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Urban Rail Transit Train Dwell Time Analysis Based on Random Forest Algorithm: A Case Study on the Beidajie Station of the Xi'an Metro in China
| 题名： | Urban Rail Transit Train Dwell Time Analysis Based on Random Forest Algorithm: A Case Study on the Beidajie Station of the Xi'an Metro in China |
| 正文语种： | eng |
| 作者： | Feng Zhou;Wenyu Wang;Fangsheng Wang;Ruihua Xu;Ling Hong |
| 作者单位： | Key Laboratory of Road and Traffic Engineering Ministry of Education Tongji Univ. 4800 Cao'an Rd. Jiading District Shanghai 201804 China;Key Laboratory of Road and Traffic Engineering Ministry of Education Tongji Univ. 4800 Cao'an Rd. Jiading District Shanghai 201804 China;Key Laboratory of Road and Traffic Engineering Ministry of Education Tongji Univ. 4800 Cao'an Rd. Jiading District Shanghai 201804 China;Key Laboratory of Road and Traffic Engineering Ministry of Education Tongji Univ. 4800 Cao'an Rd. Jiading District Shanghai 201804 China;Key Laboratory of Road and Traffic Engineering Ministry of Education Tongji Univ. 4800 Cao'an Rd. Jiading District Shanghai 201804 China |
| 关键词： | Urban rail transit; Dwell time (DT) analysis; Data mining; Random forest (RF); Feature extraction |
| 摘要： | The dwell time (DT) is an essential element in the compilation of urban rail transit train diagrams. To improve the reliability of urban rail transit operations, it is necessary to identify the major factors that influence the actual DT and formulate effective measures. Via data mining, this study proposes a random forest (RF) based identification model to diagnose the major problems that affect the DT at different stations in different periods and put forward corresponding targeted measures. Considering the Beidajie (BDJ) station of Xi'an Metro in the upstream direction of Line 2, this model sensitively identifies major factors leading to the variation in DT during the morning peak, consistent with the detailed analysis. Then, the targeted improvement measures are proposed for the BDJ station. The obtained application results indicate that the established influencing factor identification model can effectively dig out the factors causing a discrepancy between actual and scheduled DT, which can provide auxiliary decision making in the compilation of train diagrams and daily operation organization. |
| 出版年： | 2023 |
| 期刊名称： | Journal of Transportation Engineering |
| 卷： | 149 |
| 期： | 7 |
| 页码： | 04023057.1-04023057.9 |
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