IDEAS home Printed from https://ideas.repec.org/a/gam/jforec/v4y2022i4p49-924d974723.html

Forecasting Daily and Weekly Passenger Demand for Urban Rail Transit Stations Based on a Time Series Model Approach

Author

Listed:
  • Dung David Chuwang

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China)

  • Weiya Chen

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
    Rail Data Research and Application Key Laboratory of Hunan Province, Changsha 410075, China)

Abstract

Forecasting daily and weekly passenger demand is a key fundamental process used by existing urban rail transit (URT) station authorities to diagnose operational problems and make decisions about train schedule patterns to improve operational efficiency, increase revenue management, and improve driving safety. The accuracy of the forecast results will directly affect the operation planning of urban rail transit (URT). Therefore, based on the collected inbound historical passenger data, this study used the Box–Jenkins time series with the Facebook Prophet algorithm to analyze the characteristics of urban rail transit passenger demand and achieved better computational forecasting performance accuracy. After analyzing the periodicity, correlation, and stationarity, different time series models were constructed. The Akaike information criteria (AIC), Bayesian information criteria (BIC), mean squared error (MSE), and root mean squared error (RMSE) were used to evaluate the adequacy of the best forecast model from among several tested candidates’ models for the Box–Jenkins. The parameters of the daily and weekly models were estimated using statistical software. The experimental results of this study are of both theoretical and practical significance to the urban rail transit (URT) station authorities for an effective station planning system. The forecasting results signify that the SARIMA (5, 1, 3) (1, 0, 0) 24 model performs better and is more stable in forecasting the daily passenger demand, and the ARMA (2, 1) model performs better in forecasting the weekly passenger demand. When comparing the SARIMA and ARMA models with the Facebook Prophet, results show that the Facebook Prophet model is superior to the SARIMA model for the daily time series, and the ARMA model is superior to the Facebook Prophet model for the weekly time series.

Suggested Citation

  • Dung David Chuwang & Weiya Chen, 2022. "Forecasting Daily and Weekly Passenger Demand for Urban Rail Transit Stations Based on a Time Series Model Approach," Forecasting, MDPI, vol. 4(4), pages 1-21, November.
    • Handle: RePEc:gam:jforec:v:4:y:2022:i:4:p:49-924:d:974723
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-9394/4/4/49/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-9394/4/4/49/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Simon Oh & Young-Ji Byon & Kitae Jang & Hwasoo Yeo, 2015. "Short-term Travel-time Prediction on Highway: A Review of the Data-driven Approach," Transport Reviews, Taylor & Francis Journals, vol. 35(1), pages 4-32, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Han Peng & Yike Chen & Linjian Shangguan & Shengnan Zhou & Yanchi Li & Qianyu Wang, 2025. "Evaluation of Urban Rail Transit System Planning Based on Integrated Empowerment Method and Matter-Element Model," Sustainability, MDPI, vol. 17(10), pages 1-22, May.
    2. João Paulo Teixeira & Ulrich Gunter, 2023. "Editorial for Special Issue: “Tourism Forecasting: Time-Series Analysis of World and Regional Data”," Forecasting, MDPI, vol. 5(1), pages 1-3, February.
    3. Aleksandr N. Grekov & Elena V. Vyshkvarkova & Aleksandr S. Mavrin, 2024. "Forecasting and Anomaly Detection in BEWS: Comparative Study of Theta, Croston, and Prophet Algorithms," Forecasting, MDPI, vol. 6(2), pages 1-14, May.
    4. Khumla Pornsiri & Sarawan Kamthorn, 2026. "Integrating Genetic Algorithms with LSTM for Improved Public Transportation Passenger Forecasting in Thailand," LOGI – Scientific Journal on Transport and Logistics, Sciendo, vol. 17(1), pages 13-24.

    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. Dujuan Wang & Jiacheng Zhu & Yunqiang Yin & Joshua Ignatius & Xiaowen Wei & Ajay Kumar, 2024. "Dynamic travel time prediction with spatiotemporal features: using a GNN-based deep learning method," Annals of Operations Research, Springer, vol. 340(1), pages 571-591, September.
    2. Milne, Dave & Watling, David, 2019. "Big data and understanding change in the context of planning transport systems," Journal of Transport Geography, Elsevier, vol. 76(C), pages 235-244.
    3. Purva Grover & Arpan Kumar Kar & Yogesh K. Dwivedi, 2022. "Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions," Annals of Operations Research, Springer, vol. 308(1), pages 177-213, January.
    4. Hosseini, Seyede-Saeede & Adulyasak, Yossiri & Rousseau, Louis-Martin, 2026. "Consistent home health care routing and scheduling problem under time uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 205(C).
    5. Victor Jian Ming Low & Hooi Ling Khoo & Wooi Chen Khoo, 2026. "A Logistic Burr mixture autoregressive model for impact quantification of contributing factors and prediction on the bimodal behavior in bus section travel time," Public Transport, Springer, vol. 18(1), pages 163-210, March.
    6. Panda, Manoj & Ngoduy, Dong & Vu, Hai L., 2019. "Multiple model stochastic filtering for traffic density estimation on urban arterials," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 280-306.
    7. Bo Qiu & Wei (David) Fan, 2021. "Machine Learning Based Short-Term Travel Time Prediction: Numerical Results and Comparative Analyses," Sustainability, MDPI, vol. 13(13), pages 1-19, July.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:jforec:v:4:y:2022:i:4:p:49-924:d:974723. 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.