Upper Great Plains Transportation Institute, Dept. 2880, North Dakota State University, Fargo, ND 58108-6050, USA.
Department of Transportation, Logistics, and Finance, Upper Great Plains Transportation Institute, North Dakota State University, Fargo, ND 58108-6050, USA.
Accid Anal Prev. 2020 Sep;144:105683. doi: 10.1016/j.aap.2020.105683. Epub 2020 Jul 10.
This paper proposes a machine learning approach, the random survival forest (RSF) for competing risks, to investigate highway-rail grade crossing (HRGC) crash severity during a 29-year analysis period. The benefits of the RSF approach are that it (1) is a special type of survival analysis able to accommodate the competing nature of multiple-event outcomes to the same event of interest (here the competing multiple events are crash severities), (2) is able to conduct an event-specific selection of risk factors, (3) has the capability to determine long-term cumulative effects of contributors with the cumulative incidence function (CIF), (4) provides high prediction performance, and (5) is effective in high-dimensional settings. The RSF approach is able to consider complexities in HRGC safety analysis, e.g., non-linear relationships between HRGCs crash severities and the contributing factors and heterogeneity in data. Variable importance (VIMP) technique is adopted in this research for selecting the most predictive contributors for each crash-severity level. Moreover, marginal effect analysis results real several HRGC countermeasures' effectiveness. Several insightful findings are discovered. For examples, adding stop signs to HRGCs that already have a combination of gate, standard flashing lights, and audible devices will reduce the likelihood of property damage only (PDO) crashes for up to seven years; but after the seventh year, the crossings are more likely to have PDO crashes. Adding audible devices to crossing with gates and standard flashing lights will reduce crash likelihood, PDO, injury, and fatal crashes by 49 %, 52 %, 46 %, and 50 %, respectively.
本文提出了一种机器学习方法，即随机生存森林（RSF），用于研究 29 年分析期间公路铁路交叉口（HRGC）碰撞严重程度的竞争风险。RSF 方法的优点在于：(1) 它是一种特殊类型的生存分析方法，能够适应同一感兴趣事件（这里的竞争多事件是碰撞严重程度）的多个事件结果的竞争性质；(2) 能够对风险因素进行特定事件的选择；(3) 具有通过累积发生率函数（CIF）确定贡献者长期累积效应的能力；(4) 提供高预测性能；(5) 在高维环境中有效。RSF 方法能够考虑 HRGC 安全分析的复杂性，例如 HRGC 碰撞严重程度与贡献因素之间的非线性关系以及数据的异质性。本研究采用变量重要性（VIMP）技术来选择每个碰撞严重程度水平最具预测性的贡献因素。此外，边际效应分析结果真实反映了几种 HRGC 对策的有效性。发现了一些有见地的发现。例如，在已经有门、标准闪光灯和声音装置的 HRGC 上添加停车标志，将降低财产损失（PDO）碰撞的可能性，最长可达七年；但在第七年之后，这些交叉口更有可能发生 PDO 碰撞。在带有门和标准闪光灯的交叉路口添加声音设备，将分别降低碰撞可能性、PDO、伤害和致命碰撞的可能性 49%、52%、46%和 50%。
