Modeling dilemma zone at urban signalized intersections using crowdsourced trajectory data

The stop/go dilemma drivers face at the yellow onset is highly correlated with the potential risks of rear-end collisions and red-light running crashes. This dilemma has been physically characterized using the Type I and Type II definitions. Unlike the Type II definition with several limitations, the Type I counterpart incorporates the dynamics of driver-vehicle attributes to quantify the dilemma zone accurately but requires high-quality vehicle trajectory data. Such trajectory data in existing studies are extracted from field-setup video cameras or radar, undergoing manual trajectory reduction and labor-intensive data processing challenges. Moreover, accurate modeling of the Type I dilemma zone dynamics and accuracy evaluation with the Type II methods remain major research gaps in the existing literature. This study addresses these gaps and challenges by accurately quantifying the Type I dilemma zone using a large sample of crowdsourced vehicle trajectory data. Quantile regression is implemented to capture the dynamics of individual driver-vehicle attributes directly into the minimum stopping and the maximum clearing distances. Results across 15 intersection approaches consistently showed that the Type I dilemma zone is created if vehicles approach at a very high speed. Accuracy evaluation yielded low root mean squared errors of 14.8 ft and 25.1 ft in estimating the start and end of zone boundary, demonstrating the proposed method's superiority over other dilemma zone quantification methods. Besides boundary comparison, driver behavior at the approach area is analyzed to understand potential rear-end and right-angle collision risks. This study advances our understanding of dilemma zone boundary dynamics and provides a sound empirical basis to support the development of efficient dilemma zone protection and signal timing strategies to improve intersection safety.