Chapter
Aug 12, 2020

Effects of Foggy Conditions and Speed Limits on the Rear-End Collisions in Freeway

Publication: CICTP 2020

ABSTRACT

Rear-end collisions often occur in the freeway under foggy conditions, the speed limit is a common measure to reduce rear-end collisions. This study chose MTTC (minimum time to collision), TMTTC (the total of minimum time to collision of following vehicles) and TTET (the total of time exposed time-to-collision) as indicators, aiming to evaluate effects of foggy conditions and speed limits on the two-vehicle rear-end collision (TREC) and vehicle fleet rear-end collisions (FRECs). The data from the multi-user driving simulator system’s experiment were used for calculating indicators. Results indicated that as the fog level increases, the risk level of FRECs increases. Under heavy fog conditions, the reduction in the speed limits can greatly reduce the risk level of TREC. As the fog level increases, the possibility and severity of FRECs increases. Under heavy fog conditions, the reduction in the speed limits can greatly reduce the possibility of occurrence of FRECs.

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Go to CICTP 2020
CICTP 2020
Pages: 4088 - 4100

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Published online: Aug 12, 2020

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1MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Dept. of Traffic and Transportation, Beijing Jiaotong Univ., Beijing 100044, China. Email: [email protected]
Xuedong Yan [email protected]
2MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Dept. of Traffic and Transportation, Beijing Jiaotong Univ., Beijing 100044, China. Email: [email protected]
Xiaomeng Li [email protected]
3Centre for Accident Research and Road Safety-Queensland, Institute of Health and Biomedical Innovation, Queensland Univ. of Technology, Kelvin Grove, QLD 4059, Australia. Email: [email protected]
4MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Dept. of Traffic and Transportation, Beijing Jiaotong Univ., Beijing 100044, China. Email: [email protected]
Yunlin Guan [email protected]
5MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Dept. of Traffic and Transportation, Beijing Jiaotong Univ., Beijing 100044, China. Email: [email protected]
6MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Dept. of Traffic and Transportation, Beijing Jiaotong Univ., Beijing 100044, China. Email: [email protected]

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