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城市过江道路隧道的肇事逃逸事故。

Hit-and-run crashes in urban river-crossing road tunnels.

机构信息

School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China.

College of Transport and Communications, Shanghai Maritime University, 1550 Haigang Avenue, Shanghai 201306, China.

出版信息

Accid Anal Prev. 2016 Oct;95(Pt B):373-380. doi: 10.1016/j.aap.2015.09.003. Epub 2015 Sep 26.

DOI:10.1016/j.aap.2015.09.003
PMID:26411325
Abstract

Hit-and-run crashes are a relatively infrequent but severe offense worldwide because the identification and emergency rescue of victims is delayed, which increases the injury severities and the mortality rate. However, no studies have been conducted on hit-and-run crashes in urban river-crossing road tunnels (URCRTs), which can greatly threaten the safety of motorists driving in the tunnels. This study, which employs a dataset of vehicle crashes that happened in thirteen urban road tunnels traversing the Huangpu River, established a binary logistic regression model to identify thirteen factors that contribute to escaping after crashes in Shanghai related to the offending drivers, the vehicular and environmental conditions, the tunnel characteristics and crash information. Among the thirty-five variables considered, this study found that a perpetrator's tendency to leave the crash scene without reporting an accident was higher at night, in the tunnel exit, near to or in short tunnels, when a two-wheeled vehicle or heavy goods vehicle (HGV) was involved and when alcohol was involved. While a perpetrator was more likely to remain on the scene in the tunnel entrance, on a rainy day, in a rear end collision, when a bus was involved, in a single vehicle or a multi-vehicle accident. Based on these findings, several countermeasures for better supervision and hit-and-run prevention are proposed.

摘要

肇事逃逸事故在全球范围内相对较少但后果严重,因为受害者的身份识别和紧急救援被延误,这增加了伤害的严重程度和死亡率。然而,目前还没有针对城市过江道路隧道(URCRT)肇事逃逸事故的研究,这可能会极大地威胁到在隧道内行驶的驾驶员的安全。本研究利用在上海市十三座穿越黄浦江的城市道路隧道中发生的车辆碰撞事故数据集,建立了二元逻辑回归模型,以确定与肇事司机、车辆和环境条件、隧道特征和碰撞信息有关的十三个导致肇事逃逸的因素。在考虑的三十五个变量中,本研究发现,肇事者在夜间、在隧道出口处、在靠近或短隧道内、涉及两轮车或重型货车(HGV)以及涉及酒精时,更有可能在没有报告事故的情况下离开事故现场。而肇事者更有可能在隧道入口处、雨天、追尾碰撞时、涉及公共汽车时、单车或多车事故时留在现场。基于这些发现,提出了一些更好的监管和预防肇事逃逸的对策。

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