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 using logistic regression. It considers the effect of variables describing driver attributes, pedestrian characteristics and concurrent conditions at the crosswalk on the yield response. Special consideration is given to 'vehicle dynamics constraints' that form a threshold for the potential to yield. Similarities are identified to driver reaction in response to the 'amber' indication at a signalized intersection. The logit models were developed from data collected at two unsignalized mid-block 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, 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 on the complex interaction of pedestrians and vehicles at unsignalized intersections and have implications for future work towards predictive models for driver yielding behavior. The developed logit models can provide the basis for representing driver yielding behavior in a microsimulation modeling environment.