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Citation |
Jo Y, Oh C, Kim S. Accid. Anal. Prev. 2019; 128: 103-113. |
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Affiliation |
Division of Architectural, Urban, and Civil Engineering, Engineering Research Institute, Gyeongsang National University, 501, Jinju-daero, Jinju-si, Gyeongnam, 52828, Republic of Korea. Electronic address: kimsb@gnu.ac.kr. |
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Copyright |
(Copyright © 2019, Elsevier Publishing) |
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DOI |
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PMID |
30991289 |
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Abstract |
Rear-end crash risk has been modeled and calibrated from various sources of data. The results can be used to develop traffic control strategies for avoiding collision. As the demand for freight transportation has increased in recent years, traffic agencies are more interested in monitoring heavy vehicle-involved crash risk because of the severity of accidents and high secondary crash risks. This study processes per-vehicle data from a WIM (weight-in-motion) sensor to investigate the heavy vehicle-involved crash risk potential (CRP) on freeways. To estimate rear-end CRP, one of the previously developed models is modified to use WIM data. In addition, this study proposes a technique for estimating the crash risk level, which is determined by comparing the estimated crash risk probability with the maximum expected risk probability at a given level of service (LOS). A time-series investigation reveals that there is clear difference in the CRP and crash risk level between lanes and there are two major time windows with high heavy vehicle-involved crash risk levels. After repeating the exercise for all available data, this study confirms that these observations are systematic and reproducible. Not only can the results in this study provide a local guidance for traffic agencies to prepare for heavy vehicle crash prevention strategies, but reiterating the investigation over multiple locations also allows them to monitor heavy vehicle crash risk levels at the network level. Language: en |
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Keywords |
CRP; Heavy vehicle; Rear-end crash; WIM |