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Discrete Dynamics in Nature and Society
Volume 2014, Article ID 397154, 8 pages
http://dx.doi.org/10.1155/2014/397154
Research Article

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

1College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, China
2Institute of Comprehensive Transportation of NDRC, Beijing 100038, China
3Research Institute of Highway Ministry of Transport, Beijing 100088, China

Received 10 November 2013; Revised 30 March 2014; Accepted 2 April 2014; Published 24 April 2014

Academic Editor: Huimin Niu

Copyright © 2014 Yujuan Sun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.