IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i8p1221-d1630284.html
   My bibliography  Save this article

Forecasting the Number of Passengers on Hungarian Railway Routes Using a Similarity and Fuzzy Arithmetic-Based Inference Method

Author

Listed:
  • Marcell Fetter

    (Faculty of Economics, Eötvös Loránd University, 1053 Budapest, Hungary
    These authors contributed equally to this work.)

  • Tamás Jónás

    (Faculty of Economics, Eötvös Loránd University, 1053 Budapest, Hungary
    These authors contributed equally to this work.)

Abstract

In this study, we present a similarity and fuzzy arithmetic-based fuzzy inference method and show how effectively it can be used to forecast the number of passengers on a railway route. We introduce a novel fuzzy similarity measure that is derived from the so-called epsilon function, which may be viewed as an alternative to the exponential function. After demonstrating the most important properties of the new similarity measure, we construct a fuzzy inference method that is founded on arithmetic operations over triangular fuzzy numbers. This inference method utilizes the proposed similarity measure to derive weight values for the above-mentioned arithmetic operations. The motivation behind the proposed method is twofold. On the one hand, we aim to construct a method that is simple and easy to implement. On the other hand, we intend to ensure that this method meets the practical requirements for rail passenger forecasts. Using a real-life case study, we demonstrate how well our method can predict the expected number of passengers on a new railway route based on characteristics of this relation. With respect to the studied case, we may conclude that although the similarity and fuzzy arithmetic-based fuzzy inference system has only two adjustable parameters, it may be regarded as a viable alternative to Sugeno-type fuzzy inference systems with a much greater number of adjustable parameters tuned by various optimization techniques.

