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Multi-step Time Series Forecasting of Bus Passenger Flow with Deep Learning Methods

In: Liss 2020

Author

Listed:
  • Feng Jiao

    (Beijing Jiaotong University)

  • Lei Huang

    (Beijing Jiaotong University)

  • Zetian Gao

    (Warwick University)

Abstract

Currently, bus is the major transportation option of the public, with nearly 9 million passengers travelling by bus every day in Beijing, with a result that the bus transportation system in Beijing has experienced huge challenges due to the large volumes of passenger flows. To solve the issues, it is necessary to predict the short-term passenger flow in an accurate way, which allows the schedule system of Beijing Public Transport Corporation to be more efficient, and then to provide better passenger services. In this study, the first step is to clean the bus and weather data and fuse them into a multi-dimensional data set. Then, the bus route 651 was chosen as the research objective, 5 min as time step in prediction. The research built one-step and multi-step prediction models by using LSTM and GRU. In the final step, we would evaluate the prediction performance between distinct prediction models with different hyperparameters. The result reveals that LSTM performs better in multi-step prediction model for route 651.

Suggested Citation

  • Feng Jiao & Lei Huang & Zetian Gao, 2021. "Multi-step Time Series Forecasting of Bus Passenger Flow with Deep Learning Methods," Springer Books, in: Shifeng Liu & Gábor Bohács & Xianliang Shi & Xiaopu Shang & Anqiang Huang (ed.), Liss 2020, pages 539-553, Springer.
    • Handle: RePEc:spr:sprchp:978-981-33-4359-7_38
    DOI: 10.1007/978-981-33-4359-7_38
    as

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