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A Bayesian Tobit quantile regression approach for naturalistic longitudinal driving capability assessment
Rongjie Yu1, Xiaojie Long1, Mohammed Quddus2
1The Key Laboratory of Road and Traffic Engineering, Ministry of Education, 201804, Shanghai, China; College of Transportation Engineering, Tongji University, 201804, Shanghai, China.
Accident; Analysis and Prevention
|September 27, 2020
Summary
This study introduces a new method to assess driving capability using naturalistic driving data, identifying nighttime driving and higher speeds as key risk factors. The approach helps rank drivers and improve fleet safety management.
Area of Science:
- Traffic Safety
- Transportation Engineering
- Behavioral Analysis
Background:
- Driving behaviors contribute to most crashes, with inadequate driving capability being a major factor.
- Conventional driving capability assessments are insufficient for complex scenarios and lack safety-specific quantification.
Purpose of the Study:
- To develop a novel approach for assessing and ranking longitudinal driving capability using naturalistic driving data.
- To quantify driving capability from a safety perspective in complex driving scenarios.
Main Methods:
- Proposed two Responsibility-Sensitive Safety (RSS) based driving capability indicators (risk exposure and severity).
- Utilized Bayesian Tobit quantile regression (BTQR) models to analyze relationships between indicators and trip characteristics.
- Incorporated travel features, operational conditions, and roadway characteristics in the analysis.
Main Results:
- Nighttime driving and higher average speeds increase longitudinal collision risk and severity.
- Driving duration significantly impacts drivers with high risk exposure and close following tendencies.
- BTQR models revealed varying factor significances across different quantile levels.
Conclusions:
- The developed model provides a robust method for assessing longitudinal driving capability.
- The approach enables relative driving capability rankings for applications in fleet safety and autonomous vehicle comparisons.
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