A Gradient Boosting Crash Prediction Approach for Highway-Rail Grade Crossing Crash Analysis
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...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/6751728 |
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| author | Pan Lu Zijian Zheng Yihao Ren Xiaoyi Zhou Amin Keramati Denver Tolliver Ying Huang |
| author_facet | Pan Lu Zijian Zheng Yihao Ren Xiaoyi Zhou Amin Keramati Denver Tolliver Ying Huang |
| author_sort | Pan Lu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c032306037d24f3f839f2fe12c50bcd0 |
| institution | DOAJ |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-c032306037d24f3f839f2fe12c50bcd02025-08-20T03:23:23ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/67517286751728A Gradient Boosting Crash Prediction Approach for Highway-Rail Grade Crossing Crash AnalysisPan Lu0Zijian Zheng1Yihao Ren2Xiaoyi Zhou3Amin Keramati4Denver Tolliver5Ying Huang6Department of Transportation, Logistics, and Finance, Upper Great Plains Transportation Institute, North Dakota State University NDSU, Dept 2880, P. O. Box 6050, Fargo, ND 58108-6050, USAGates Corporation, 1144 Fifteenth St. Suite 1400, Denver, CO 80202, USAUpper Great Plains Transportation Institute, North Dakota State University NDSU, Dept 2880 P. O. Box 6050, Fargo, ND 58108-6050, USAUpper Great Plains Transportation Institute, North Dakota State University NDSU, Dept 2880 P. O. Box 6050, Fargo, ND 58108-6050, USAUpper Great Plains Transportation Institute, North Dakota State University NDSU, Dept 2880 P. O. Box 6050, Fargo, ND 58108-6050, USAUpper Great Plains Transportation Institute, North Dakota State University NDSU, Dept 2880 P. O. Box 6050, Fargo, ND 58108-6050, USADepartment of Civil and Environmental Engineering, North Dakota State University NDSU, Dept 2470, P. O. Box 6050, Fargo, ND 58108-6050, USAHighway-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.http://dx.doi.org/10.1155/2020/6751728 |
| spellingShingle | Pan Lu Zijian Zheng Yihao Ren Xiaoyi Zhou Amin Keramati Denver Tolliver Ying Huang A Gradient Boosting Crash Prediction Approach for Highway-Rail Grade Crossing Crash Analysis Journal of Advanced Transportation |
| title | A Gradient Boosting Crash Prediction Approach for Highway-Rail Grade Crossing Crash Analysis |
| title_full | A Gradient Boosting Crash Prediction Approach for Highway-Rail Grade Crossing Crash Analysis |
| title_fullStr | A Gradient Boosting Crash Prediction Approach for Highway-Rail Grade Crossing Crash Analysis |
| title_full_unstemmed | A Gradient Boosting Crash Prediction Approach for Highway-Rail Grade Crossing Crash Analysis |
| title_short | A Gradient Boosting Crash Prediction Approach for Highway-Rail Grade Crossing Crash Analysis |
| title_sort | gradient boosting crash prediction approach for highway rail grade crossing crash analysis |
| url | http://dx.doi.org/10.1155/2020/6751728 |
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