地铁站点短时客流变化规律分析及预测方法
黎家靖1张宁2温龙辉2李兆君3
Analysis and Prediction Methods of Short\|term Passenger Flow Changes at Metro Station
LI JiajingZHANG NingWEN LonghuiLI Zhaojun
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作者信息:1.中铁第四勘察设计院集团有限公司，430063，武汉
2.东南大学智能运输系统研究中心，210018，南京
3.滁州市滁宁城际铁路开发建设有限公司，239001，滁州
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Affiliation:China Railway Siyuan Survey and Design Group Co., Ltd., 430063, Wuhan, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2023.11.007
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栏目/Col:研究报告
摘要:
目的：地铁站点短时客流预测是地铁车站运营管理的重要组成部分之一，精准的客流预测结果可为地铁车站的运营计划提供数据支撑，需对地铁站点短时客流的预测方法进行系统、深入的研究。方法：以地铁南京南站（四线换乘站）为例，分析了该站点短时客流的变化规律，发现其在一周内存在3种日客流发展模式。基于此，使用STL(时间序列分解)算法和EMD（经验模态分解）算法对该站的原始进站客流进行双层分解，结合BiLSTM（双向长短期记忆网络）构建了该站的客流预测组合模型。将该组合模型的客流预测流程细分为3个阶段，选取合适的参数值，对输出的预测结果进行对比分析。最后对不同时间粒度的客流统计间隔进行试验，以验证客流统计间隔和模型预测效果的关系。结果及结论：STLEMDBiLSTM组合模型在3类日客流发展模式下的平均绝对百分比误差分别为5.0%、6.3%、6.3%。与其余5种预测模型相比，STLEMDBiLSTM组合模型的均方根误差、平均绝对误差和平均绝对百分比误差均为最优，这说明该组合模型的有效性和准确性。客流统计间隔增加，模型的预测效果随之提升。
Abstracts:
Objective: Short\|term station passenger flow prediction is one of the important components of metro operation management. Accurate prediction can provide data support for the operation planning of metro stations. Therefore, systematic and in\|depth research on short\|term passenger flow prediction methods at metro stations is needed. Method: Taking metro Nanjingnan Station (a four\|line transfer station) as an example, the changing patterns of short\|term passenger flow at this station are analyzed, and three different daily patterns are identified within a week. Based on this, the original inbound passenger flow is decomposed into two layers using the STL (seasonal and trend decomposition using Loess) algorithm and the EMD (Empirical Mode Decomposition) algorithm. Combined with BiLSTM (Bidirectional Long Short\|Term Memory) network, a composite model for prediction is constructed. The passenger flow prediction process of this composite model is divided into three stages, suitable parameter values are selected to compare and analyze the predicted results. Finally, experiments are conducted on different time granularity of passenger flow statistics intervals to verify the relationship between passenger flow statistics intervals and the effectiveness of the prediction model. Result &Conclusion: The mean absolute percentage error of the STL\|EMD\|BiLSTM combined model for the three types of daily passenger flow development patterns are 5.0%, 6.3%, and 6.3%, respectively. Compared with the other five prediction models, the root\|mean\|square error, mean absolute error, and mean absolute percentage error of the STL\|EMD\|BiLSTM combined model are the best, indicating the effectiveness and accuracy of this combined model. As the interval of passenger flow statistics increases, the predictive performance of the model improves accordingly.
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