IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i24p10901-d1542340.html
   My bibliography  Save this article

Exploring the Footprint of COVID-19 on the Evolution of Public Bus Transport Demand Using GIS

Author

Listed:
  • Rafael González-Escobar

    (Department of Construction, School of Technology, Research Institute for Sustainable Territorial Development (INTERRA), Universidad de Extremadura, Avda de la Universidad s/n, 10003 Cáceres, Spain)

  • Juan Miguel Vega Naranjo

    (Department of Construction, School of Technology, Research Institute for Sustainable Territorial Development (INTERRA), Universidad de Extremadura, Avda de la Universidad s/n, 10003 Cáceres, Spain)

  • Montaña Jiménez-Espada

    (Department of Construction, School of Technology, Research Institute for Sustainable Territorial Development (INTERRA), Universidad de Extremadura, Avda de la Universidad s/n, 10003 Cáceres, Spain)

  • Jonathan Galeano Vivas

    (Department of Art and Territorial Sciences, School of Philosophy and Arts, Universidad de Extremadura, Avda de la Universidad s/n, 10003 Cáceres, Spain)

Abstract

The scope of the research work described in this article involved identifying the effects of the COVID-19 pandemic on the urban public transport system in a medium-sized city and its adjacent metropolitan area, using as reference information the number of tickets effectively sold in order to determine the fluctuation in the volume of passengers on the different bus lines before, during and after the pandemic. At the methodological level, a combined approach was employed, involving, on the one hand, the collection of open access public data from institutional repositories and information provided by the government and, on the other hand, network analysis and graphical mapping using GIS tools. The results obtained at the micro level (individualised study of each urban bus line) reveal a significant decrease in the number of passengers during the pandemic, showing the effect of mobility restrictions and the fear of contagion. However, a gradual recovery in post-pandemic demand has been observed, highlighting a large variability in recovery patterns between different bus lines. Such a situation could be attributable to several factors, such as the socio-demographic characteristics of the areas served, the frequency of the service, connectivity with other modes of transport and users’ perception of the quality of the service. At the macro level (comparison between urban and interurban transport), lines with higher demand prior to the pandemic have shown greater resilience and faster recovery. However, urban transport has experienced a more uniform and accelerated recuperation than interurban transport, with significant percentage differences in the years analysed. This disparity could be explained by the greater dependence of inhabitants on urban transport for their daily trips, due to its greater frequency and geographical coverage. Interurban transport, on the other hand, shows a more fluctuating demand and a lower dependence of users. Finally, the lack of previous research focused on the impact of the pandemic in sparsely populated rural areas restricts the ability to establish a solid frame of reference and generalise the results of this study. The authors consider that more detailed future research, including a comparative analysis of different alternative transport modes in inter-urban settings and considering a broader set of socio-demographic variables of passengers, is needed to better understand mobility dynamics in these areas and their evolution in the context of the pandemic.

