Assessing the crash risks of evacuation: A matched case-control approach applied over data collected during Hurricane Irma
Assessing the crash risks of evacuation: A matched case-control approach applied over data collected during Hurricane Irma
复制标题
评估疏散的崩溃风险：对飓风艾尔玛期间收集的数据应用匹配的病例对照方法
DOI：
10.1016/j.aap.2021.106260
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发表时间：
2021
影响因子：
5.9
通讯作者：
Hasan, Samiul
中科院分区：
文献类型：
--
作者：
Rahman, Rezaur;Bhowmik, Tanmoy;Eluru, Naveen;Hasan, Samiul
最近的飓风经历引起了运输机构和政策制定者对寻找更好疏散策略的担忧，特别是在飓风“艾尔玛”之后，该飓风迫使约 650 万佛罗里达人疏散，并因严重拥堵而造成严重延误。发布疏散令的一个主要问题是，它可能会导致高速公路上发生大量车祸。在这项研究中，我们提出了一种基于匹配病例对照的方法，以了解导致疏散期间事故数量增加的因素。我们使用事故发生前 5 到 10 分钟的交通数据。对于每次碰撞观察，交通数据是从碰撞位置的两个上游和两个下游检测器收集的。我们估计三种不同条件的模型：常规时段、疏散时段以及结合疏散和常规时段数据。模型结果表明，如果上游站点的交通量较大，而下游站点的速度变化较大，则发生事故的可能性就会增加。使用面板混合二元logit模型，我们还估计了疏散本身对碰撞风险的影响，发现在控制交通特征后，疏散期间发生碰撞的几率高于正常时期。我们的研究结果对疏散声明具有影响，并强调在疏散期间需要更好的交通管理策略。未来的研究可能会开发先进的实时碰撞预测模型，这将使我们能够部署主动对策，以减少疏散期间的碰撞发生。
Recent hurricane experiences have created concerns for transportation agencies and policymakers to find better evacuation strategies, especially after Hurricane Irma—which forced about 6.5 million Floridians to evacuate and caused a significant amount of delay due to heavy congestion. A major concern for issuing an evacuation order is that it may involve a high number of crashes in highways. In this study, we present a matched case-control based approach to understand the factors contributing to the increase in the number of crashes during evacuation. We use traffic data for a period of 5 to 10 min just before the crash occurred. For each crash observation, traffic data are collected from two upstream and two downstream detectors of the crash location. We estimate models for three different conditions: regular period, evacuation period, and combining both evacuation and regular period data. Model results show that, if there exist a high volume of traffic at an upstream station and a high variation of speed at a downstream station, the likelihood of crash occurrence increases. Using a panel mixed binary logit model, we also estimate the effect of evacuation itself on crash risk and find that, after controlling for traffic characteristics, during evacuation the chance of a crash is higher than in a regular period. Our findings have implications for evacuation declarations and highlight the need for better traffic management strategies during evacuation. Future studies may develop advanced real-time crash prediction models which would allow us to deploy proactive countermeasures to reduce crash occurrences during evacuation.