RAILWAY PASSENGER TRAFFIC VOLUME PREDICTION BASED ON NEURAL NETWORK

Authors:
Wang Zhuo;Jia Li-Min;Qin Yong;Wang Yan-hui
Affiliations:
School of Traffic and Transportation, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China;School of Traffic and Transportation, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China;School of Traffic and Transportation, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China;School of Traffic and Transportation, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China
Venue:
Applied Artificial Intelligence
Year:
2007

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Abstract

In order to overcome the shortcomings of the methods in railway passenger traffic volume prediction, an improved BP neural network method is adopted to predict railway passenger traffic volume. By analyzing the improved BP neural network theory, the neural network prediction model of railway passenger traffic volume is set up. The network learning and training simulation experiment is carried out on the data of railway passenger traffic volume from 1980-1998. Comparing with standard BP neural network, the improved BP neural network presents more accurate and reliable prediction results and faster learning speed. The improved BP neural network provides a new and feasible thought to predict railway passenger traffic volume, which can also be used to predict other relative problems.