Suggested Citation

  • Rafael González-Escobar & Juan Miguel Vega Naranjo & Montaña Jiménez-Espada & Jonathan Galeano Vivas, 2024. "Exploring the Footprint of COVID-19 on the Evolution of Public Bus Transport Demand Using GIS," Sustainability, MDPI, vol. 16(24), pages 1-22, December.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:24:p:10901-:d:1542340
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/24/10901/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/24/10901/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Luis A. Camarero & Jesús Oliva, 2008. "Exploring the Social Face of Urban Mobility: Daily Mobility as Part of the Social Structure in Spain," International Journal of Urban and Regional Research, Wiley Blackwell, vol. 32(2), pages 344-362, June.
    2. Das, Sanhita & Boruah, Alice & Banerjee, Arunabha & Raoniar, Rahul & Nama, Suresh & Maurya, Akhilesh Kumar, 2021. "Impact of COVID-19: A radical modal shift from public to private transport mode," Transport Policy, Elsevier, vol. 109(C), pages 1-11.
    3. Deepti Muley & Md. Shahin & Charitha Dias & Muhammad Abdullah, 2020. "Role of Transport during Outbreak of Infectious Diseases: Evidence from the Past," Sustainability, MDPI, vol. 12(18), pages 1-22, September.
    4. Neil M. Ferguson & Derek A.T. Cummings & Simon Cauchemez & Christophe Fraser & Steven Riley & Aronrag Meeyai & Sopon Iamsirithaworn & Donald S. Burke, 2005. "Strategies for containing an emerging influenza pandemic in Southeast Asia," Nature, Nature, vol. 437(7056), pages 209-214, September.
    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. Juan Miguel Vega Naranjo & Rafael González-Escobar & Montaña Jiménez-Espada & Jonathan Galeano Vivas, 2024. "Resilience of Interurban Public Transport and Impact of COVID-19 on Rural Connectivity in Sparsely Populated Regions," Land, MDPI, vol. 13(11), pages 1-26, October.
    2. Karimi, Sina & Samadzad, Mahdi & Lesteven, Gaele, 2024. "Navigating public transport during a pandemic: Key lessons on travel behavior and social equity from two surveys in Tehran," Transportation Research Part A: Policy and Practice, Elsevier, vol. 184(C).
    3. S. M. Mniszewski & S. Y. Del Valle & P. D. Stroud & J. M. Riese & S. J. Sydoriak, 2008. "Pandemic simulation of antivirals + school closures: buying time until strain-specific vaccine is available," Computational and Mathematical Organization Theory, Springer, vol. 14(3), pages 209-221, September.
    4. Jeremy Hadidjojo & Siew Ann Cheong, 2011. "Equal Graph Partitioning on Estimated Infection Network as an Effective Epidemic Mitigation Measure," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-10, July.
    5. Tamer Edirne & Dilek Avci & Burçak Dagkara & Muslum Aslan, 2011. "Knowledge and anticipated attitudes of the community about bird flu outbreak in Turkey, 2007–2008: a survey-based descriptive study," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 56(2), pages 163-168, April.
    6. Wei Zhong, 2017. "Simulating influenza pandemic dynamics with public risk communication and individual responsive behavior," Computational and Mathematical Organization Theory, Springer, vol. 23(4), pages 475-495, December.
    7. Houštecká, Anna & Koh, Dongya & Santaeulàlia-Llopis, Raül, 2021. "Contagion at work: Occupations, industries and human contact," Journal of Public Economics, Elsevier, vol. 200(C).
    8. John M Drake & Tobias S Brett & Shiyang Chen & Bogdan I Epureanu & Matthew J Ferrari & Éric Marty & Paige B Miller & Eamon B O’Dea & Suzanne M O’Regan & Andrew W Park & Pejman Rohani, 2019. "The statistics of epidemic transitions," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-14, May.
    9. Zhou, Yuyang & Wang, Peiyu & Zheng, Shuyan & Zhao, Minhe & Lam, William H.K. & Chen, Anthony & Sze, N.N. & Chen, Yanyan, 2024. "Modeling dynamic travel mode choices using cumulative prospect theory," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
    10. Moshe B Hoshen & Anthony H Burton & Themis J V Bowcock, 2007. "Simulating disease transmission dynamics at a multi-scale level," International Journal of Microsimulation, International Microsimulation Association, vol. 1(1), pages 26-34.
    11. Molloy, Joseph & Schatzmann, Thomas & Schoeman, Beaumont & Tchervenkov, Christopher & Hintermann, Beat & Axhausen, Kay W., 2021. "Observed impacts of the Covid-19 first wave on travel behaviour in Switzerland based on a large GPS panel," Transport Policy, Elsevier, vol. 104(C), pages 43-51.
    12. Linus Nyiwul, 2021. "Epidemic Control and Resource Allocation: Approaches and Implications for the Management of COVID-19," Studies in Microeconomics, , vol. 9(2), pages 283-305, December.
    13. Zhongqiang Bai & Juanle Wang & Mingming Wang & Mengxu Gao & Jiulin Sun, 2018. "Accuracy Assessment of Multi-Source Gridded Population Distribution Datasets in China," Sustainability, MDPI, vol. 10(5), pages 1-15, April.
    14. Jaydarifard, Saeed & Morawska, Lidia & Paz, Alexander, 2024. "Mitigating airborne infection risks in public transportation: A systematic review," Transport Policy, Elsevier, vol. 155(C), pages 309-320.
    15. James Truscott & Neil M Ferguson, 2012. "Evaluating the Adequacy of Gravity Models as a Description of Human Mobility for Epidemic Modelling," PLOS Computational Biology, Public Library of Science, vol. 8(10), pages 1-12, October.
    16. Andrew J Black & Joshua V Ross, 2013. "Estimating a Markovian Epidemic Model Using Household Serial Interval Data from the Early Phase of an Epidemic," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-8, August.
    17. Eva K. Lee & Chien-Hung Chen & Ferdinand Pietz & Bernard Benecke, 2009. "Modeling and Optimizing the Public-Health Infrastructure for Emergency Response," Interfaces, INFORMS, vol. 39(5), pages 476-490, October.
    18. Li, Qian & Xiao, Yanni, 2023. "Analysis of a hybrid SIR model combining the fixed-moments pulse interventions with susceptibles-triggered threshold policy," Applied Mathematics and Computation, Elsevier, vol. 453(C).
    19. Humpe, Andreas & Gössling, Stefan & Haustein, Sonja, 2022. "Car careers: A socio-psychological evaluation of aspirational automobile ownership," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 156-166.
    20. Nguyen, Le Khanh Ngan & Howick, Susan & Megiddo, Itamar, 2024. "A framework for conceptualising hybrid system dynamics and agent-based simulation models," European Journal of Operational Research, Elsevier, vol. 315(3), pages 1153-1166.

    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:24:p:10901-:d:1542340. 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.