IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i11p6066-d568951.html
   My bibliography  Save this article

Using Bus Ticketing Big Data to Investigate the Behaviors of the Population Flow of Chinese Suburban Residents in the Post-COVID-19 Phase

Author

Listed:
  • Yanbing Bai

    (Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, China
    These authors contributed equally to this work.)

  • Lu Sun

    (School of Statistics, Southwestern University of Finance and Economics, Chengdu 611130, China
    These authors contributed equally to this work.)

  • Haoyu Liu

    (Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, China)

  • Chao Xie

    (China Transport Information Co., Ltd., Beijing 100007, China
    China Transport Telecommunications and Information Center, Beijing 100011, China)

Abstract

Large-scale population movements can turn local diseases into widespread epidemics. Grasping the characteristic of the population flow in the context of the COVID-19 is of great significance for providing information to epidemiology and formulating scientific and reasonable prevention and control policies. Especially in the post-COVID-19 phase, it is essential to maintain the achievement of the fight against the epidemic. Previous research focuses on flight and railway passenger travel behavior and patterns, but China also has numerous suburban residents with a not-high economic level; investigating their travel behaviors is significant for national stability. However, estimating the impacts of the COVID-19 for suburban residents’ travel behaviors remains challenging because of lacking apposite data. Here we submit bus ticketing data including approximately 26,000,000 records from April 2020–August 2020 for 2705 stations. Our results indicate that Suburban residents in Chinese Southern regions are more likely to travel by bus, and travel frequency is higher. Associated with the economic level, we find that residents in the economically developed region more likely to travel or carry out various social activities. Considering from the perspective of the traveling crowd, we find that men and young people are easier to travel by bus; however, they are exactly the main workforce. The indication of our findings is that suburban residents’ travel behavior is affected profoundly by economy and consistent with the inherent behavior patterns before the COVID-19 outbreak. We use typical regions as verification and it is indeed the case.

