IDEAS home Printed from https://ideas.repec.org/a/eee/trapol/v136y2023icp98-112.html
   My bibliography  Save this article

Quantifying the impact of COVID-19 on travel behavior in different socio-economic segments

Author

Listed:
  • Xi, Haoning
  • Li, Qin
  • Hensher, David A.
  • Nelson, John D.
  • Ho, Chinh

Abstract

The COVID-19 pandemic has resulted in substantial negative impacts on social equity. To investigate transport inequities in communities with varying medical resources and COVID controlling measures during the COVID pandemic and to develop transport-related policies for the post-COVID-19 world, it is necessary to evaluate how the pandemic has affected travel behavior patterns in different socio-economic segments (SES). We first analyze the travel behavior change percentage due to COVID, e.g., increased working from home (WFH), decreased in-person shopping trips, decreased public transit trips, and canceled overnight trips of individuals with varying age, gender, education levels, and household income, based on the most recent US Household Pulse Survey census data during Aug 2020 ∼ Dec 2021. We then quantify the impact of COVID-19 on travel behavior of different socio-economic segments, using integrated mobile device location data in the USA over the period 1 Jan 2020–20 Apr 2021. Fixed-effect panel regression models are proposed to statistically estimate the impact of COVID monitoring measures and medical resources on travel behavior such as nonwork/work trips, travel miles, out-of-state trips, and the incidence of WFH for low SES and high SES. We find that as exposure to COVID increases, the number of trips, traveling miles, and overnight trips started to bounce back to pre-COVID levels, while the incidence of WFH remained relatively stable and did not tend to return to pre-COVID level. We find that the increase in new COVID cases has a significant impact on the number of work trips in the low SES but has little impact on the number of work trips in the high SES. We find that the fewer medical resources there are, the fewer mobility behavior changes that individuals in the low SES will undertake. The findings have implications for understanding the heterogeneous mobility response of individuals in different SES to various COVID waves and thus provide insights into the equitable transport governance and resiliency of the transport system in the “post-COVID” era.

