IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i3p1225-d1330932.html
   My bibliography  Save this article

Predicting Station-Level Peak Hour Ridership of Metro Considering the Peak Deviation Coefficient

Author

Listed:
  • Ying Zhao

    (School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China
    Key Laboratory of Railway Industry on Plateau Railway Transportation Intelligent Management and Control, Lanzhou 730070, China)

  • Jie Wei

    (School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Haijun Li

    (School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China
    Key Laboratory of Railway Industry on Plateau Railway Transportation Intelligent Management and Control, Lanzhou 730070, China)

  • Yan Huang

    (School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China
    Key Laboratory of Railway Industry on Plateau Railway Transportation Intelligent Management and Control, Lanzhou 730070, China)

Abstract

Subway station-level peak hour ridership (SPR) is a crucial input parameter for multiple applications, including the planning, design, construction, and operation of stations. However, traditional SPR estimation techniques may produce biased results. A unified peak hour factor (PHF) extracted from the line level is generally set for all attributed stations, which ignores the possible peak deviation that arises between the station and line and the wide variation of PHFs in practice. This study presents a comprehensive and refined estimation framework for SPR that accommodates the peak deviation context by introducing the peak deviation coefficient (PDC). Moreover, the estimation of the PDC and PHF variability is improved by constructing spatial regression based relationship models. The empirical results show that the proposed approach exhibits wider applicability and a higher prediction precision across all types of peak periods considered as compared to conventional methods (i.e., MAPE decreases of 0.115–0.351). The findings demonstrate the importance of the consideration of the peak deviation scenario and the spatial dependency in SPR estimation to achieve better decision making. Moreover, the underlying influencing mechanism of the PHF and PDC at distinct peak periods is further revealed using the spatial model. This provides critical theoretical references and policy implications to prudently deploy land-use resources to balance the travel demand between peak and off-peak periods and thus enhance the line operation efficiency.

