Application research of passenger flow forecasting method for large railway station based on support vector regression
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摘要: 准确预测大型客运站发送客流量，是铁路依据旅客出行需求制订开行方案、编制运行图和完成客流输送任务的重要基础。简要介绍支持向量回归的概念和原理；以汉口车站2017年1月—12月日实际发送客流量作为样本数据集，分析大型铁路客运车站客流特点，即年度客流呈现明显周期波动性、长周期内因多次节假日出现客流大幅激增；将样本数据集分为训练集及测试集，利用支持向量回归模型对剔除节假日前后的客流量进行预测，预测误差对比表明：排除节假日突发大客流的影响后，由支持向量回归模型计算得到车站日常发送客流量的预测精度可明显提高。Abstract: Accurate prediction of the number of passengers sent at large train stations is one of the main basis for the compilation of train operation plan and train timetables to complete the task of passenger trasportation based on passenger travel demand. Firstly, this paper gives a brief introduction to the theory and principle of support vector regression. Taking the actual daily passenger flow of Hankou Station from January to December in 2017 as the sample data set, the characteristics of passenger flow of the large railway station are analyzed. And the analysis shows that the annual passenger flow fluctuates in obvious cycles and the passenger flow spikes suddenly due to several holidays in a long period. The sample data set is then divided into one training set and one test set and the numbers of passengers daily sent at the station before and after the elimination of holidays are respectively predicted by using the support vector regression model and the comparison of the errors of the predication results indicates that the accuracy of the number of passengers sent at a station derived by using this model can be enhanced subtantially after eliminating the impact of sudden spikes of holidays' passenger flow.
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表 1 排除节假日前后的汉口站发送客流量预测误差对比
误差指标 包含节假日 剔除节假日 RMSE 8099.46 2721.42 MAE 1918.38 880.31 R2 0.5295 0.8999 
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