Pedestrian crossing volume estimation at signalized intersections using Bayesian additive regression trees

Pedestrian crossing volume is one of the most important variables used to retime and optimize the signal timing plans for traffic delay migration and traffic safety improvement at signalized intersections. However, most of the existing studies only focus on long-term pedestrian volume estimation for planning purposes. To bridge the research gap, this study applied a Bayesian Additive Regression Trees (BART) model to estimate the short-term pedestrian crossing volume at signalized intersections equipped with pushbutton devices. Pedestrian-related traffic controller event-based data used as the time-dependent variables representing the temporal trend of pedestrian crossing volume in conjunction with point-of-interest (POI) data and transit trips are chosen as the inputs of the BART model. Seventy signalized intersections from the Pima County region are selected to collect data for calibrating and validating the developed model. When compared with ground-truth data, the proposed method has an R-squared of 0.83, 0.81, and 0.71 for 60 min, 30 min, and 15 min intervals of pedestrian crossing volume, respectively. To further evaluate the performance of the proposed method, the proposed method is used in comparison to two traditional methods (stepwise linear regression and Random Forest). The comparison results show that the BART model is superior to the other two models for hourly pedestrian crossing volume estimation. The evaluation results show that the proposed method can accurately estimate pedestrian crossing volume and provide valuable information for signal retiming.