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

Understanding driver-pedestrian interactions to predict driver yielding: naturalistic open-source dataset collected in Minnesota