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

Tsung Hsien Tsai, Chi Kang Lee, Chien Hung Wei

研究成果: Article同行評審

176 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)3728-3736
頁數9
期刊Expert Systems With Applications
36
發行號2 PART 2
DOIs
出版狀態Published - 2009 3月

All Science Journal Classification (ASJC) codes

  • 一般工程
  • 電腦科學應用
  • 人工智慧

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