A Gradient Boosting Crash Prediction Approach for Highway-Rail Grade Crossing Crash Analysis

• Published in: Journal of advanced transportation Vol. 2020; no. 2020; pp. 1 - 10
• Main Authors: Huang, Ying, Keramati, Amin, Zhou, Xiaoyi, Ren, Yihao, Zheng, Zijian, Lu, Pan, Tolliver, Denver
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi; John Wiley & Sons, Inc; Hindawi Limited; Hindawi-Wiley
• Subjects: Accuracy; Algorithms; Analysis; Crashes; Data mining; Datasets; Decision trees; Generalized linear models; Learning algorithms; Level crossings; Lu Pan; Machine learning; Missing data; Model accuracy; Performance prediction; Prediction models; Railroad accidents; Railroad accidents & safety; Researchers; Roads & highways; Safety research; Sample variance; Survival analysis; Traffic control; Traffic engineering; Traffic flow; Traffic speed; Traffic volume; Trains; Transportation planning; Variables; North Dakota; United States > US
• ISSN: 0197-6729; 2042-3195
• DOI: 10.1155/2020/6751728

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.