Suggested Citation

  • Ying Zhao & Jie Wei & Haijun Li & Yan Huang, 2024. "Predicting Station-Level Peak Hour Ridership of Metro Considering the Peak Deviation Coefficient," Sustainability, MDPI, vol. 16(3), pages 1-16, February.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:3:p:1225-:d:1330932
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/3/1225/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/3/1225/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jiao, Jingjuan & Wang, Jiaoe & Zhang, Fangni & Jin, Fengjun & Liu, Wei, 2020. "Roles of accessibility, connectivity and spatial interdependence in realizing the economic impact of high-speed rail: Evidence from China," Transport Policy, Elsevier, vol. 91(C), pages 1-15.
    2. Chen Zhong & Michael Batty & Ed Manley & Jiaqiu Wang & Zijia Wang & Feng Chen & Gerhard Schmitt, 2016. "Variability in Regularity: Mining Temporal Mobility Patterns in London, Singapore and Beijing Using Smart-Card Data," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-17, February.
    3. Lijie Yu & Quan Chen & Kuanmin Chen, 2019. "Deviation of Peak Hours for Urban Rail Transit Stations: A Case Study in Xi’an, China," Sustainability, MDPI, vol. 11(10), pages 1-21, May.
    4. Yu, Lijie & Cui, Mengying, 2023. "How subway network affects transit accessibility and equity: A case study of Xi'an metropolitan area," Journal of Transport Geography, Elsevier, vol. 108(C).
    5. Wang, Jiangbo & Yamamoto, Toshiyuki & Liu, Kai, 2021. "Spatial dependence and spillover effects in customized bus demand: Empirical evidence using spatial dynamic panel models," Transport Policy, Elsevier, vol. 105(C), pages 166-180.
    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. Peikun Li & Chaoqun Ma & Jing Ning & Yun Wang & Caihua Zhu, 2019. "Analysis of Prediction Accuracy under the Selection of Optimum Time Granularity in Different Metro Stations," Sustainability, MDPI, vol. 11(19), pages 1-19, September.
    2. Jiaoe Wang & Yanan Li & Jingjuan Jiao & Haitao Jin & Fangye Du, 2023. "Bus ridership and its determinants in Beijing: A spatial econometric perspective," Transportation, Springer, vol. 50(2), pages 383-406, April.
    3. Minhua Yang & Rui Yao & Linkun Ma & Ang Yang, 2024. "Towards a Low-Carbon Target: How the High-Speed Rail and Its Expansion Affects Industrial Concentration and Macroeconomic Conditions: Evidence from Chinese Urban Agglomerations," Sustainability, MDPI, vol. 16(19), pages 1-18, September.
    4. Yang, Zhiwei & Li, Can & Jiao, Jingjuan & Liu, Wei & Zhang, Fangni, 2020. "On the joint impact of high-speed rail and megalopolis policy on regional economic growth in China," Transport Policy, Elsevier, vol. 99(C), pages 20-30.
    5. Yulin Zhao & Linkun Li & Zhishuo Zhang & Daniel (Jian) Sun, 2024. "Performance Evaluation for the Expansion of Multi-Level Rail Transit Network in Xi’an Metropolitan Area: Empirical Analysis on Accessibility and Resilience," Land, MDPI, vol. 13(10), pages 1-26, October.
    6. Li, Siping & Zhou, Yaoming & Kundu, Tanmoy & Zhang, Fangni, 2021. "Impact of entry restriction policies on international air transport connectivity during COVID-19 pandemic," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    7. Wu, Bingyu & Levinson, David M., 2024. "A multi-modal analysis of the effect of transport on population and productivity in China," Journal of Transport Geography, Elsevier, vol. 116(C).
    8. Huang, Yan & Ma, Liang & Cao, Jason, 2023. "Exploring spatial heterogeneity in the high-speed rail impact on air quality," Journal of Transport Geography, Elsevier, vol. 106(C).
    9. Hiramatsu, Tomoru, 2023. "Inter-metropolitan regional migration galvanized by high-speed rail: A simulation analysis of the Linear Chuo Shinkansen line in Japan," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).
    10. Yi Cao & Xue Li, 2022. "Multi-Model Attention Fusion Multilayer Perceptron Prediction Method for Subway OD Passenger Flow under COVID-19," Sustainability, MDPI, vol. 14(21), pages 1-16, November.
    11. Chuanchuan Yuan & Li Gan & Huili Zhuo, 2022. "Coupling Mechanisms and Development Patterns of Revitalizing Intangible Cultural Heritage by Integrating Cultural Tourism: The Case of Hunan Province, China," Sustainability, MDPI, vol. 14(12), pages 1-16, June.
    12. Songhong Li & Hongwei Wang & Xiaoyang Liu & Zhen Yang, 2024. "The Evolution and Economic and Social Effects of the Spatial and Temporal Pattern of Transport Superiority Degree in Southern Xinjiang, China," Land, MDPI, vol. 13(2), pages 1-20, February.
    13. Daniela- Luminița CONSTANTIN & Corina- Cristiana NASTACĂ & Emilia GEAMBASU, 2021. "Population Accessibility To Rail Services. Insights Through The Lens Of Territorial Cohesion," Regional Science Inquiry, Hellenic Association of Regional Scientists, vol. 0(1), pages 81-98, June.
    14. Wenfang Fu & Chuanjian Luo & Modan Yan, 2023. "Does Urban Agglomeration Promote the Development of Cities? Evidence from the Urban Network Externalities," Sustainability, MDPI, vol. 15(12), pages 1-20, June.
    15. Wen, Jian & Nassir, Neema & Zhao, Jinhua, 2019. "Value of demand information in autonomous mobility-on-demand systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 121(C), pages 346-359.
    16. Åse Jevinger & Jan A. Persson, 2019. "Exploring the potential of using real-time traveler data in public transport disturbance management," Public Transport, Springer, vol. 11(2), pages 413-441, August.
    17. Zhou, Yang & Thill, Jean-Claude & Xu, Yang & Fang, Zhixiang, 2021. "Variability in individual home-work activity patterns," Journal of Transport Geography, Elsevier, vol. 90(C).
    18. Hu, Xinlei & Huang, Jie & Shi, Feng, 2022. "A robustness assessment with passenger flow data of high-speed rail network in China," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    19. Li, Hui & Dong, Xiucheng & Jiang, Qingzhe & Dong, Kangyin, 2021. "Policy analysis for high-speed rail in China: Evolution, evaluation, and expectation," Transport Policy, Elsevier, vol. 106(C), pages 37-53.
    20. Lijie Yu & Yarong Cong & Kuanmin Chen, 2020. "Determination of the Peak Hour Ridership of Metro Stations in Xi’an, China Using Geographically-Weighted Regression," Sustainability, MDPI, vol. 12(6), pages 1-22, March.

    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:jsusta:v:16:y:2024:i:3:p:1225-:d:1330932. 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.