Highway-rail grade crossing (HRGC) crashes continue to be the major contributors to rail causalities in the United States and have been intensively researched in the past. Data-mining models focus on prediction while dominant general linear models focus on model and data fitness. Decision makers and traffic engineers rely on prediction models to examine at-grade crash frequency and make safety improvement. The gradient boosting (GB) model has gained popularity in many research areas. In this study, to fully understand the model performance on HRGC accident prediction performance, the GB model with functional gradient descent algorithm is selected to analyze crashes at highway-rail grade crossings (HRGCs) and to identify contributor factors. Moreover, contributors’ importance and partial-dependent relations are generated to further understand the relationship of identified contributors and HRGC crash likelihood to concur “black box” issues that most machine learning methods face. Furthermore, to fully demonstrate the model’s prediction performance, a comprehensive model prediction power assessment based on six measurements is conducted, and the prediction performance of the GB model is verified and compared with a decision tree model as a reference due to their popularity and comparable data availability. It is demonstrated that the GB model produces better prediction accuracy and reveals nonlinear relationships among contributors and crash likelihood. In general, HRGC crash likelihood is significantly impacted by several traffic exposure factors: highway traffic volume, railway traffic volume, and train travel speed and others.

中文翻译：

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公路-铁路平交道口碰撞分析的梯度提升碰撞预测方法

高速公路-铁路平交道口（HRGC）撞车仍然是美国铁路因果关系的主要起因，并且在过去进行了深入研究。数据挖掘模型专注于预测，而占优势的通用线性模型则专注于模型和数据适用性。决策者和交通工程师依靠预测模型来检查坡道碰撞频率并提高安全性。梯度增强（GB）模型已在许多研究领域广受欢迎。在这项研究中，为了充分了解HRGC事故预测性能的模型性能，选择具有功能梯度下降算法的GB模型来分析公路-铁路平交道口的撞车事故，并确定影响因素。此外，生成贡献者的重要性和部分相关的关系是为了进一步了解已确定的贡献者之间的关系以及HRGC崩溃可能性，以同意大多数机器学习方法面临的“黑匣子”问题。此外，为了充分展示模型的预测性能，基于六次测量进行了全面的模型预测能力评估，并验证了GB模型的预测性能，并基于其受欢迎程度和可比较的数据与决策树模型进行了比较，以作为参考可用性。结果表明，GB模型具有更好的预测精度，并揭示了影响因素与崩溃可能性之间的非线性关系。通常，HRGC崩溃的可能性会受到以下几种交通风险因素的显着影响：高速公路交通量，铁路交通量，