Affiliation:
1. School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430205, China
2. Planning Research Office, Department of Transport of Hubei Province, Wuhan 430030, China
Abstract
Rear-end crashes or crash risk is widely recognized as safety-critical state of vehicles under comprehensive conditions. This study investigated the association between traffic flow uncertainty, drivers’ visual perception, car-following behavior, roadway and vehicular characteristics, and rear-end crash risk variation and compared the crash risk variation prediction with and without specific flow-level data. Two datasets comprising 5055 individual vehicles in car-following state were collected through on-road experiments on two freeways in China. A hierarchical hybrid BN model approach was proposed to capture the association between drivers’ visual perception, traffic flow uncertainty, and rear-end crash risk variation. Results show that (1) the BN model with flow-level data outperformed the BN model without flow-level data and could predict 85.3% of the cases of crash risk decrease, with a false alarm rate of 21.4%; (2) the hierarchical hybrid BN models showed plausible spatial transferability in predicting crash risk variation; and (3) the incorporation of specific flow-level variables and data greatly benefited the successful identification of rear-end crash risk variations. The findings of this study suggest that rear-end crash risk is inherently associated with both individual driving behaviors and traffic flow uncertainty, and appropriate visual perceptual information could compensate for crash risk and improve safety.
Funder
National Natural Science Foundation of China
Cited by
3 articles.
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