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CEEMDAN-IPSO-LSTM: A Novel Model for Short-Term Passenger Flow Prediction in Urban Rail Transit Systems

Author

Listed:
  • Lu Zeng

    (School of Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
    State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
    Ganjiang Innovation Academy, Chinese Academy of Sciences, Ganzhou 341000, China)

  • Zinuo Li

    (School of Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China)

  • Jie Yang

    (School of Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
    Ganjiang Innovation Academy, Chinese Academy of Sciences, Ganzhou 341000, China)

  • Xinyue Xu

    (State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China)

Abstract

Urban rail transit (URT) is a key mode of public transport, which serves for greatest user demand. Short-term passenger flow prediction aims to improve management validity and avoid extravagance of public transport resources. In order to anticipate passenger flow for URT, managing nonlinearity, correlation, and periodicity of data series in a single model is difficult. This paper offers a short-term passenger flow prediction combination model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and long-short term memory neural network (LSTM) in order to more accurately anticipate the short-period passenger flow of URT. In the meantime, the hyperparameters of LSTM were calculated using the improved particle swarm optimization (IPSO). First, CEEMDAN-IPSO-LSTM model performed the CEEMDAN decomposition of passenger flow data and obtained uncoupled intrinsic mode functions and a residual sequence after removing noisy data. Second, we built a CEEMDAN-IPSO-LSTM passenger flow prediction model for each decomposed component and extracted prediction values. Third, the experimental results showed that compared with the single LSTM model, CEEMDAN-IPSO-LSTM model reduced by 40 persons/35 persons, 44 persons/35 persons, 37 persons/31 persons, and 46.89%/35.1% in SD, RMSE, MAE, and MAPE, and increase by 2.32%/3.63% and 2.19%/1.67% in R and R 2 , respectively. This model can reduce the risks of public health security due to excessive crowding of passengers (especially in the period of COVID-19), as well as reduce the negative impact on the environment through the optimization of traffic flows, and develop low-carbon transportation.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:24:p:16433-:d:996723
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    References listed on IDEAS

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    Cited by:

    1. Mariusz Korzeń & Maciej Kruszyna, 2023. "Modified Ant Colony Optimization as a Means for Evaluating the Variants of the City Railway Underground Section," IJERPH, MDPI, vol. 20(6), pages 1-15, March.
    2. Majerčák Peter & Majerčák Jozef & Kurenkov Petr Vladimirovič, 2023. "Impact of the COVID Crisis on Public Passenger Transport in Slovakia and Urban Transport in Žilina on a Selected Line," LOGI – Scientific Journal on Transport and Logistics, Sciendo, vol. 14(1), pages 169-180, January.

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