ty - jour a2 - 高，鲁··鲁，潘·奥 - 郑，紫房·奥 - 仁，yao - 周，小玉 - k k，amin互惠 - 托尔，丹佛·奥 - 黄，ying py - 2020年/06/19 TI - 公路铁路级交叉碰撞崩溃分析SP - 6751728 VL - 2020 AB - 公路铁路级交叉（HRGC）崩溃继续成为美国的主要贡献者和美国的主要贡献者过去已经集中研究。数据挖掘模型专注于预测，而主导的一般线性模型专注于模型和数据健身。决策者和交通工程师依靠预测模型来检查级别的碰撞频率并进行安全改进。梯度升压（GB）模型在许多研究领域获得了普及。在本研究中，为了充分了解HRGC事故预测性能的模型性能，选择了具有功能梯度下降算法的GB模型，分析了公路铁路级交叉口（HRGCS）的崩溃并识别贡献因素。此外，产生了贡献者的重要性和部分依赖关系，以进一步了解所确定的贡献者和HRGC崩溃可能性，以调节大多数机器学习方法面临的“黑匣子”问题。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. SN - 0197-6729 UR - https://doi.org/10.1155/2020/6751728 DO - 10.1155/2020/6751728 JF - Journal of Advanced Transportation PB - Hindawi KW - ER -