Suggested Citation

  • Marcell Fetter & Tamás Jónás, 2025. "Forecasting the Number of Passengers on Hungarian Railway Routes Using a Similarity and Fuzzy Arithmetic-Based Inference Method," Mathematics, MDPI, vol. 13(8), pages 1-28, April.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:8:p:1221-:d:1630284
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/8/1221/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/13/8/1221/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kuby, Michael & Barranda, Anthony & Upchurch, Christopher, 2004. "Factors influencing light-rail station boardings in the United States," Transportation Research Part A: Policy and Practice, Elsevier, vol. 38(3), pages 223-247, March.
    2. Ghoseiri, Keivan & Szidarovszky, Ferenc & Asgharpour, Mohammad Jawad, 2004. "A multi-objective train scheduling model and solution," Transportation Research Part B: Methodological, Elsevier, vol. 38(10), pages 927-952, December.
    3. Theodore Tsekeris & Charalambos Tsekeris, 2011. "Demand Forecasting in Transport: Overview and Modeling Advances," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 24(1), pages 82-94, January.
    4. Chiou, Yu-Chiun & Jou, Rong-Chang & Yang, Cheng-Han, 2015. "Factors affecting public transportation usage rate: Geographically weighted regression," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 161-177.
    5. Banerjee, Nilabhra & Morton, Alec & Akartunalı, Kerem, 2020. "Passenger demand forecasting in scheduled transportation," European Journal of Operational Research, Elsevier, vol. 286(3), pages 797-810.
    6. Iseki, Hiroyuki & Liu, Chao & Knaap, Gerrit, 2018. "The determinants of travel demand between rail stations: A direct transit demand model using multilevel analysis for the Washington D.C. Metrorail system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 635-649.
    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. Du, Qiang & Zhou, Yuqing & Huang, Youdan & Wang, Yalei & Bai, Libiao, 2022. "Spatiotemporal exploration of the non-linear impacts of accessibility on metro ridership," Journal of Transport Geography, Elsevier, vol. 102(C).
    2. Li, Shaoying & Lyu, Dijiang & Huang, Guanping & Zhang, Xiaohu & Gao, Feng & Chen, Yuting & Liu, Xiaoping, 2020. "Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou, China," Journal of Transport Geography, Elsevier, vol. 82(C).
    3. Cui, Mengying & Yu, Lijie & Nie, Shaoyu & Dai, Zhe & Ge, Ying-en & Levinson, David, 2025. "How do access and spatial dependency shape metro passenger flows," Journal of Transport Geography, Elsevier, vol. 123(C).
    4. Ingvardson, Jesper Bláfoss & Nielsen, Otto Anker, 2018. "How urban density, network topology and socio-economy influence public transport ridership: Empirical evidence from 48 European metropolitan areas," Journal of Transport Geography, Elsevier, vol. 72(C), pages 50-63.
    5. Tu, Wei & Cao, Rui & Yue, Yang & Zhou, Baoding & Li, Qiuping & Li, Qingquan, 2018. "Spatial variations in urban public ridership derived from GPS trajectories and smart card data," Journal of Transport Geography, Elsevier, vol. 69(C), pages 45-57.
    6. Aston, Laura & Currie, Graham & Kamruzzaman, Md. & Delbosc, Alexa & Teller, David, 2020. "Study design impacts on built environment and transit use research," Journal of Transport Geography, Elsevier, vol. 82(C).
    7. Xuesong Feng & Zhibin Tao & Xuejun Niu & Zejing Ruan, 2021. "Multi-Objective Land Use Allocation Optimization in View of Overlapped Influences of Rail Transit Stations," Sustainability, MDPI, vol. 13(23), pages 1-14, November.
    8. Ding, Chuan & Cao, Xinyu & Liu, Chao, 2019. "How does the station-area built environment influence Metrorail ridership? Using gradient boosting decision trees to identify non-linear thresholds," Journal of Transport Geography, Elsevier, vol. 77(C), pages 70-78.
    9. Jesper Bláfoss Ingvardson & Otto Anker Nielsen, 2022. "The influence of vicinity to stations, station characteristics and perceived safety on public transport mode choice: a case study from Copenhagen," Public Transport, Springer, vol. 14(2), pages 459-480, June.
    10. 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.
    11. Yang Liu & Yanjie Ji & Zhuangbin Shi & Liangpeng Gao, 2018. "The Influence of the Built Environment on School Children’s Metro Ridership: An Exploration Using Geographically Weighted Poisson Regression Models," Sustainability, MDPI, vol. 10(12), pages 1-16, December.
    12. Shao, Qifan & Zhang, Wenjia & Cao, Xinyu & Yang, Jiawen & Yin, Jie, 2020. "Threshold and moderating effects of land use on metro ridership in Shenzhen: Implications for TOD planning," Journal of Transport Geography, Elsevier, vol. 89(C).
    13. Wu, Zhen & Woo, Su-Han & Lai, Po-Lin & Chen, Xiaoyi, 2022. "The economic impact of inland ports on regional development: Evidence from the Yangtze River region," Transport Policy, Elsevier, vol. 127(C), pages 80-91.
    14. Guerra, Erick & Cervero, Robert & Tischler, Daniel, 2011. "The Half-Mile Circle: Does It Represent Transit Station Catchments?," University of California Transportation Center, Working Papers qt0d84c2f4, University of California Transportation Center.
    15. Kirtonia, Sajeeb & Sun, Yanshuo, 2022. "Evaluating rail transit's comparative advantages in travel cost and time over taxi with open data in two U.S. cities," Transport Policy, Elsevier, vol. 115(C), pages 75-87.
    16. Banerjee, Nilabhra & Morton, Alec & Akartunalı, Kerem, 2020. "Passenger demand forecasting in scheduled transportation," European Journal of Operational Research, Elsevier, vol. 286(3), pages 797-810.
    17. Ahmed Daqrouq & Ardeshir Anjomani, 2019. "Public Transit Ridership and Car-Oriented Cities: The Case of the Dallas Region," Economies, MDPI, vol. 7(3), pages 1-17, August.
    18. Hugo M. Repolho & António P. Antunes & Richard L. Church, 2013. "Optimal Location of Railway Stations: The Lisbon-Porto High-Speed Rail Line," Transportation Science, INFORMS, vol. 47(3), pages 330-343, August.
    19. Huang, Yeran & Yang, Lixing & Tang, Tao & Gao, Ziyou & Cao, Fang, 2017. "Joint train scheduling optimization with service quality and energy efficiency in urban rail transit networks," Energy, Elsevier, vol. 138(C), pages 1124-1147.
    20. Samanta, Sutapa & Jha, Manoj K., 2011. "Modeling a rail transit alignment considering different objectives," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(1), pages 31-45, January.

    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:jmathe:v:13:y:2025:i:8:p:1221-:d:1630284. 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.