Understanding driver-pedestrian interactions to predict driver yielding:
  naturalistic open-source dataset collected in Minnesota
Tianyi Li Joshua Klavins Te Xu Niaz Mahmud Zafri Raphael Stern
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.