Passenger Flow Prediction of Subway Transfer Stations Based on Nonparametric Regression Model
Passenger Flow Prediction of Subway Transfer Stations Based on Nonparametric Regression Model
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DOI：
10.1155/2014/397154
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发表时间：
2014-01-01
影响因子：
1.4
通讯作者：
Yin, Huanhuan
中科院分区：
文献类型：
--
作者：
Sun, Yujuan;Zhang, Guanghou;Yin, Huanhuan
随着中国大城市地铁网络的建成，客流量急剧增加。换乘站作为地铁线路的汇聚节点，由于不同线路之间的换乘需求较大，需要承担更多的乘客。然后，换乘设施不得不面临巨大的压力，如行人拥堵或其他异常情况。为了避免行人拥堵或在行人拥堵发生前预警管理，对换乘客流进行预测来预测行人拥堵是非常必要的。基于非参数回归理论，建立了客运客流预测模型。为了验证和说明预测模型，利用西单中转站一个月的换乘客流数据对模型进行了标定和验证。通过与卡尔曼滤波模型和支持向量机回归模型的比较，结果表明，非参数回归模型具有精度高、移植能力强的优点，能够准确预测不同区间的换乘客流。
Passenger flow is increasing dramatically with accomplishment of subway network system in big cities of China. As convergence nodes of subway lines, transfer stations need to assume more passengers due to amount transfer demand among different lines. Then, transfer facilities have to face great pressure such as pedestrian congestion or other abnormal situations. In order to avoid pedestrian congestion or warn the management before it occurs, it is very necessary to predict the transfer passenger flow to forecast pedestrian congestions. Thus, based on nonparametric regression theory, a transfer passenger flow prediction model was proposed. In order to test and illustrate the prediction model, data of transfer passenger flow for one month in XIDAN transfer station were used to calibrate and validate the model. By comparing with Kalman filter model and support vector machine regression model, the results show that the nonparametric regression model has the advantages of high accuracy and strong transplant ability and could predict transfer passenger flow accurately for different intervals.