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Aug 30, 2023
An Efficient Bayesian Robit Model for Traffic Safety Modeling
Authors: Zhenning Li, Haicheng Liao, Ruru Tang, Guofa Li, Yiming Bie, and Chengzhong XuAuthor Affiliations
Publication: CICTP 2023
ABSTRACT
Traffic crash data sets may contain anomalous observations (i.e., outliers) which do not share the same distribution with the other observations. Regarding the most popular methods in the domain of traffic safety analysis, that is, logit and probit models, their sensitivity to outliers may lead to biased estimates, which has not been widely discussed. This paper introduces a robust Bayesian regression approach, namely the robit model, which substitutes the link function of these thin-tailed distributions with a heavy-tailed student’s t-distribution to down-weight the impacts of outliers. Additionally, a sandwich algorithm based on the data augmentation algorithm is proposed to improve the estimation efficiency of posteriors. The model is tested with a data set of tunnel crashes. Estimation results show that the robit model is efficient and robust and outperforms the baselines. Results also reveal that nine variables, that is, night, speeding, and fatigue driving, have significant effects on the severity of tunnel crashes.
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Published online: Aug 30, 2023
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ASCE Technical Topics:
- Algorithms
- Analysis (by type)
- Bayesian analysis
- Engineering fundamentals
- Geotechnical engineering
- Information management
- Infrastructure
- Mathematics
- Models (by type)
- Statistical analysis (by type)
- Traffic accidents
- Traffic analysis
- Traffic engineering
- Traffic management
- Traffic models
- Traffic safety
- Transportation engineering
- Tunnels
Authors
Affiliations
Zhenning Li
1State Key Laboratory of Internet of Things for Smart City and Dept. of Civil and Environmental Engineering, Univ. of Macau, Macau SAR, China
Haicheng Liao
2State Key Laboratory of Internet of Things for Smart City and Dept. of Computer and Information Science, Univ. of Macau, Macau SAR, China
Ruru Tang
3State Key Laboratory of Internet of Things for Smart City and Dept. of Civil and Environmental Engineering, Univ. of Macau, Macau SAR, China
Guofa Li
4College of Mechanical and Vehicle Engineering, Chongqing Univ., Chongqing, China
Yiming Bie
5School of Transportation, Jilin Univ., Changchun, China
Chengzhong Xu
6State Key Laboratory of Internet of Things for Smart City and Dept. of Computer and Information Science, Univ. of Macau, Macau SAR, China
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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.