Geography &Anthropology Department, Louisiana State University, Baton Rouge, LA, 70802, United States.
Geography &Anthropology Department, Louisiana State University, Baton Rouge, LA, 70802, United States.
Accid Anal Prev. 2021 Mar;152:105985. doi: 10.1016/j.aap.2021.105985. Epub 2021 Jan 22.
The consolidation of Highway-Railroad Grade Crossing (HRGC) is one of the effective approaches to decrease the number of crashes between trains and vehicles. From 2015-2019, there were 57 HRGC crashes at crossings in East Baton Rouge Parish (EBRP), resulting in thirteen injuries with $346,875 cost of vehicle damages. Consolidation programs help to close redundant crossings and thereby decrease the crash risks; however, it is difficult to find the best crossing in a neighborhood for closure. In our previous research working on HRGC consolidation models in 2019, from among four Machine Learning algorithms, eXtreme Gradient Boosting (XGboost) performed better in HRGC prediction models. In continuation of our previous studies on developing a HRGC prediction model, this research employed Text Mining Techniques, and Geospatial Analysis in addition to the XGboost Machine Learning algorithm. The aim was to develop a consolidation model that is customized for local implementation. The results indicated an overall accuracy of 88 % for the proposed model. The relative importance of the variables input to the model was also reported and offers an in-depth understanding of the model's behavior. Considering the different correlation threshold, a sensitivity analysis was also performed on different aggregation gain values. Subsequently, it resulted in the development of a simplified model utilizing 14 variables, with aggregated gain values of 95 % and a correlation threshold of 0.5. Based on this model, 15 % of current highway-rail grade crossings should be closed.
公路-铁路平交道口的整合是减少火车与车辆碰撞事故的有效方法之一。2015 年至 2019 年间，东巴吞鲁日教区（EBRP）有 57 起公路-铁路平交道口碰撞事故，导致 13 人受伤，车辆损失 346875 美元。整合计划有助于关闭多余的道口，从而降低碰撞风险；然而，要找到一个社区中最适合关闭的道口是很困难的。在我们 2019 年关于公路-铁路平交道口整合模型的研究中，在四种机器学习算法中，极端梯度提升（XGboost）在公路-铁路平交道口预测模型中表现更好。在继续进行我们之前关于开发公路-铁路平交道口预测模型的研究的基础上，本研究采用了文本挖掘技术和地理空间分析，以及 XGboost 机器学习算法。目的是开发一个适合本地实施的整合模型。结果表明，所提出的模型的整体准确性为 88%。还报告了输入到模型中的变量的相对重要性，这为模型的行为提供了深入的理解。考虑到不同的相关阈值，还对不同的聚合增益值进行了敏感性分析。随后，利用 14 个变量开发了一个简化模型，聚合增益值为 95%，相关阈值为 0.5。基于该模型，目前 15%的公路-铁路平交道口应关闭。
