Passenger Flow Prediction of Subway Transfer Stations Based on Nonparametric Regression Model.

Yujuan Sun; Guanghou Zhang; Huanhuan Yin

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.

CHINA; SUBWAYS; TRAFFIC congestion; REGRESSION analysis; KALMAN filtering; SUPPORT vector machines

Discrete Dynamics in Nature & Society, 2014, p1

1026-0226

Academic Journal

10.1155/2014/397154