title: Hit-and-run crashes in urban river-crossing road tunnels.
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.
authors.affiliation: School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China. Electronic address: cheemychiang@sjtu.edu.cn.
authors.forename: Chenming
authors.initials: C
authors.lastname: Jiang
authors.affiliation: School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China. Electronic address: linjunlu@sjtu.edu.cn.
authors.forename: Linjun
authors.initials: L
authors.lastname: Lu
authors.affiliation: College of Transport and Communications, Shanghai Maritime University, 1550 Haigang Avenue, Shanghai 201306, China. Electronic address: sdchen@shmtu.edu.cn.
authors.forename: Shengdi
authors.initials: S
authors.lastname: Chen
authors.affiliation: School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China; College of Transportation Engineering, Tongji University, 4800 Cao'an Road, Shanghai 201804, China. Electronic address: jianjohnlu@tongji.edu.cn.
authors.forename: Jian John
authors.initials: JJ
authors.lastname: Lu
mesh_terms: D000063:Accidents, Traffic / Q000517:prevention & control / Q000706:statistics & numerical data*; D001334:Automobile Driving / Q000523:psychology*; D002681:China; D004779:Environment Design; D006801:Humans; D016015:Logistic Models; D018986:Motor Vehicles; D012372:ROC Curve; D012307:Risk Factors; D045483:Rivers; D012449:Safety; D014505:Urban Population
keywords: Hit-and-run; Logistic regression; River-crossing tunnels; Shanghai; Urban road safety