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Using computer vision and machine learning to identify bus safety risk factors.

Becky P Y Loo¹, Zhuangyuan Fan², Ting Lian²

¹Department of Geography, The University of Hong Kong, Hong Kong, China; School of Geography and Environment, Jiangxi Normal University, Nanchang, China.

Accident; Analysis and Prevention

|March 8, 2023

Summary

Bus crashes pose significant risks due to passenger numbers and road network disruption. This study identifies pedestrian behavior and street design as key factors in bus crash frequency, offering insights for improved road safety planning.

Keywords:

Bus safety Crash modeling Pedestrian behaviour Video analytics

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Area of Science:

Road safety engineering
Urban planning
Transportation research

Background:

Bus crashes have substantial impacts on public health and infrastructure.
Cities increasingly prioritize people-oriented road design, necessitating a focus on pedestrian and street factors.
Understanding dynamic street environments is crucial for mitigating bus crash risks.

Purpose of the Study:

To identify high-risk factors contributing to bus crash frequency using bus dashcam footage.
To analyze behavioral and environmental elements influencing bus safety.
To inform urban planning and road safety interventions.

Main Methods:

Utilized deep learning models and computer vision techniques on bus dashcam video data.
Quantified pedestrian exposure, jaywalking, bus stop crowding, sidewalk railings, and sharp turns.
Developed a risk factor model for bus crash frequency estimation.

Main Results:

Identified pedestrian exposure, jaywalking, bus stop crowding, sidewalk railings, and sharp turns as significant risk factors.
Highlighted the dynamic nature of street environments and their impact on crash risk.
Demonstrated the effectiveness of computer vision in analyzing road safety.

Conclusions:

Road safety administrations should focus on streets with high pedestrian volumes and implement protective measures like railings.
Addressing bus stop crowding can prevent minor injuries.
Integrating pedestrian-centric design and advanced data analysis is vital for enhancing urban bus safety.