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Bicycle crash frequency modeling across different crash severities using a random-forest-based Shapley Additive
Tao Li1, Ruiqi Wang1, Hongliang Ding2
1School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, Sichuan, China.
International Journal of Injury Control and Safety Promotion
|March 29, 2025
Summary
Understanding bicycle crash risk factors is key to improving safety. This study uses advanced modeling to identify critical factors like building density and population, revealing how they impact crash severity and frequency.
Area of Science:
- Urban planning and transportation safety
- Statistical modeling and data analytics
- Road safety engineering
Background:
- Bicycle accident studies often lack detailed explanations of risk factor impacts on crash severity.
- Understanding these mechanisms is crucial for developing effective safety interventions.
- Existing research has not fully explored the interplay of various factors across different crash severities.
Purpose of the Study:
- To investigate the influence of diverse risk factors on bicycle crash frequency across varying severity levels.
- To apply advanced machine learning techniques for a deeper understanding of crash causation.
- To identify and quantify the primary determinants of bicycle collision severity in London.
Main Methods:
- Utilized three years of London crash data (2017-2019).
- Employed Random Forest and Shapley Additive Explanations (RF-SHAP) for predictive modeling and factor importance analysis.
- Integrated data on population demographics, land use, road infrastructure, and traffic flow.
Main Results:
- Identified building area proportion and population density as critical factors influencing bicycle crash numbers across severities.
- Quantified main and interactive effects of risk factors, revealing complex relationships.
- Found a negative correlation between traffic flow and crash frequency below 2.25 road network connectivity.
- Determined a safety impact boundary for road density (6.3) on severe crashes.
- Observed a three-stage influence of residential areas on slight-injury crashes, controlling for population density.
Conclusions:
- RF-SHAP effectively identifies key risk factors and their impact on bicycle crash severity.
- Findings provide crucial insights for targeted, cost-effective safety countermeasures in urban environments.
- Long-term improvements in bicycle safety can be achieved through data-driven infrastructure, traffic, and education strategies.
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