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Short-Term Subway Passenger Flow Prediction Based on Time Series Adaptive Decomposition and Multi-Model Combination (IVMD-SE-MSSA)

Author

Listed:
  • Xianwang Li

    (College of Mechanical Engineering, Guangxi University, Nanning 530004, China)

  • Zhongxiang Huang

    (College of Mechanical Engineering, Guangxi University, Nanning 530004, China)

  • Saihu Liu

    (College of Mechanical Engineering, Guangxi University, Nanning 530004, China)

  • Jinxin Wu

    (College of Mechanical Engineering, Guangxi University, Nanning 530004, China)

  • Yuxiang Zhang

    (College of Mechanical Engineering, Guangxi University, Nanning 530004, China)

Abstract

The accurate forecasting of short-term subway passenger flow is beneficial for promoting operational efficiency and passenger satisfaction. However, the nonlinearity and nonstationarity of passenger flow time series bring challenges to short-term passenger flow prediction. To solve this challenge, a prediction model based on improved variational mode decomposition (IVMD) and multi-model combination is proposed. Firstly, the mixed-strategy improved sparrow search algorithm (MSSA) is used to adaptively determine the parameters of the VMD with envelope entropy as the fitness value. Then, IVMD is applied to decompose the original passenger flow time series into several sub-series adaptively. Meanwhile, the sample entropy is utilized to divide the sub-series into high-frequency and low-frequency components, and different models are established to predict the sub-series with different frequencies. Finally, the MSSA is employed to determine the weight coefficients of each sub-series to combine the prediction results of the sub-series and get the final passenger flow prediction results. To verify the prediction performance of the established model, passenger flow datasets from four different types of Nanning Metro stations were taken as examples for carrying out experiments. The experimental results showed that: (a) The proposed hybrid model for short-term passenger flow prediction is superior to several baseline models in terms of both prediction accuracy and versatility. (b) The proposed hybrid model is excellent in multi-step prediction. Taking station 1 as an example, the MAEs of the proposed model are 3.677, 5.7697, and 8.1881, respectively, which can provide technical support for subway operations management.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:7949-:d:1145599
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    References listed on IDEAS

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    1. Bin Qiu & Yun Zhao, 2022. "Research on Improved Traffic Flow Prediction Network Based on CapsNet," Sustainability, MDPI, vol. 14(23), pages 1-17, November.
    2. Zhou, Tuqiang & Wu, Wanting & Peng, Liqun & Zhang, Mingyang & Li, Zhixiong & Xiong, Yubing & Bai, Yuelong, 2022. "Evaluation of urban bus service reliability on variable time horizons using a hybrid deep learning method," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    3. Binglei Xie & Yu Sun & Xiaolong Huang & Le Yu & Gangyan Xu, 2020. "Travel Characteristics Analysis and Passenger Flow Prediction of Intercity Shuttles in the Pearl River Delta on Holidays," Sustainability, MDPI, vol. 12(18), pages 1-23, September.
    4. 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.
    5. Xinqiang Chen & Jinquan Lu & Jiansen Zhao & Zhijian Qu & Yongsheng Yang & Jiangfeng Xian, 2020. "Traffic Flow Prediction at Varied Time Scales via Ensemble Empirical Mode Decomposition and Artificial Neural Network," Sustainability, MDPI, vol. 12(9), pages 1-17, May.
    6. Bontempi, Gianluca & Ben Taieb, Souhaib, 2011. "Conditionally dependent strategies for multiple-step-ahead prediction in local learning," International Journal of Forecasting, Elsevier, vol. 27(3), pages 689-699, July.
    7. Xiaolei Ma & Haiyang Yu & Yunpeng Wang & Yinhai Wang, 2015. "Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-17, March.
    8. Krishna Rayi, Vijaya & Mishra, S.P. & Naik, Jyotirmayee & Dash, P.K., 2022. "Adaptive VMD based optimized deep learning mixed kernel ELM autoencoder for single and multistep wind power forecasting," Energy, Elsevier, vol. 244(PA).
    9. Omer Saud Azeez & Biswajeet Pradhan & Helmi Z. M. Shafri, 2018. "Vehicular CO Emission Prediction Using Support Vector Regression Model and GIS," Sustainability, MDPI, vol. 10(10), pages 1-18, September.
    10. Kui Yang & Bofu Wang & Xiang Qiu & Jiahua Li & Yuze Wang & Yulu Liu, 2022. "Multi-Step Short-Term Wind Speed Prediction Models Based on Adaptive Robust Decomposition Coupled with Deep Gated Recurrent Unit," Energies, MDPI, vol. 15(12), pages 1-24, June.
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