Khalilikhah Majid, Heaslip Kevin
Department of Civil & Environmental Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA 22203, United States.
J Safety Res. 2017 Sep;62:1-12. doi: 10.1016/j.jsr.2017.04.003. Epub 2017 Apr 21.
Animal-vehicle collisions (AVCs) can result in serious injury and death to drivers, animals' death, and significant economic costs. However, the cost effectiveness of the majority of AVC mitigation measures is a significant issue.
A mobile-based data collection effort was deployed to measure signs under the Utah Department of Transportation's (UDOT) jurisdiction. The crash data were obtained from the UDOT risk management database. ArcGIS was employed to link these two data sets and extract animal-related crashes and signs. An algorithm was developed to process the data and identify AVCs that occurred within sign recognition distance. Kernel density estimation (KDE) technique was applied to identify potential crash hotspots.
Only 2% of AVCs occurred within the recognition distance of animal crossing signs. Almost 58% of animal-related crashes took place on the Interstate and U.S. highways, wherein only 30% of animal crossing signs were installed. State routes with a higher average number of signs experienced a lower number of AVCs per mile. The differences between AVCs that occurred within versus outside of sign recognition distance were not statistically significant regarding crash severity, time of crash, weather condition, driver age, vehicle speed, and type of animal. It is more likely that drivers become accustomed to deer crossing signs than cow signs.
Based on the historical crash data and landscape structure, with attention given to the low cost safety improvement methods, a combination of different types of AVC mitigation measures can be developed to reduce the number of animal-related crashes. After an in-depth analysis of AVC data, warning traffic signs, coupled with other low cost mitigation countermeasures can be successfully placed in areas with higher priority or in critical areas. Practical applications: The findings of this study assist transportation agencies in developing more efficient mitigation measures against AVCs.
动物与车辆碰撞（AVC）会导致驾驶员严重受伤甚至死亡、动物死亡以及巨大的经济损失。然而，大多数AVC缓解措施的成本效益是一个重大问题。
开展了一项基于移动设备的数据收集工作，以测量犹他州交通运输部（UDOT）管辖范围内的标识。碰撞数据来自UDOT风险管理数据库。利用ArcGIS将这两个数据集进行关联，并提取与动物相关的碰撞事故和标识。开发了一种算法来处理数据，并识别在标识识别距离内发生的AVC。应用核密度估计（KDE）技术来识别潜在的碰撞热点区域。
只有2%的AVC发生在动物穿越标识的识别距离内。近58%的与动物相关的碰撞事故发生在州际公路和美国高速公路上，而这些道路上仅安装了30%的动物穿越标识。平均标识数量较多的州级公路每英里发生的AVC数量较少。在碰撞严重程度、碰撞时间、天气状况、驾驶员年龄、车速和动物类型方面，发生在标识识别距离内和距离外的AVC之间的差异无统计学意义。驾驶员对鹿穿越标识的习惯程度可能高于牛穿越标识。
基于历史碰撞数据和景观结构，同时关注低成本的安全改进方法，可以制定不同类型AVC缓解措施的组合，以减少与动物相关的碰撞事故数量。在对AVC数据进行深入分析之后，警示交通标识以及其他低成本缓解对策可以成功设置在优先级较高的区域或关键区域。实际应用：本研究结果有助于交通部门制定更有效的AVC缓解措施。
