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Improved Two-Layer Stacking Model for Prediction of the Level of Delay Caused by Crashes: An Empirical Analysis of Texas
| 题名： | Improved Two-Layer Stacking Model for Prediction of the Level of Delay Caused by Crashes: An Empirical Analysis of Texas |
| 正文语种： | eng |
| 作者： | Zehao Wang;Pengpeng Jiao;Jianyu Wang;Wei Luo;Huapu Lu |
| 作者单位： | Beijing Key Laboratory of General Aviation Technology Beijing Univ. of Civil Engineering and Architecture 15 Yongyuan Rd. Beijing 100044 China;Beijing Key Laboratory of General Aviation Technology Beijing Univ. of Civil Engineering and Architecture 15 Yongyuan Rd. Beijing 100044 China;Beijing Key Laboratory of General Aviation Technology Beijing Univ. of Civil Engineering and Architecture 15 Yongyuan Rd. Beijing 100044 China;Beijing Key Laboratory of General Aviation Technology Beijing Univ. of Civil Engineering and Architecture 15 Yongyuan Rd. Beijing 100044 China;Institute of Transportation Engineering and Geomatics Tsinghua Univ. 30 Shuangqing Rd. Beijing 100084 China |
| 关键词： | Road crash; Level of delay caused by crashes (LDC); Stacking model; Multiobjective feature selection (FS); Ensemble selection (ES) |
| 摘要： | Road crashes cause significant traffic delay, which can bring unnecessary financial losses. The objective of this study is to predict the level of delay caused by crashes (LDC) and discuss significant risk factors. To ensure the efficiency and accuracy of prediction, an improved stacking model was developed using Texas crash data of 2020. The first layer integrates seven base classifiers and the second layer tests three classifiers with different advantages. To improve and simplify the stacking model, three state-of-the-art methods-Bayesian hyperparameter optimization (BO), multiobjective feature selection (FS), and ensemble selection (ES)-were used. First, the hyperpara-meters and the least and most effective features were selected for each base classifier by BO and FS, respectively. Then ES, considering diversity and performance, selects the least base classifiers to reduce the input of the second layer. Finally, permutation feature importance was used to interpret the best stacking model. The results indicate that the stacking model achieves superior performance on four indicators: recall, G mean, F1 score, and area under the receiver operating characteristic (ROC) curve (AUC-ROC). FS significantly improves the efficiency of the stacking model and ES obtains a simplified stacking model without significantly reducing performance. In addition, the combination of the two methods (FS and ES) tends to achieve the best performance, and six risk factors have the greatest contributions in prediction using permutation feature importance. The prediction of LDC and the analysis of the main contributing factors help road managers respond to the rescue strategies to mitigate traffic congestion caused by crashes in a timely manner, thus minimizing economic losses. |
| 出版年： | 2023 |
| 期刊名称： | Journal of Transportation Engineering |
| 卷： | 149 |
| 期： | 2 |
| 页码： | 05022008.1-05022008.17 |
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