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

Author

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  • Pengpeng Jiao
  • Ruimin Li
  • Tuo Sun
  • Zenghao Hou
  • Amir Ibrahim

Abstract

Short-term prediction of passenger flow is very important for the operation and management of a rail transit system. Based on the traditional Kalman filtering method, this paper puts forward three revised models for real-time passenger flow forecasting. First, the paper introduces the historical prediction error into the measurement equation and formulates a revised Kalman filtering model based on error correction coefficient (KF-ECC). Second, this paper employs the deviation between real-time passenger flow and corresponding historical data as state variable and presents a revised Kalman filtering model based on Historical Deviation (KF-HD). Third, the paper integrates nonparametric regression forecast into the traditional Kalman filtering method using a Bayesian combined technique and puts forward a revised Kalman filtering model based on Bayesian combination and nonparametric regression (KF-BCNR). A case study is implemented using statistical passenger flow data of rail transit line 13 in Beijing during a one-month period. The reported prediction results show that KF-ECC improves the applicability to historical trend, KF-HD achieves excellent accuracy and stability, and KF-BCNR yields the best performances. Comparisons among different periods further indicate that results during peak periods outperform those during nonpeak periods. All three revised models are accurate and stable enough for on-line predictions, especially during the peak periods.

Suggested Citation

  • Pengpeng Jiao & Ruimin Li & Tuo Sun & Zenghao Hou & Amir Ibrahim, 2016. "Three Revised Kalman Filtering Models for Short-Term Rail Transit Passenger Flow Prediction," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-10, March.
  • Handle: RePEc:hin:jnlmpe:9717582
    DOI: 10.1155/2016/9717582
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    Cited by:

    1. Fenling Feng & Zhaohui Zou & Chengguang Liu & Qianran Zhou & Chang Liu, 2023. "Forecast of Short-Term Passenger Flow in Multi-Level Rail Transit Network Based on a Multi-Task Learning Model," Sustainability, MDPI, vol. 15(4), pages 1-17, February.
    2. Yang, Xin & Xue, Qiuchi & Ding, Meiling & Wu, Jianjun & Gao, Ziyou, 2021. "Short-term prediction of passenger volume for urban rail systems: A deep learning approach based on smart-card data," International Journal of Production Economics, Elsevier, vol. 231(C).
    3. Sun, He, 2023. "Deep Learning and Bayesian Calibration Approach to Hourly Passenger Occupancy Prediction in Beijing Metro: A Study Exploiting Cellular Data and Metro Conditions," DES - Working Papers. Statistics and Econometrics. WS 38783, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Zhang, Qian & Liu, Xiaoxiao & Spurgeon, Sarah & Yu, Dingli, 2021. "A two-layer modelling framework for predicting passenger flow on trains: A case study of London underground trains," Transportation Research Part A: Policy and Practice, Elsevier, vol. 151(C), pages 119-139.
    5. Yu Zhang & Xiaodan Wang & Jingjing Xie & Yun Bai, 2024. "Comparative analysis of deep-learning-based models for hourly bus passenger flow forecasting," Transportation, Springer, vol. 51(5), pages 1759-1784, October.
    6. Lu Zeng & Zinuo Li & Jie Yang & Xinyue Xu, 2022. "CEEMDAN-IPSO-LSTM: A Novel Model for Short-Term Passenger Flow Prediction in Urban Rail Transit Systems," IJERPH, MDPI, vol. 19(24), pages 1-19, December.
    7. Xianwang Li & Zhongxiang Huang & Saihu Liu & Jinxin Wu & Yuxiang Zhang, 2023. "Short-Term Subway Passenger Flow Prediction Based on Time Series Adaptive Decomposition and Multi-Model Combination (IVMD-SE-MSSA)," Sustainability, MDPI, vol. 15(10), pages 1-30, May.
    8. Li, Peng & Wu, Weitiao & Pei, Xiangjing, 2023. "A separate modelling approach for short-term bus passenger flow prediction based on behavioural patterns: A hybrid decision tree method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 616(C).

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