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Mathematical Problems in Engineering
Volume 2016, Article ID 9717582, 10 pages
http://dx.doi.org/10.1155/2016/9717582
Research Article

Three Revised Kalman Filtering Models for Short-Term Rail Transit Passenger Flow Prediction

1Beijing Urban Transportation Infrastructure Engineering Technology Research Center, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
2Institute of Transportation Engineering, Tsinghua University, Beijing 100084, China
3Parsons Transportation Group, 100 Broadway, New York, NY 10005, USA
4New Jersey Department of Transportation (NJDOT), 1035 Parkway Avenue, Trenton, NJ 08625, USA

Received 16 December 2015; Accepted 10 March 2016

Academic Editor: Payman Jalali

Copyright © 2016 Pengpeng Jiao 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.

Citations to this Article [5 citations]

The following is the list of published articles that have cited the current article.

  • Zhong-Sheng Xiao, Bao-Hua Mao, and Tong Zhang, “Integrated predicting model for daily passenger volume of rail transit station based on neural network and Markov chain,” 2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), pp. 578–583, . View at Publisher · View at Google Scholar
  • Rung-Ching Chen, and Lijuan Liu, “A novel passenger flow prediction model using deep learning methods,” Transportation Research Part C: Emerging Technologies, vol. 84, pp. 74–91, 2017. View at Publisher · View at Google Scholar
  • Lijuan Liu, Qiangfu Zhao, Rung-Ching Chen, and Shunzhi Zhu, “Applying a multistage of input feature combination to random forest for improving MRT passenger flow prediction,” Journal of Ambient Intelligence and Humanized Computing, 2018. View at Publisher · View at Google Scholar
  • Mariano Gallo, Giuseppina De Luca, Luca D’Acierno, and Marilisa Botte, “Artificial Neural Networks for Forecasting Passenger Flows on Metro Lines,” Sensors, vol. 19, no. 15, pp. 3424, 2019. View at Publisher · View at Google Scholar
  • Yang Liu, Zhiyuan Liu, and Ruo Jia, “DeepPF: A deep learning based architecture for metro passenger flow prediction,” Transportation Research Part C: Emerging Technologies, vol. 101, pp. 18–34, 2019. View at Publisher · View at Google Scholar