Vehicle-Pedestrian near miss analysis at signalized mid-block crossings

Abstract

Introduction

This study aims to identify the factors related to pedestrian and roadway characteristics that affect vehicle–pedestrian Post Encroachment Time (PET) and Relative Time to Collision (RTTC) under traffic control systems at mid-block pedestrian crossings.

Methodology

A total of 112 h of video data were collected using multiple cameras from Pedestrian Hybrid Beacon (PHB) and Rectangular Rapid Flashing Beacon (RRFB) sites. To extract vehicle and pedestrian trajectories and construct an accurate dataset, where each observation corresponds to a specific timeframe, with a recorded speeds of both vehicles and pedestrians, a self-developed cutting-edge Computer Vision (CV) technology was deployed. A bivariate regression approach is employed to capture the relationship between near misses and various factors.

Results and Conclusions

The findings reveal that both pedestrian and roadway characteristics significantly influence PET and RTTC. Pedestrian characteristics, such as gender, clothing color, distraction, waiting time, and crossing speed, significantly affect both PET and RTTC. The presence of children as pedestrians, eye contact with drivers, and pedestrian signal compliance rate has a significant influence on PET. Among roadway characteristics, the presence of a median, hourly traffic flow, and land use diversity of the crossing area were found to be significant determinants of both PET and RTTC. The results indicate that there is no difference in the influence of RRFB and PHB on PET values, but there is a significant difference in the influence of RRFB and PHB on RTTC values. PHB increases RTTC relative to RRFB. Finally, this study enriches existing literature by incorporating unique factors that impact pedestrian safety.

Practical Applications

The findings underscore the importance of data-driven approach to pedestrian safety, encouraging transportation agencies to implement targeted and effective safety strategies. In the future, the integration of artificial intelligence (AI) in traffic management and safety systems could greatly benefit from incorporating these findings.