School of Transport & Communications, Shanghai Maritime University, Shanghai 201306, China.
Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety, Key Laboratory of Road and Traffic Engineering of the State Ministry of Education, College of Transportation Engineering, Tongji University, Shanghai 201804, China.
Int J Environ Res Public Health. 2020 May 1;17(9):3155. doi: 10.3390/ijerph17093155.
The impact that trucks have on crash severity has long been a concern in crash analysis literature. Furthermore, if a truck crash happens in a tunnel, this would result in more serious casualties due to closure and the complexity of the tunnel. However, no studies have been reported to analyze traffic crashes that happened in tunnels and develop crash databases and statistical models to explore the influence of contributing factors on tunnel truck crashes. This paper summarizes a study that aims to examine the impact of risk factors such as driver factor, environmental factor, vehicle factor, and tunnel factor on truck crashes injury propensity based on tunnel crashes data obtained from Shanghai, China. An ordered logit model was developed to analyze injury crashes and property damage only crashes. The driver factor, environmental factor, vehicle factor, and tunnel factor were explored to identify the relationship between these factors and crashes and the severity of crashes. Results show that increased injury severity is associated with driver factors, such as male drivers, older drivers, fatigue driving, drunkenness, safety belt used improperly, and unfamiliarity with vehicles. Late night (00:00-06:59) and afternoon rushing hours (16:30-18:59), weekdays, snow or icy road conditions, combination truck, overload, and single vehicle were also found to significantly increase the probability of injury severity. In addition, tunnel factors including two lanes, high speed limits (≥80 km/h), zone 3, extra-long tunnels (over 3000 m) are also significantly associated with a higher risk of severe injury. So, the gender, age of driver, mid-night to dawn and afternoon peak hours, weekdays, snowy or icy road conditions, the interior zone of a tunnel, the combination truck, overloaded trucks, and extra-long tunnels are associated with higher crash severity. Identification of these contributing factors for tunnel truck crashes can provide valuable information to help with new and improved tunnel safety control measures.
卡车对碰撞严重程度的影响长期以来一直是碰撞分析文献中的一个关注点。此外，如果卡车事故发生在隧道中，由于隧道的封闭和复杂性，这将导致更严重的人员伤亡。然而，目前还没有研究报告分析发生在隧道中的交通碰撞事故，并开发碰撞数据库和统计模型来探索各种因素对隧道卡车碰撞事故的影响。本文总结了一项研究，该研究旨在根据从中国上海获得的隧道碰撞数据，检查驾驶员因素、环境因素、车辆因素和隧道因素等风险因素对卡车碰撞事故受伤倾向的影响。建立了有序逻辑回归模型来分析受伤碰撞和仅财产损失碰撞。探讨了驾驶员因素、环境因素、车辆因素和隧道因素，以确定这些因素与碰撞事故以及碰撞事故严重程度之间的关系。结果表明，受伤严重程度与驾驶员因素（如男性驾驶员、年龄较大的驾驶员、疲劳驾驶、醉酒、安全带使用不当、对车辆不熟悉）有关。深夜（00:00-06:59）和下午高峰时段（16:30-18:59）、工作日、雪天或结冰路面条件、组合卡车、超载以及单辆卡车也显著增加了受伤严重程度的概率。此外，隧道因素包括两条车道、高速限速（≥80km/h）、区域 3、超长隧道（超过 3000m）也与严重受伤的风险显著相关。因此，驾驶员的性别、年龄、午夜至黎明和下午高峰时段、工作日、雪天或结冰路面条件、隧道内部区域、组合卡车、超载卡车和超长隧道与更高的碰撞严重程度有关。识别这些导致隧道卡车碰撞事故的因素可以提供有价值的信息，有助于制定新的和改进的隧道安全控制措施。
