Neural network based temporal feature models for short-term railway passenger demand forecasting

Authors:
Tsung-Hsien Tsai;Chi-Kang Lee;Chien-Hung Wei
Affiliations:
School of Hotel Administration, Cornell University, 214 Texas Lane, Ithaca, New York 14850, USA;Department of Marketing and Logistics, Southern Taiwan University of Technology, 1, Nantai Street, Yongkang City, Tainan County 710, Taiwan;Department of Transportation and Communication Management Science, National Cheng Kung University, 1, University Road, Tainan 701, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

Accurate forecasts are the base for correct decisions in revenue management. This paper addresses two novel neural network structures for short-term railway passenger demand forecasting. An idea to render information at suitable places rather than mixing all available information at the beginning in neural network operations is proposed. The first proposed network structure is multiple temporal units neural network (MTUNN), which deals with distinctive input information via designated connections in the network. The second proposed network structure is parallel ensemble neural network (PENN), which deals with different input information in several individual models. The outputs of the individual models are then integrated to obtain final forecasts. Conventional multi-layer perceptron (MLP) is also constructed for comparison purposes. The results show that both MTUNN and PENN outperform conventional MLP in the study. On average, MTUNN can obtain 8.1% improvement of MSE and 4.4% improvement of MAPE in comparison with MLP. PENN can achieve 10.5% improvement of MSE and 3.3% improvement of MAPE in comparison with MLP.