Suggested Citation

  • Yanbing Bai & Lu Sun & Haoyu Liu & Chao Xie, 2021. "Using Bus Ticketing Big Data to Investigate the Behaviors of the Population Flow of Chinese Suburban Residents in the Post-COVID-19 Phase," IJERPH, MDPI, vol. 18(11), pages 1-16, June.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:11:p:6066-:d:568951
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/11/6066/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/11/6066/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ivanova, Maya & Ivanov, Ivan Krasimirov & Ivanov, Stanislav Hristov, 2020. "Travel behaviour after the pandemic: the case of Bulgaria," SocArXiv 36rkb, Center for Open Science.
    2. Yu, Chang & He, Zhao-Cheng, 2017. "Analysing the spatial-temporal characteristics of bus travel demand using the heat map," Journal of Transport Geography, Elsevier, vol. 58(C), pages 247-255.
    3. Singhal, Amit & Singh, Pushpendra & Lall, Brejesh & Joshi, Shiv Dutt, 2020. "Modeling and prediction of COVID-19 pandemic using Gaussian mixture model," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    4. Xiao Ma & Feiran Wang & Jiandong Chen & Yang Zhang, 2018. "The Income Gap Between Urban and Rural Residents in China: Since 1978," Computational Economics, Springer;Society for Computational Economics, vol. 52(4), pages 1153-1174, December.
    5. Zhang, Xiaolei & Ma, Renjun & Wang, Lin, 2020. "Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    6. Julia Anderson & Simone Tagliapietra & Guntram B. Wolff, 2020. "Rebooting Europe- a framework for a post COVID-19 economic recovery," Policy Briefs 36658, Bruegel.
    7. Chia-Lin Chang & Michael McAleer & Vicente Ramos, 2020. "A Charter for Sustainable Tourism after COVID-19," Sustainability, MDPI, vol. 12(9), pages 1-4, May.
    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. Da Van Huynh & Thuy Thi Kim Truong & Long Hai Duong & Nhan Trong Nguyen & Giang Vu Huong Dao & Canh Ngoc Dao, 2021. "The COVID-19 Pandemic and Its Impacts on Tourism Business in a Developing City: Insight from Vietnam," Economies, MDPI, vol. 9(4), pages 1-17, November.
    2. Salinas Fernández, José Antonio & Guaita Martínez, José Manuel & Martín Martín, José María, 2022. "An analysis of the competitiveness of the tourism industry in a context of economic recovery following the COVID19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    3. Agnès Bénassy-Quéré & Beatrice Weder di Mauro, 2020. "European Pandemic Recovery: An Opportunity to Reboot," Intereconomics: Review of European Economic Policy, Springer;ZBW - Leibniz Information Centre for Economics;Centre for European Policy Studies (CEPS), vol. 55(4), pages 205-209, July.
    4. Łukasz Mamica & Jakub Głowacki & Kamil Makieła, 2021. "Determinants of the Energy Poverty of Polish Students during the COVID-19 Pandemic," Energies, MDPI, vol. 14(11), pages 1-15, June.
    5. Zahra Dehghan Shabani & Rouhollah Shahnazi, 2020. "Spatial distribution dynamics and prediction of COVID‐19 in Asian countries: spatial Markov chain approach," Regional Science Policy & Practice, Wiley Blackwell, vol. 12(6), pages 1005-1025, December.
    6. Singhal, Amit & Singh, Pushpendra & Lall, Brejesh & Joshi, Shiv Dutt, 2020. "Modeling and prediction of COVID-19 pandemic using Gaussian mixture model," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    7. Christian M. Hafner, 2020. "The Spread of the Covid-19 Pandemic in Time and Space," IJERPH, MDPI, vol. 17(11), pages 1-13, May.
    8. Merkebe Getachew Demissie & Lina Kattan, 2022. "Understanding the temporal and spatial interactions between transit ridership and urban land-use patterns: an exploratory study," Public Transport, Springer, vol. 14(2), pages 385-417, June.
    9. Monika Widz & Renata Krukowska & Bartłomiej Walas & Zygmunt Kruczek, 2022. "Course of Values of Key Performance Indicators in City Hotels during the COVID-19 Pandemic: Poland Case Study," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
    10. Anca-Gabriela Turtureanu & Rodica Pripoaie & Carmen-Mihaela Cretu & Carmen-Gabriela Sirbu & Emanuel Ştefan Marinescu & Laurentiu-Gabriel Talaghir & Florentina Chițu, 2022. "A Projection Approach of Tourist Circulation under Conditions of Uncertainty," Sustainability, MDPI, vol. 14(4), pages 1-21, February.
    11. Larissa Batrancea, 2021. "The Nexus between Financial Performance and Equilibrium: Empirical Evidence on Publicly Traded Companies from the Global Financial Crisis Up to the COVID-19 Pandemic," JRFM, MDPI, vol. 14(5), pages 1-12, May.
    12. Luca Bonacini & Giovanni Gallo & Fabrizio Patriarca, 2021. "Identifying policy challenges of COVID-19 in hardly reliable data and judging the success of lockdown measures," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(1), pages 275-301, January.
    13. Jiandong Chen & Sishi Rong & Malin Song, 2021. "Poverty Vulnerability and Poverty Causes in Rural China," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 153(1), pages 65-91, January.
    14. Alba Viana-Lora & Marta Gemma Nel-lo-Andreu, 2022. "Bibliometric analysis of trends in COVID-19 and tourism," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-8, December.
    15. Lu, Haiyan & Zhao, Pengjun & Hu, Haoyu & Zeng, Liangen & Wu, Kai Sheng & Lv, Di, 2022. "Transport infrastructure and urban-rural income disparity: A municipal-level analysis in China," Journal of Transport Geography, Elsevier, vol. 99(C).
    16. Beatriz Palacios-Florencio & Luna Santos-Roldán & Juan Manuel Berbel-Pineda & Ana María Castillo-Canalejo, 2021. "Sustainable Tourism as a Driving force of the Tourism Industry in a Post-Covid-19 Scenario," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 158(3), pages 991-1011, December.
    17. Amaral, Marco A. & Oliveira, Marcelo M. de & Javarone, Marco A., 2021. "An epidemiological model with voluntary quarantine strategies governed by evolutionary game dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    18. Swapnarekha, H. & Behera, Himansu Sekhar & Nayak, Janmenjoy & Naik, Bighnaraj, 2020. "Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    19. Huang, Chiou-Jye & Shen, Yamin & Kuo, Ping-Huan & Chen, Yung-Hsiang, 2022. "Novel spatiotemporal feature extraction parallel deep neural network for forecasting confirmed cases of coronavirus disease 2019," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    20. Yusuke Kitamura & Selim Karkour & Yuki Ichisugi & Norihiro Itsubo, 2020. "Evaluation of the Economic, Environmental, and Social Impacts of the COVID-19 Pandemic on the Japanese Tourism Industry," Sustainability, MDPI, vol. 12(24), pages 1-22, December.

    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:jijerp:v:18:y:2021:i:11:p:6066-:d:568951. 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.