Suggested Citation

  • Xi, Haoning & Li, Qin & Hensher, David A. & Nelson, John D. & Ho, Chinh, 2023. "Quantifying the impact of COVID-19 on travel behavior in different socio-economic segments," Transport Policy, Elsevier, vol. 136(C), pages 98-112.
  • Handle: RePEc:eee:trapol:v:136:y:2023:i:c:p:98-112
    DOI: 10.1016/j.tranpol.2023.03.014
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0967070X23000732
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.tranpol.2023.03.014?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Edward L. Glaeser & Caitlin S. Gorback & Stephen J. Redding, 2020. "How Much Does COVID-19 Increase with Mobility? Evidence from New York and Four Other U.S. Cities," Working Papers 2020-22, Princeton University. Economics Department..
    2. Sun, Fan & Jin, Minjie & Zhang, Tao & Huang, Wencheng, 2022. "Satisfaction differences in bus traveling among low-income individuals before and after COVID-19," Transportation Research Part A: Policy and Practice, Elsevier, vol. 160(C), pages 311-332.
    3. William Greene, 2004. "The behaviour of the maximum likelihood estimator of limited dependent variable models in the presence of fixed effects," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 98-119, June.
    4. Beck, Matthew J. & Hensher, David A., 2020. "Insights into the impact of COVID-19 on household travel and activities in Australia – The early days of easing restrictions," Transport Policy, Elsevier, vol. 99(C), pages 95-119.
    5. Hensher, David A. & Balbontin, Camila & Beck, Matthew J. & Wei, Edward, 2022. "The impact of working from home on modal commuting choice response during COVID-19: Implications for two metropolitan areas in Australia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 179-201.
    6. Bell, Andrew & Jones, Kelvyn, 2015. "Explaining Fixed Effects: Random Effects Modeling of Time-Series Cross-Sectional and Panel Data," Political Science Research and Methods, Cambridge University Press, vol. 3(1), pages 133-153, January.
    7. Minha Lee & Jun Zhao & Qianqian Sun & Yixuan Pan & Weiyi Zhou & Chenfeng Xiong & Lei Zhang, 2020. "Human mobility trends during the early stage of the COVID-19 pandemic in the United States," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-15, November.
    8. Luyu Liu & Harvey J Miller & Jonathan Scheff, 2020. "The impacts of COVID-19 pandemic on public transit demand in the United States," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-22, November.
    9. de Palma, André & Vosough, Shaghayegh & Liao, Feixiong, 2022. "An overview of effects of COVID-19 on mobility and lifestyle: 18 months since the outbreak," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 372-397.
    10. Sanmay Shelat & Oded Cats & Sander van Cranenburgh, 2021. "Traveller behaviour in public transport in the early stages of the COVID-19 pandemic in the Netherlands," Papers 2104.10973, arXiv.org, revised Apr 2022.
    11. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    12. Henning Holgersen & Zhiyang Jia & Simen Svenkerud, 2021. "Who and how many can work from home? Evidence from task descriptions," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 55(1), pages 1-13, December.
    13. Lee, Lung-fei & Yu, Jihai, 2010. "Estimation of spatial autoregressive panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 154(2), pages 165-185, February.
    14. Ton, Danique & Arendsen, Koen & de Bruyn, Menno & Severens, Valerie & van Hagen, Mark & van Oort, Niels & Duives, Dorine, 2022. "Teleworking during COVID-19 in the Netherlands: Understanding behaviour, attitudes, and future intentions of train travellers," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 55-73.
    15. repec:iab:iabjlr:v:55:i::p:art.4 is not listed on IDEAS
    16. Parker, Madeleine E.G. & Li, Meiqing & Bouzaghrane, Mohamed Amine & Obeid, Hassan & Hayes, Drake & Frick, Karen Trapenberg & Rodríguez, Daniel A. & Sengupta, Raja & Walker, Joan & Chatman, Daniel G., 2021. "Public transit use in the United States in the era of COVID-19: Transit riders’ travel behavior in the COVID-19 impact and recovery period," Transport Policy, Elsevier, vol. 111(C), pages 53-62.
    17. Beck, Matthew J. & Hensher, David A., 2020. "Insights into the impact of COVID-19 on household travel and activities in Australia – The early days under restrictions," Transport Policy, Elsevier, vol. 96(C), pages 76-93.
    18. Shelat, Sanmay & Cats, Oded & van Cranenburgh, Sander, 2022. "Traveller behaviour in public transport in the early stages of the COVID-19 pandemic in the Netherlands," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 357-371.
    19. Holgersen, Henning & Jia, Zhiyang & Svenkerud, Simen, 2021. "Who and how many can work from home? Evidence from task descriptions," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 55(55), pages 1-.4.
    20. Abdullah, Muhammad & Ali, Nazam & Hussain, Syed Arif & Aslam, Atif Bilal & Javid, Muhammad Ashraf, 2021. "Measuring changes in travel behavior pattern due to COVID-19 in a developing country: A case study of Pakistan," Transport Policy, Elsevier, vol. 108(C), pages 21-33.
    21. Beck, Matthew J. & Hensher, David A., 2022. "Working from home in Australia in 2020: Positives, negatives and the potential for future benefits to transport and society," Transportation Research Part A: Policy and Practice, Elsevier, vol. 158(C), pages 271-284.
    22. Valenzuela-Levi, Nicolás, 2021. "The rich and mobility: A new look into the impacts of income inequality on household transport expenditures," Transport Policy, Elsevier, vol. 100(C), pages 161-171.
