Geography & Anthropology Department, Louisiana State University, Baton Rouge, LA 70802, United States.
Civil & Environmental Engineeing Department, Louisiana State University, LA 70808, United States.
Accid Anal Prev. 2019 Jul;128:65-77. doi: 10.1016/j.aap.2019.04.002. Epub 2019 Apr 10.
In the United States, there are approximately 212,000 highway-rail grade crossings, some of which experience vehicle-train incidents that often cause a massive financial burden, loss of life, and injury. In 2017, there were 2,108 highway-rail incidents resulting in 827 injuries and 307 fatalities nationwide. To eliminate collision risks, crossing grade separation and active alarm improvement are commonly used. Moreover, crossing closures are considered to be the most effective safety improvement program. While it may be very difficult, and in some cases impossible to close crossings, there are some incentive programs that facilitate the closure process. One of these programs is a working consolidation model that aims to determine crossing closure suitability. Using details of highway-rail grade crossings in the United States and applying an eXtreme Gradient Boosting (XGboost) algorithm, this paper proposes a data-driven consolidation model that takes into consideration a number of engineering variables. The results indicated an overall accuracy of 0.991 for the proposed model. In addition, the developed XGboost consolidation model reported the relative importance of the variables input to the model, offering an in-depth understanding of the model's behavior. Finally, for the practical implementation of the model, a simplified version containing fewer variables was developed. A sensitivity analysis was performed considering the aggregate gain and the different correlation threshold values between variables. This analysis developed a simplified model utilizing 14 variables, with aggregated gain values of 75% and a correlation threshold of 0.9 which would perform similarly to the full model. Based on this model, 62% of current highway-rail grade crossings should be closed.
在美国，大约有 212000 个公路铁路平交道口，其中一些道口发生的车辆-火车事故经常造成巨大的经济负担、生命损失和伤害。2017 年，全国发生了 2108 起公路铁路事故，造成 827 人受伤，307 人死亡。为了消除碰撞风险，通常采用道口立体交叉和主动报警改进措施。此外，道口关闭被认为是最有效的安全改进计划。虽然关闭道口可能非常困难，在某些情况下甚至不可能，但有一些激励计划可以促进关闭过程。其中一个计划是一个工作整合模型，旨在确定道口关闭的适宜性。本文使用美国公路铁路平交道口的详细信息，并应用极端梯度提升（XGboost）算法，提出了一个数据驱动的整合模型，该模型考虑了许多工程变量。结果表明，所提出的模型的整体准确性为 0.991。此外，开发的 XGboost 整合模型报告了输入到模型中的变量的相对重要性，为模型的行为提供了深入的了解。最后，为了模型的实际实施，开发了一个包含较少变量的简化版本。考虑到总增益和变量之间的不同相关阈值，进行了敏感性分析。该分析开发了一个简化模型，包含 14 个变量，总增益值为 75%，相关阈值为 0.9，其性能与全模型相似。基于该模型，62%的现有公路铁路平交道口应该关闭。
