IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v14y2025i5p1102-d1658776.html
   My bibliography  Save this article

Analyzing the Impact of Land-Use Characteristics and Demographic Factors on Spatial Variations in Public Bus Usage: A Comparison of Pre- and During COVID-19 Periods

Author

Listed:
  • Sukchan Hong

    (Department of Geography Education, Dongguk University, 30, Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea)

  • Byungyun Yang

    (Department of Geography Education, Dongguk University, 30, Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea)

Abstract

The spread of the coronavirus pandemic led to significant changes in bus-usage patterns in urban areas worldwide. Researchers have frequently employed linear and nonlinear models in bus-usage studies. However, existing linear models assume that each variable affects a uniform range, limiting their ability to capture localized pattern changes. This study applies a multiscale geographically weighted regression model reflecting the characteristics of the variables to address these limitations. Linear models are constrained by their inability to account adequately for the complex dynamics of real-world bus usage. This research introduces nonlinear methods to overcome these constraints. The geographical random forest method, an advanced variant of the random forest model, integrates spatial concepts to explain local patterns more effectively than traditional machine learning techniques. The linear models revealed significant changes in four variables (i.e., population size, over-65 population ratio, number of students, and land-use complexity). In contrast, nonlinear models demonstrated diverse movement patterns influenced by several factors, indicating a shift toward new public transportation patterns.

