Citation

Li Y, Xing L, Wang W, Wang H, Dong C, Liu S. Accid. Anal. Prev. 2017; 107: 63-76.

Affiliation

Department of City and Regional Planning, University of North Carolina at Chapel Hill, New East Building, Chapel Hill, NC 27599, United States. Electronic address: shanwen@live.unc.edu.

Copyright

(Copyright © 2017, Elsevier Publishing)

DOI

10.1016/j.aap.2017.07.014

PMID

28803960

Abstract

Multi-vehicle rear-end (MVRE) crashes during small-scale inclement (SSI) weather cause high fatality rates on freeways, which cannot be solved by traditional speed limit strategies. This study aimed to reduce MVRE crash risks during SSI weather using different longitudinal driver assistance systems (LDAS). The impact factors on MVRE crashes during SSI weather were firstly analyzed. Then, four LDAS, including Forward collision warning (FCW), Autonomous emergency braking (AEB), Adaptive cruise control (ACC) and Cooperative ACC (CACC), were modeled based on a unified platform, the Intelligent Driver Model (IDM). Simulation experiments were designed and a large number of simulations were then conducted to evaluate safety effects of different LDAS.

RESULTS indicate that the FCW and ACC system have poor performance on reducing MVRE crashes during SSI weather. The slight improvement of sight distance of FCW and the limitation of perception-reaction time of ACC lead the failure of avoiding MVRE crashes in most scenarios. The AEB system has the better effect due to automatic perception and reaction, as well as performing the full brake when encountering SSI weather. The CACC system has the best performance because wireless communication provides a larger sight distance and a shorter time delay at the sub-second level. Sensitivity analyses also indicated that the larger number of vehicles and speed changes after encountering SSI weather have negative impacts on safety performances.

RESULTS of this study provide useful information for accident prevention during SSI weather.

Copyright © 2017 Elsevier Ltd. All rights reserved.

Language: en

Keywords

Accident prevention; Inclement weather; Intelligent transportation system; Safety; Simulation