Many factors influence the yielding result of a driver-pedestrian interaction, including traffic volume, vehicle speed, roadway characteristics, etc. While individual aspects of these interactions have been explored, comprehensive, naturalistic studies, particularly those considering the built environment's influence on driver-yielding behavior, are lacking. To address this gap, our study introduces an extensive open-source dataset, compiled from video data at 18 unsignalized intersections across Minnesota. Documenting more than 3000 interactions, this dataset provides a detailed view of driver-pedestrian interactions and over 50 distinct contextual variables. The data, which covers individual driver-pedestrian interactions and contextual factors, is made publicly available at https://github.com/tianyi17/pedestrian_yielding_data_MN. Using logistic regression, we developed a classification model that predicts driver yielding based on the identified variables. Our analysis indicates that vehicle speed, the presence of parking lots, proximity to parks or schools, and the width of major road crossings significantly influence driver yielding at unsignalized intersections. This study contributes to one of the most comprehensive driver-pedestrian datasets in the US, offering valuable insights for traffic safety improvements. By making this information available, our study will support communities across Minnesota and the United States in their ongoing efforts to improve road safety for pedestrians.

通过建立一个大规模的开源数据集进行综合研究，我们发现交通流量、车辆速度、道路特征等是影响驾驶员避让行为的重要因素。研究结果表明，车辆速度、停车场的存在、公园或学校的接近程度以及主要道路路口的宽度显著影响无信号灯十字路口的驾驶员避让行为，为改善行人交通安全提供了宝贵的洞见。

理解驾驶员-行人交互以预测驾驶员让行行为：在明尼苏达收集的自然开源数据集