Suggested Citation

  • Sukchan Hong & Byungyun Yang, 2025. "Analyzing the Impact of Land-Use Characteristics and Demographic Factors on Spatial Variations in Public Bus Usage: A Comparison of Pre- and During COVID-19 Periods," Land, MDPI, vol. 14(5), pages 1-21, May.
  • Handle: RePEc:gam:jlands:v:14:y:2025:i:5:p:1102-:d:1658776
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/14/5/1102/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2073-445X/14/5/1102/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Diego Maria Barbieri & Baowen Lou & Marco Passavanti & Cang Hui & Inge Hoff & Daniela Antunes Lessa & Gaurav Sikka & Kevin Chang & Akshay Gupta & Kevin Fang & Arunabha Banerjee & Brij Maharaj & Louisa, 2021. "Impact of COVID-19 pandemic on mobility in ten countries and associated perceived risk for all transport modes," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-18, February.
    2. Peng, Qiao & Bakkar, Yassine & Wu, Liangpeng & Liu, Weilong & Kou, Ruibing & Liu, Kailong, 2024. "Transportation resilience under Covid-19 Uncertainty: A traffic severity analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
    3. Galasso, Joseph & Cao, Duy M. & Hochberg, Robert, 2022. "A random forest model for forecasting regional COVID-19 cases utilizing reproduction number estimates and demographic data," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    4. Alfonso Orro & Margarita Novales & Ángel Monteagudo & José-Benito Pérez-López & Miguel R. Bugarín, 2020. "Impact on City Bus Transit Services of the COVID–19 Lockdown and Return to the New Normal: The Case of A Coruña (Spain)," Sustainability, MDPI, vol. 12(17), pages 1-30, September.
    5. Jun, Myung-Jin & Choi, Keechoo & Jeong, Ji-Eun & Kwon, Ki-Hyun & Kim, Hee-Jae, 2015. "Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul," Journal of Transport Geography, Elsevier, vol. 48(C), pages 30-40.
    6. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    7. Kim, Suji & Lee, Sujin & Ko, Eunjeong & Jang, Kitae & Yeo, Jiho, 2021. "Changes in car and bus usage amid the COVID-19 pandemic: Relationship with land use and land price," Journal of Transport Geography, Elsevier, vol. 96(C).
    8. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
    9. Vergel-Tovar, C. Erik & Rodriguez, Daniel A., 2018. "The ridership performance of the built environment for BRT systems: Evidence from Latin America," Journal of Transport Geography, Elsevier, vol. 73(C), pages 172-184.
    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. Jonas Botz & Diego Valderrama & Jannis Guski & Holger Fröhlich, 2024. "A dynamic ensemble model for short-term forecasting in pandemic situations," PLOS Global Public Health, Public Library of Science, vol. 4(8), pages 1-18, August.
    2. Hou, Lei & Elsworth, Derek & Zhang, Fengshou & Wang, Zhiyuan & Zhang, Jianbo, 2023. "Evaluation of proppant injection based on a data-driven approach integrating numerical and ensemble learning models," Energy, Elsevier, vol. 264(C).
    3. Ma, Zhikai & Huo, Qian & Wang, Wei & Zhang, Tao, 2023. "Voltage-temperature aware thermal runaway alarming framework for electric vehicles via deep learning with attention mechanism in time-frequency domain," Energy, Elsevier, vol. 278(C).
    4. Patrick Krennmair & Timo Schmid, 2022. "Flexible domain prediction using mixed effects random forests," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1865-1894, November.
    5. Jie Shi & Arno P. J. M. Siebes & Siamak Mehrkanoon, 2023. "TransCORALNet: A Two-Stream Transformer CORAL Networks for Supply Chain Credit Assessment Cold Start," Papers 2311.18749, arXiv.org.
    6. Bourdouxhe, Axel & Wibail, Lionel & Claessens, Hugues & Dufrêne, Marc, 2023. "Modeling potential natural vegetation: A new light on an old concept to guide nature conservation in fragmented and degraded landscapes," Ecological Modelling, Elsevier, vol. 481(C).
    7. Manuel J. García Rodríguez & Vicente Rodríguez Montequín & Francisco Ortega Fernández & Joaquín M. Villanueva Balsera, 2019. "Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning," Complexity, Hindawi, vol. 2019, pages 1-20, November.
    8. Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023. "Targeting predictors in random forest regression," International Journal of Forecasting, Elsevier, vol. 39(2), pages 841-868.
    9. Yiyi Huo & Yingying Fan & Fang Han, 2023. "On the adaptation of causal forests to manifold data," Papers 2311.16486, arXiv.org, revised Dec 2023.
    10. Akshita Bassi & Aditya Manchanda & Rajwinder Singh & Mahesh Patel, 2023. "A comparative study of machine learning algorithms for the prediction of compressive strength of rice husk ash-based concrete," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(1), pages 209-238, August.
    11. Sachin Kumar & Zairu Nisha & Jagvinder Singh & Anuj Kumar Sharma, 2022. "Sensor network driven novel hybrid model based on feature selection and SVR to predict indoor temperature for energy consumption optimisation in smart buildings," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(6), pages 3048-3061, December.
    12. Yong-Chao Su & Cheng-Yu Wu & Cheng-Hong Yang & Bo-Sheng Li & Sin-Hua Moi & Yu-Da Lin, 2021. "Machine Learning Data Imputation and Prediction of Foraging Group Size in a Kleptoparasitic Spider," Mathematics, MDPI, vol. 9(4), pages 1-16, February.
    13. Diogenis A. Kiziridis & Anna Mastrogianni & Magdalini Pleniou & Elpida Karadimou & Spyros Tsiftsis & Fotios Xystrakis & Ioannis Tsiripidis, 2022. "Acceleration and Relocation of Abandonment in a Mediterranean Mountainous Landscape: Drivers, Consequences, and Management Implications," Land, MDPI, vol. 11(3), pages 1-23, March.
    14. Escribano, Álvaro & Wang, Dandan, 2021. "Mixed random forest, cointegration, and forecasting gasoline prices," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1442-1462.
    15. Hunish Bansal & Basavraj Chinagundi & Prashant Singh Rana & Neeraj Kumar, 2022. "An Ensemble Machine Learning Technique for Detection of Abnormalities in Knee Movement Sustainability," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    16. Yigit Aydede & Jan Ditzen, 2022. "Identifying the regional drivers of influenza-like illness in Nova Scotia with dominance analysis," Papers 2212.06684, arXiv.org.
    17. Siyoon Kwon & Hyoseob Noh & Il Won Seo & Sung Hyun Jung & Donghae Baek, 2021. "Identification Framework of Contaminant Spill in Rivers Using Machine Learning with Breakthrough Curve Analysis," IJERPH, MDPI, vol. 18(3), pages 1-26, January.
    18. Sylwester Bejger, 2024. "Machine Learning in Cartel Screening—The Case of Parallel Pricing in a Fuel Wholesale Market," Energies, MDPI, vol. 17(16), pages 1-17, August.
    19. Karim Zkik & Anass Sebbar & Oumaima Fadi & Sachin Kamble & Amine Belhadi, 2024. "Securing blockchain-based crowdfunding platforms: an integrated graph neural networks and machine learning approach," Electronic Commerce Research, Springer, vol. 24(1), pages 497-533, March.
    20. Lotfi Boudabsa & Damir Filipovi'c, 2022. "Ensemble learning for portfolio valuation and risk management," Papers 2204.05926, arXiv.org.

    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:jlands:v:14:y:2025:i:5:p:1102-:d:1658776. 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.