logistic regression. It considers the effect of variables describing driver attributes, pedestrian characteristics, and concurrent conditions at the crosswalk on yield response. Special consideration is given to “vehicle dynamics constraints” that form a threshold for the potential to yield. Similarities to driver reaction in response to the amber indication at a signalized intersection are identified. The logit models were developed from data collected at two unsignalized midblock crosswalks in North Carolina. The data include before and after observations of two pedestrian safety treatments, an in-street pedestrian crossing sign and pedestrian-actuated in-roadway warning lights. The analysis suggests that drivers are more likely to yield to assertive pedestrians who walk briskly in their approach to the crosswalk. In turn, the yield probability is reduced with higher speeds, with deceleration rates, and if vehicles are traveling in platoons. The treatment effects proved to be significant and increased the propensity of drivers to yield, but their effectiveness may be dependent on whether the pedestrian activates the treatment. The results of this research provide new insights into the complex interaction of pedestrians and vehicles at unsignalized intersections and have implications for future work toward predictive models for driver yielding behavior. The developed logit models can provide the basis for representing driver yielding behavior in a microsimulation modeling environment.