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Computational Intelligence and Neuroscience
Volume 2014 (2014), Article ID 375487, 8 pages
http://dx.doi.org/10.1155/2014/375487
Research Article

Forecasting the Short-Term Passenger Flow on High-Speed Railway with Neural Networks

School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China

Received 24 July 2014; Revised 4 October 2014; Accepted 4 October 2014; Published 4 November 2014

Academic Editor: Yongjun Shen

Copyright © 2014 Mei-Quan Xie 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

Short-term passenger flow forecasting is an important component of transportation systems. The forecasting result can be applied to support transportation system operation and management such as operation planning and revenue management. In this paper, a divide-and-conquer method based on neural network and origin-destination (OD) matrix estimation is developed to forecast the short-term passenger flow in high-speed railway system. There are three steps in the forecasting method. Firstly, the numbers of passengers who arrive at each station or depart from each station are obtained from historical passenger flow data, which are OD matrices in this paper. Secondly, short-term passenger flow forecasting of the numbers of passengers who arrive at each station or depart from each station based on neural network is realized. At last, the OD matrices in short-term time are obtained with an OD matrix estimation method. The experimental results indicate that the proposed divide-and-conquer method performs well in forecasting the short-term passenger flow on high-speed railway.