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Prediction of Truck-Involved Crsh Severity on Rurl Mountinous Freewy Using Trnsfer Lerning with ResNet-50 Deep Neurl Network
| 题名： | Prediction of Truck-Involved Crsh Severity on Rurl Mountinous Freewy Using Trnsfer Lerning with ResNet-50 Deep Neurl Network |
| 正文语种： | eng |
| 作者： | Nasim Khan;Anik Das;Mohamed M. Ahmed |
| 作者单位： | Civil Engineering Program Ingram School of Engineering Texas State Univ. RFM 5224 601 University Dr. San Marcos TX 78666;Research and Implementation Program Texas A&M Transportation Institute 1111 RELLIS Pkwy. Room 3448 Bryan TX 77807-3135;Dept. |
| 关键词： | Crash severity; Heavy trucks; Prediction; ResNet; Deep neural networks; Deeplnsight; Synthetic minority oversampling technique (SMOT) |
| 摘要： | Crashes involving heavy trucks on rural mountainous freeways are known to result in severe injuries and fatalities, particularly under challenging driving conditions. This study aims to develop a robust model to accurately predict fatal and injury crashes |
| 出版年： | 2024 |
| 期刊名称： | Journal of Transportation Engineering, Part A: Systems |
| 卷： | 150 |
| 期： | 2 |
| 页码： | 04023131.1-04023131.18 |
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