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Incorporating accident liability into crash risk analysis: A multidimensional risk source approach
Xin Wang1, Zhaowei Qu1, Xianmin Song1
1Department of Transportation, Jilin University, Changchun, 130022, China.
Accident; Analysis and Prevention
|February 19, 2021
Summary
This study identifies traffic accident risk factors and quantifies accident risk using a novel model. Findings aid in developing better accident prevention strategies and risk propagation models for improved road safety.
Area of Science:
- Traffic Safety
- Accident Analysis
- Risk Management
Background:
- Traffic accidents pose a significant concern for road safety regulators and users.
- Identifying accident causes and quantifying risk is crucial for prevention and control.
- Existing models may not fully capture the complexity of accident risk factors.
Purpose of the Study:
- To mine risk factors influencing traffic accidents from detailed records.
- To quantify accident risk considering combined risk factors.
- To develop an effective accident risk quantify model (ARQM) for scenario comparison.
Main Methods:
- Constructed a multi-dimension, bi-level risk factor framework, enhanced by the Human Factors Analysis and Classification System (HFACS).
- Identified and statistically analyzed risk factors (sources and characteristics) within accident data.
- Proposed 'accident liability weight' to measure factor impact, updated accident probabilities, and developed the ARQM using mean mutual information.
Main Results:
- Identified key risk factors and their statistical characteristics.
- Quantified accident risk under combined factors using the ARQM.
- Demonstrated updated accident probabilities based on liability affirmation.
Conclusions:
- The developed ARQM effectively quantifies accident risk and compares likelihood across scenarios.
- Understanding liability weights enhances accident probability assessment.
- Findings provide fundamental insights for effective traffic accident prevention.
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