Chapter
Aug 30, 2023
Understanding the Influence Factors for CAV Crash Severity Based on CatBoost and SHAP Analysis
Authors: Pei Liu [email protected], Yanyong Guo, Ph.D. [email protected], Mengmeng Zhang [email protected], and Yueying Huo [email protected]Author Affiliations
Publication: CICTP 2023
ABSTRACT
Connected and automated vehicle (CAV) is an effective way to solve traffic safety issues. However, CAV crashes occurred on roads during the past decades. This study aims to investigate the potential influence factors of CAV crash severity. Eight years of crash data between 2015 and 2022 were collected from the California Department of Motor Vehicles. Categorical Boosting (CatBoost) was utilized to model the severity with independent variables. SHapely Addictive exPlanation (SHAP) was applied to improve interpretability and provide insights into the impacts of risk factors on severity. The results show that the number of lanes, season, vehicle movement, and crash type correlate highly to CAV crash severity. Rear-end, bad weather, and adverse road conditions will increase the major accident rate. Signal control, bright streetlights, bus stops, and bicycle lanes can effectively increase the probability of no-loss crashes. These findings can help transportation agencies resolve CAV safety problems in practice.
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Published In
CICTP 2023
Pages: 1204 - 1214
History
Published online: Aug 30, 2023
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ASCE Technical Topics:
- Automation and robotics
- Data collection
- Engineering fundamentals
- Highway and road management
- Highway transportation
- Highways and roads
- Infrastructure
- Intelligent transportation systems
- Methodology (by type)
- Models (by type)
- Research methods (by type)
- Systems engineering
- Traffic accidents
- Traffic analysis
- Traffic engineering
- Traffic management
- Traffic models
- Traffic safety
- Transportation engineering
- Transportation management
Authors
Affiliations
1School of Transportation, Southeast Univ., Nanjing, China. Email: [email protected]
2Professor, School of Transportation, Southeast Univ., Nanjing, China. Email: [email protected]
3School of Transportation and Logistics Engineering, Shandong Jiaotong Univ., Jinan, China. Email: [email protected]
4Transportation Institute, Inner Mongolia Univ., Hohhot, China. Email: [email protected]
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Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.