基于时间序列分解方法的城市轨道交通大客流预测方法
郑俊锋1方旭峰2王晨阳1
Prediction Method for Urban Rail Transit Large Passenger Flow Based on Time Series Decomposition Method
ZHENG JunfengFANG XufengWANG Chenyang
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作者信息:1.合肥市轨道交通集团有限公司， 230001， 合肥
2.西南交通大学交通运输与物流学院， 611756， 成都
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Affiliation:Hefei Rail Transit Group Co., Ltd., 230001, Hefei, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2023.08.031
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中图分类号/CLCN:U442.55
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栏目/Col:研究报告
摘要:
目的：大型活动举办当日，周边城市轨道交通车站易产生瞬时进站大客流，需研究大型活动引发大客流的预测方法，以便及时调整运输组织方案，有效处置可预见性大客流。方法：介绍了城市轨道交通大客流定义；分析了大型活动引发可预测性大客流的客流结构特征、空间特征和时间特征；介绍了大型活动引发可预测性大客流的预测方法，并通过实例进行了预测方法的应用验证。结果及结论：根据大客流是否可预测，可分为可预测大客流和不可预测大客流；大型活动引发可预测性大客流具有明显的结构特征、空间特征和时间特征；实例验证结果表明，所提出的基于STL(Seasonal and Trend decomposition using Loess)的时间序列分解方法的LightGBM方法(即STLLightGBM)，预测精度较高，有助于充分掌握大型活动引发大客流的特征与规律。
Abstracts:
Objective: On days of largescale events taking place, URT (urban rail transit) stations in surrounding areas often experience instantaneous spike of inbound passenger flow. It is necessary to study the prediction methods for large passenger flow resulting from largescale events, so that transportation organization plans are promptly adjusted and foreseeable large passenger flow can be effectively handled. Method: The definition of URT large passenger flow is introduced. The structural, spatial, and temporal characteristics of the foreseeable large passenger flow induced by largescale events are analyzed, the prediction methods for which are presented, and their application is verified through practical examples. Result & Conclusion: Based on the predictability of large passenger flow, two categories of foreseeable and unforeseeable large passenger flows can be derived. Foreseeable large passenger flow from largescale events exhibits distinct structural, spatial, and temporal characteristics. The results of practical example verification indicate that the proposed prediction method based on STL (Seasonal and Trend decomposition using Loess) combined with LightGBM (also known as STLLightGBM) achieves high prediction accuracy. This method helps to fully understand the characteristics and patterns of large passenger flow induced by largescale events.
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