Abstract

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.

摘要

行人过街量是用于重新定时和优化信号配时计划的最重要变量之一，以改善信号交叉口的交通延误迁移和交通安全。然而，大多数现有研究仅关注用于规划目的的长期行人流量估计。为了弥补研究空白​​，本研究应用贝叶斯加性回归树 (BART) 模型来估计配备按钮设备的信号交叉口的短期行人过街量。选择与兴趣点 (POI) 数据和公交行程相结合的行人相关交通控制器基于事件的数据作为时间相关变量，表示行人过街量的时间趋势，作为 BART 模型的输入。选择了皮马县地区的 70 个信号交叉口来收集数据，用于校准和验证开发的模型。与地面实况数据相比，所提出的方法在 60 分钟、30 分钟和 15 分钟的行人过街量间隔内的 R 平方分别为 0.83、0.81 和 0.71。为了进一步评估所提出方法的性能，将所提出的方法与两种传统方法（逐步线性回归和随机森林）进行了比较。比较结果表明，BART 模型在每小时行人过街流量估计方面优于其他两种模型。评估结果表明，该方法可以准确估计行人过街量，为信号重定时提供有价值的信息。