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Predicting Highway–Rail Grade Crossing Collision Risk by Neural Network Systems
| 题名： | Predicting Highway–Rail Grade Crossing Collision Risk by Neural Network Systems |
| 正文语种： | 英文 |
| 作者： | Zijian Zheng1; Pan Lu, Ph.D.2; Danguang Pan, Ph.D.3 |
| 作者单位： | 1Research Assistant, Upper Great Plains Transportation Institute, North Dakota State Univ., NDSU Dept. 2880, P.O. Box 6050, Fargo, ND 58108-6050. 2Associate Professor, Dept. of Transportation and Logistics, Upper Great Plains Transportation Institute, North Dakota State Univ., NDSU Dept. 2880, P.O. Box 6050, Fargo, ND 58108-6050 (corresponding author). 3Professor, Dept. of Civil Engineering, Univ. of Science and Technology Beijing, No. 30, Xueyuan Rd., Haidian District, Beijing 100083, PR China. |
| 关键词： | different；characteristics；relationship；application；connection；vari；predict；network；principal；aria |
| 摘要： | In this paper, train-vehicle crash risk at highway–rail grade crossings (HRGCs) is analyzed with a neural network (NN) model to return meaningful rankings of crash-contributory-variable importance based on different criteria, but also to produce dependent nonlinear contributor-crash curves with all other contributors considered for a specific contributor variable. Historical crash data for North Dakota public HRGCs from 1996 to 2014 were used for the study. Several principal findings were observed: (1) 22 input variables describing traffic characteristics and crossing characteristics are related to crashes at public HRGCs; (2) a mean-square error–based NN model and a connection weights–based NN model represent two relative contributory-variable importance lists for different application purposes; (3) the effect of different variables on crash likelihood is different when all other contributors are set at different levels, and the relationship between contributors and crash likelihood is dynamic nonlinear; and (4) in predictive and explanatory power, the neural network model outperforms the decision tree approach for the considered case study. |
| 出版年： | 2019 |
| 期刊名称： | Journal of Transportation Engineering |
| 卷： | 145 |
| 期： | 8 |
| 页码： | 1-8 |
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