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Impact of traffic states on freeway crash involvement rates

Authors
Yeo, HwasooJang, KitaeSkabardonis, AlexanderKang, Seungmo
Issue Date
1월-2013
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Keywords
Traffic safety; Accident; Traffic state; Crash involvement rate
Citation
ACCIDENT ANALYSIS AND PREVENTION, v.50, pp.713 - 723
Indexed
SSCI
SCOPUS
Journal Title
ACCIDENT ANALYSIS AND PREVENTION
Volume
50
Start Page
713
End Page
723
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/104418
DOI
10.1016/j.aap.2012.06.023
ISSN
0001-4575
Abstract
Freeway traffic accidents are complicated events that are influenced by multiple factors including roadway geometry, drivers' behavior, traffic conditions and environmental factors. Among the various factors, crash occurrence on freeways is supposed to be strongly influenced by the traffic states representing driving situations that are changed by road geometry and cause the change of drivers' behavior. This paper proposes a methodology to investigate the relationship between traffic states and crash involvements on the freeway. First, we defined section-based traffic states: free flow (FF), back of queue (BQ), bottleneck front (BN) and congestion (CT) according to their distinctive patterns; and traffic states of each freeway section are determined based on actual measurements of traffic data from upstream and downstream ends of the section. Next, freeway crash data are integrated with the traffic states of a freeway section using upstream and downstream traffic measurements. As an illustrative study to show the applicability, we applied the proposed method on a 32-mile section of I-880 freeway. By integrating freeway crash occurrence and traffic data over a three-year period, we obtained the crash involvement rate for each traffic state. The results show that crash involvement rate in BN, BQ, and CT states are approximately 5 times higher than the one in FF. The proposed method shows promise to be used for various safety performance measurement including hot spot identification and prediction of the number of crash involvements on freeway sections. (C) 2012 Elsevier Ltd. All rights reserved.
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Kang, Seung mo
공과대학 (건축사회환경공학부)
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