    23. Hensher, David A. & Wei, Edward & Beck, MatthewJ. & Balbontin, Camila, 2021. "The impact of COVID-19 on cost outlays for car and public transport commuting - The case of the Greater Sydney Metropolitan Area after three months of restrictions," Transport Policy, Elsevier, vol. 101(C), pages 71-80.
    24. Hensher, David A. & Beck, Matthew J. & Wei, Edward, 2021. "Working from home and its implications for strategic transport modelling based on the early days of the COVID-19 pandemic," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 64-78.
    25. Armita Kar & Huyen T. K. Le & Harvey J. Miller, 2022. "What Is Essential Travel? Socioeconomic Differences in Travel Demand in Columbus, Ohio, during the COVID-19 Lockdown," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 112(4), pages 1023-1046, April.
    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. Chen, Ruoyu & Zhang, Min & Zhou, Jiangping, 2023. "Jobs-housing relationships before and amid COVID-19: An excess-commuting approach," Journal of Transport Geography, Elsevier, vol. 106(C).
    2. Magnus Moglia & Stephen Glackin & John L. Hopkins, 2022. "The Working-from-Home Natural Experiment in Sydney, Australia: A Theory of Planned Behaviour Perspective," Sustainability, MDPI, vol. 14(21), pages 1-21, October.
    3. Balbontin, Camila & Hensher, David A. & Beck, Matthew J., 2022. "Advanced modelling of commuter choice model and work from home during COVID-19 restrictions in Australia," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 162(C).
    4. Farrukh Baig & Konstantinos Kirytopoulos & Jaeyoung Lee & Evangelos Tsamilis & Ruizhi Mao & Panagiotis Ntzeremes, 2022. "Changes in People’s Mobility Behavior in Greece after the COVID-19 Outbreak," Sustainability, MDPI, vol. 14(6), pages 1-22, March.
    5. Soria, Jason & Edward, Deirdre & Stathopoulos, Amanda, 2023. "Requiem for transit ridership? An examination of who abandoned, who will return, and who will ride more with mobility as a service," Transport Policy, Elsevier, vol. 134(C), pages 139-154.
    6. Mauro Caselli & Andrea Fracasso & Sergio Scicchitano, 2022. "From the lockdown to the new normal: individual mobility and local labor market characteristics following the COVID-19 pandemic in Italy," Journal of Population Economics, Springer;European Society for Population Economics, vol. 35(4), pages 1517-1550, October.
    7. Shelat, Sanmay & Cats, Oded & van Cranenburgh, Sander, 2022. "Traveller behaviour in public transport in the early stages of the COVID-19 pandemic in the Netherlands," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 357-371.
    8. Kaiser, Caspar, 2022. "Using memories to assess the intrapersonal comparability of wellbeing reports," Journal of Economic Behavior & Organization, Elsevier, vol. 193(C), pages 410-442.
    9. Nie, Qifan & Qian, Xinwu & Guo, Shuocheng & Jones, Steven & Doustmohammadi, Mehrnaz & Anderson, Michael D., 2022. "Impact of COVID-19 on paratransit operators and riders: A case study of central Alabama," Transportation Research Part A: Policy and Practice, Elsevier, vol. 161(C), pages 48-67.
    10. Bagdatli, Muhammed Emin Cihangir & Ipek, Fatima, 2022. "Transport mode preferences of university students in post-COVID-19 pandemic," Transport Policy, Elsevier, vol. 118(C), pages 20-32.
    11. Liu, Shasha & Yamamoto, Toshiyuki, 2022. "Role of stay-at-home requests and travel restrictions in preventing the spread of COVID-19 in Japan," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 1-16.
    12. Siliang Luan & Qingfang Yang & Zhongtai Jiang & Huxing Zhou & Fanyun Meng, 2022. "Analyzing Commute Mode Choice Using the LCNL Model in the Post-COVID-19 Era: Evidence from China," IJERPH, MDPI, vol. 19(9), pages 1-26, April.
    13. Riccardo Ceccato & Riccardo Rossi & Massimiliano Gastaldi, 2021. "Travel Demand Prediction during COVID-19 Pandemic: Educational and Working Trips at the University of Padova," Sustainability, MDPI, vol. 13(12), pages 1-20, June.
    14. Tscharaktschiew, Stefan & Reimann, Felix, 2021. "On employer-paid parking and parking (cash-out) policy: A formal synthesis of different perspectives," Transport Policy, Elsevier, vol. 110(C), pages 499-516.
    15. 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).
    16. Wang, Yiyuan & Shen, Qing & Abu Ashour, Lamis & Dannenberg, Andrew L., 2022. "Ensuring equitable transportation for the disadvantaged: Paratransit usage by persons with disabilities during the COVID-19 pandemic," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 84-95.
    17. Zhang, Junyi & Hayashi, Yoshitsugu, 2022. "Research frontier of COVID-19 and passenger transport: A focus on policymaking," Transport Policy, Elsevier, vol. 119(C), pages 78-88.
    18. Balbontin, Camila & Hensher, David A. & Beck, Matthew J. & Giesen, Ricardo & Basnak, Paul & Vallejo-Borda, Jose Agustin & Venter, Christoffel, 2021. "Impact of COVID-19 on the number of days working from home and commuting travel: A cross-cultural comparison between Australia, South America and South Africa," Journal of Transport Geography, Elsevier, vol. 96(C).
    19. Cui, Zhiwei & Fu, Xin & Wang, Jianwei & Qiang, Yongjie & Jiang, Ying & Long, Zhiyou, 2022. "How does COVID-19 pandemic impact cities' logistics performance? An evidence from China's highway freight transport," Transport Policy, Elsevier, vol. 120(C), pages 11-22.
    20. Aleem, Majid & Sufyan, Muhammad & Ameer, Irfan & Mustak, Mekhail, 2023. "Remote work and the COVID-19 pandemic: An artificial intelligence-based topic modeling and a future agenda," Journal of Business Research, Elsevier, vol. 154(C).

    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:eee:trapol:v:136:y:2023:i:c:p:98-112. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/30473/description#description .

    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.