Identifying built environment factors influencing driver yielding behavior at unsignalized intersections: A...

Many factors influence the yielding result of driver-pedestrian interactions, including traffic, vehicle, roadway, pedestrian attributes, and more. While researchers have examined the individual influence of these factors on interaction outcomes, there is a noticeable absence of comprehensive, naturalistic studies in current literature, particularly those investigating the impact of the built environment on driver-yielding behavior.

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://hdl.handle.net/11299/254556. 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.

Through our findings and by publishing one of the most comprehensive driver-pedestrian datasets in the United States, our study will support communities across Minnesota and the United States in their ongoing efforts to improve road safety for pedestrians and be helpful for automated vehicle design.

We have compiled a dataset on driver-pedestrian interactions at 18 unsignalized intersections in Minnesota, making it one of the most extensive datasets available in the United States. This dataset can be utilized by researchers and local agencies to enhance intersection safety and walkability. Furthermore, our study proposes recommendations for increasing pedestrian safety at intersections, providing valuable insights that local governments can use as guidance for designing future intersections.