IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i22p3556-d1520861.html
   My bibliography  Save this article

Passenger Flow Prediction for Rail Transit Stations Based on an Improved SSA-LSTM Model

Author

Listed:
  • Xing Zhao

    (College of Civil and Transportation Engineering, Hohai University, No. 1, Xikang Road, Nanjing 210098, China)

  • Chenxi Li

    (College of Civil and Transportation Engineering, Hohai University, No. 1, Xikang Road, Nanjing 210098, China)

  • Xueting Zou

    (College of Civil and Transportation Engineering, Hohai University, No. 1, Xikang Road, Nanjing 210098, China)

  • Xiwang Du

    (Jsti Group, No. 8, Fuchun Jiangdong Street, Jianye District, Nanjing 210019, China)

  • Ahmed Ismail

    (College of Civil and Transportation Engineering, Hohai University, No. 1, Xikang Road, Nanjing 210098, China)

Abstract

Accurate and timely passenger flow prediction is important for the successful deployment of rail transit intelligent operation. The Sparrow Search Algorithm (SSA) has been applied to the parameter optimization of a Long-Short-Term Memory (LSTM) model. To solve the inherent weaknesses of SSA, this paper proposes an improved SSA-LSTM model with optimization strategies including Tent Map and Levy Flight to practice the short-term prediction of boarding passenger flow at rail transit stations. Aimed at the passenger flow at four rail transit stations in Nanjing, China, it is found that the day of a week and rainfall are the influencing factors with the highest correlation. On this basis, we apply the proposed SSA-LSTM and four baseline models to realize the short-term prediction, and carry out the prediction experiments with different time granularities. According to the experimental results, the proposed SSA-LSTM model has a more effective performance than the Support Vector Regression (SVR) method, the eXtreme Gradient Boosting (XGBoost) model, the traditional LSTM model, and the improved LSTM model with the Whale Optimization Algorithm (WOA-LSTM) in the passenger flow prediction. In addition, for most stations, the prediction accuracy of the proposed SSA-LSTM model is greater at a larger time granularity, but there are still exceptions.

Suggested Citation

  • Xing Zhao & Chenxi Li & Xueting Zou & Xiwang Du & Ahmed Ismail, 2024. "Passenger Flow Prediction for Rail Transit Stations Based on an Improved SSA-LSTM Model," Mathematics, MDPI, vol. 12(22), pages 1-24, November.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:22:p:3556-:d:1520861
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/22/3556/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/22/3556/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Peng, Yanni & Xiang, Wanli, 2020. "Short-term traffic volume prediction using GA-BP based on wavelet denoising and phase space reconstruction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fei Qu & Yi-Ting Wang & Wen-Hui Hou & Xiao-Yu Zhou & Xiao-Kang Wang & Jun-Bo Li & Jian-Qiang Wang, 2022. "Forecasting of Automobile Sales Based on Support Vector Regression Optimized by the Grey Wolf Optimizer Algorithm," Mathematics, MDPI, vol. 10(13), pages 1-22, June.
    2. Yingli Wu & Xin Li & Qingquan Liu & Guangji Tong, 2022. "The Analysis of Credit Risks in Agricultural Supply Chain Finance Assessment Model Based on Genetic Algorithm and Backpropagation Neural Network," Computational Economics, Springer;Society for Computational Economics, vol. 60(4), pages 1269-1292, December.
    3. Chen, Xinqiang & Chen, Huixing & Yang, Yongsheng & Wu, Huafeng & Zhang, Wenhui & Zhao, Jiansen & Xiong, Yong, 2021. "Traffic flow prediction by an ensemble framework with data denoising and deep learning model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    4. Sariyer, Gorkem & Mangla, Sachin Kumar & Sozen, Mert Erkan & Li, Guo & Kazancoglu, Yigit, 2024. "Leveraging explainable artificial intelligence in understanding public transportation usage rates for sustainable development," Omega, Elsevier, vol. 127(C).
    5. Zeng, Chao & Ma, Changxi & Wang, Ke & Cui, Zihao, 2022. "Predicting vacant parking space availability: A DWT-Bi-LSTM model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    6. Hou, Yue & Zhang, Di & Li, Da & Deng, Zhiyuan, 2024. "Regional traffic flow combination prediction model considering virtual space of the road network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    7. Ma, Changxi & Zhao, Mingxi & Huang, Xiaoting & Zhao, Yongpeng, 2024. "Optimized deep extreme learning machine for traffic prediction and autonomous vehicle lane change decision-making," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    8. Yang, Hongtai & Ping, An & Wei, Hongmin & Zhai, Guocong, 2023. "Unique in the metro system: The likelihood to re-identify a metro user with limited trajectory points," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(C).
    9. Min Li & Mengshan Li & Bilong Liu & Jiang Liu & Zhen Liu & Dijia Luo, 2022. "Spatio-Temporal Traffic Flow Prediction Based on Coordinated Attention," Sustainability, MDPI, vol. 14(12), pages 1-17, June.
    10. Ma, Changxi & Zhao, Mingxi, 2023. "Spatio-temporal multi-graph convolutional network based on wavelet analysis for vehicle speed prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    11. Wang, Ke & Ma, Changxi & Qiao, Yihuan & Lu, Xijin & Hao, Weining & Dong, Sheng, 2021. "A hybrid deep learning model with 1DCNN-LSTM-Attention networks for short-term traffic flow prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    12. Huang, Haichao & Chen, Jingya & Sun, Rui & Wang, Shuang, 2022. "Short-term traffic prediction based on time series decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:22:p:3556-:d:1520861. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.