Chapter
Jul 2, 2019
Forecasting Short-Term Entrance Passenger Flow of Urban Rail Transit Stations by the Improved Elman Neural Network
Authors: Tao Bi [email protected], Jiantao He [email protected], Lingling Ma [email protected], Qiao Xie [email protected], and Mao Ye [email protected]Author Affiliations
Publication: CICTP 2019
Abstract
Passenger flow forecasting is the basis of the rail transit operation department. In the context of the big data era, this paper proposes a new short-term incoming passenger flow forecasting method for rail transit. In this paper, an improved neural network model is trained on the spark distributed parallel computing framework to reduce training time, and a new method to divide data types is proposed. In order to select the input of the model, this paper implements a correlation measure method to characterize the relationship between the influencing factors and the predicted target. Finally, the proposed model is validated by the actual passenger flow data of Guangzhou Metro. The results show that the prediction accuracy of passenger flow is ideal and meets the project requirements.
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Published In
CICTP 2019
Pages: 1651 - 1659
Copyright
© 2019 American Society of Civil Engineers.
History
Published online: Jul 2, 2019
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Affiliations
Nanjing Univ. of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China. E-mail: [email protected]
Guangzhou Metro Group Co., Ltd., 1238 Xinggang East Rd., Haizhu District, Guangzhou 510330, China. E-mail: [email protected]
Nanjing Univ. of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China. E-mail: [email protected]
Guangzhou Metro Group Co., Ltd., 1238 Xinggang East Rd., Haizhu District, Guangzhou 510330, China. E-mail: [email protected]
Nanjing Univ. of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China. E-mail: [email protected]
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