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Journal Article

Citation

Yue L, Abdel-Aty MA, Wu Y, Wang L. Accid. Anal. Prev. 2018; 117: 55-64.

Affiliation

The Key Laboratory of Road and Traffic Engineering, Ministry of Education, China; School of Transportation Engineering, Tongji University, Shanghai 201804, China; Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, USA. Electronic address: LingWang@Knights.ucf.edu.

Copyright

(Copyright © 2018, Elsevier Publishing)

DOI

10.1016/j.aap.2018.04.002

PMID

29654988

Abstract

The Connected Vehicle (CV) technologies together with other Driving Assistance (DA) technologies are believed to have great effects on traffic operation and safety, and they are expected to impact the future of our cities. However, few research has estimated the exact safety benefits when all vehicles are equipped with these technologies. This paper seeks to fill the gap by using a general crash avoidance effectiveness framework for major CV&DA technologies to make a comprehensive crash reduction estimation. Twenty technologies that were tested in recent studies are summarized and sensitivity analysis is used for estimating their total crash avoidance effectiveness. The results show that crash avoidance effectiveness of CV&DA technology is significantly affected by the vehicle type and the safety estimation methodology. A 70% crash avoidance rate seems to be the highest effectiveness for the CV&DA technologies operating in the real-world environment. Based on the 2005-2008 U.S. GES Crash Records, this research found that the CV&DA technologies could lead to the reduction of light vehicles' crashes and heavy trucks' crashes by at least 32.99% and 40.88%, respectively. The rear-end crashes for both light vehicles and heavy trucks have the most expected crash benefits from the technologies. The paper also studies the effectiveness of Forward Collision Warning technology (FCW) under fog conditions, and the results show that FCW could reduce 35% of the near-crash events under fog conditions.

Copyright © 2018 Elsevier Ltd. All rights reserved.


Language: en

Keywords

Connected vehicle technology; Crash avoidance effectiveness; Driving assistance technology; Pre-Crash scenarios; Safety estimation methodology

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