| 后疫情时代基于XGBoost的铁路客运站客流量预测研究 |
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| 引用本文： | 王平,吴文波,马毅华,许江,宗智诚.后疫情时代基于XGBoost的铁路客运站客流量预测研究[J].铁路计算机应用,2022,31(1):22-26. |
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| 作者姓名： | 王平 吴文波 马毅华 许江 宗智诚 |
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| 作者单位： | 上海申铁信息工程有限公司，上海 200071 |
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| 基金项目： | 中国铁路上海局集团有限公司科研项目(2018179)。 |
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| 摘 要： | 进入"后疫情时期",铁路客流正逐步回升,但呈现较大波动,面对铁路提质增效的任务,准确预测客流量愈发重要.文章采用极端梯度提升(XGBoost,eXtreme Gradient Boosting)模型,以新冠肺炎疫情、天气和日期属性作为影响因素,选取上海站2016年1月1日—2020年7月27日客流量数据作为训练集和验证...
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| 关 键 词： | 客流量预测 后疫情时代 XGBoost k折交叉验证 网格搜索 |
| 收稿时间： | 2021-06-22 |
Research on passenger flow forecast for railway passenger station based on XGBoost in post-pandemic era |
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| WANG Ping,WU Wenbo,MA Yihua,XU Jiang,ZONG Zhicheng.Research on passenger flow forecast for railway passenger station based on XGBoost in post-pandemic era[J].Railway Computer Application,2022,31(1):22-26. |
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| Authors: | WANG Ping WU Wenbo MA Yihua XU Jiang ZONG Zhicheng |
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| Affiliation: | Shanghai Shentie Information Engineering Co. Ltd., Shanghai 200071, China |
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| Abstract: | Entering the post-pandemic era, railway passenger flow is gradually rising, but there is a large fluctuation. Facing the task of improving the quality and operation efficiency of railway, accurate prediction of passenger flow is becoming more and more important. In this paper, XGBoost model was adopted for passenger flow forecast with COVID-19 pandemic, weather, and date attributes as influencing factors. Meanwhile, passenger flow data of Shanghai Railway Station from January 1, 2016 to July 27, 2020 were selected as training set and validation set and the optimal parameters of the XGBoost-based passenger flow forecast model were obtained by using 5-fold cross-validation and Grid Search. Then, the passenger flow of Shanghai Railway Station from July 28, 2020 to May 17, 2021 was predicted using this model. The result of the prediction attained a fitting degree of 0.812, indicating that the overall prediction effect is good. |
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| Keywords: | passenger flow forecast post-pandemic era eXtreme Gradient Boosting(XGBoost) K-fold cross-validation Grid Search |
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