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Activity Duration under the COVID-19 Pandemic: A Comparative Analysis among Different Urbanized Areas Using a Hazard-Based Duration Model

Author

Listed:
  • Chunguang Liu

    (Don School, International Education College, Shandong Jiaotong University, Jinan 250357, China)

  • Xinyu Zuo

    (Don School, International Education College, Shandong Jiaotong University, Jinan 250357, China)

  • Xiaoning Gu

    (State Key Laboratory of Structural Analysis for Industrial Equipment, School of Automotive Engineering, Dalian University of Technology, Dalian 116024, China)

  • Mengru Shao

    (Urban Planning and Transportation, Department of the Built Environment, Eindhoven University of Technology, 5600MB Eindhoven, The Netherlands)

  • Chao Chen

    (State Key Laboratory of Structural Analysis for Industrial Equipment, School of Automotive Engineering, Dalian University of Technology, Dalian 116024, China)

Abstract

There have been significant changes in daily activities and corresponding durations since the outbreak of COVID-19. This study examines how the built environment factors and individual/household characteristics affect activity durations (e.g., shopping, social-related, hiking, and working) under the COVID-19 pandemic and analyzes the heterogeneity between different urbanized areas using the data of a Dutch national travel survey in 2020. A hazard-based duration model (e.g., the Cox proportional hazard model) was used to predict activity durations. Estimation results showed that the activity durations for different social groups varied under different geographical and policy conditions. In particular, women and seniors are more susceptible to the unprecedented pandemic, manifested in significantly shorter durations for work and hiking activities. In addition, couples with one or more children need to shorten their working hours and give more attention to their children due to the closure of nurseries and schools. Furthermore, the influences of built environment factors also present significant differences. A higher number of service facilities does not significantly foster the extension of hiking activity duration; however, this is the opposite among regions with more open green areas. Compared with previous studies on analyzing the influencing factors of activity durations, this study incorporated some unique variables (e.g., COVID-19 countermeasures and urban class) to consider the temporal and spatial heterogeneity under the particular pandemic period.

Suggested Citation

  • Chunguang Liu & Xinyu Zuo & Xiaoning Gu & Mengru Shao & Chao Chen, 2023. "Activity Duration under the COVID-19 Pandemic: A Comparative Analysis among Different Urbanized Areas Using a Hazard-Based Duration Model," Sustainability, MDPI, vol. 15(12), pages 1-28, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9537-:d:1170655
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    1. Dissanayake, Dilum, 2017. "Watching the clock on the way to work? Analysing trends in commuting activities, modes and gender differences in commute times, using hazard-based duration modelling methods," Journal of Transport Geography, Elsevier, vol. 65(C), pages 188-199.
    2. Eisenmann, Christine & Nobis, Claudia & Kolarova, Viktoriya & Lenz, Barbara & Winkler, Christian, 2021. "Transport mode use during the COVID-19 lockdown period in Germany: The car became more important, public transport lost ground," Transport Policy, Elsevier, vol. 103(C), pages 60-67.
    3. Yee, Julie L. & Niemeier, Debbie A., 2000. "Analysis of activity duration using the Puget sound transportation panel," Transportation Research Part A: Policy and Practice, Elsevier, vol. 34(8), pages 607-624, November.
    4. Wenjia Zhang & Kexin Ning, 2023. "Spatiotemporal Heterogeneities in the Causal Effects of Mobility Intervention Policies during the COVID-19 Outbreak: A Spatially Interrupted Time-Series (SITS) Analysis," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 113(5), pages 1112-1134, May.
    5. Aggelos Tsaligopoulos & Stella Kyvelou & Nefta-Eleftheria Votsi & Aimilia Karapostoli & Chris Economou & Yiannis G. Matsinos, 2021. "Revisiting the Concept of Quietness in the Urban Environment—Towards Ecosystems’ Health and Human Well-Being," IJERPH, MDPI, vol. 18(6), pages 1-19, March.
    6. Bei Liu & Hong Chen & Xin Gan, 2019. "How Much Is Too Much? The Influence of Work Hours on Social Development: An Empirical Analysis for OECD Countries," IJERPH, MDPI, vol. 16(24), pages 1-15, December.
    7. Clark, Ben & Chatterjee, Kiron & Melia, Steve, 2016. "Changes to commute mode: The role of life events, spatial context and environmental attitude," Transportation Research Part A: Policy and Practice, Elsevier, vol. 89(C), pages 89-105.
    8. Chen, Chao & Feng, Tao & Gu, Xiaoning & Yao, Baozhen, 2022. "Investigating the effectiveness of COVID-19 pandemic countermeasures on the use of public transport: A case study of The Netherlands," Transport Policy, Elsevier, vol. 117(C), pages 98-107.
    9. Calastri, Chiara & Hess, Stephane & Daly, Andrew & Carrasco, Juan Antonio, 2017. "Does the social context help with understanding and predicting the choice of activity type and duration? An application of the Multiple Discrete-Continuous Nested Extreme Value model to activity diary," Transportation Research Part A: Policy and Practice, Elsevier, vol. 104(C), pages 1-20.
    10. Rafiq, Rezwana & McNally, Michael G. & Sarwar Uddin, Yusuf & Ahmed, Tanjeeb, 2022. "Impact of working from home on activity-travel behavior during the COVID-19 Pandemic: An aggregate structural analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 35-54.
    11. Maria Elvén & Birgitta Kerstis & Jonas Stier & Charlotta Hellström & Petra von Heideken Wågert & Micael Dahlen & Daniel Lindberg, 2022. "Changes in Physical Activity and Sedentary Behavior before and during the COVID-19 Pandemic: A Swedish Population Study," IJERPH, MDPI, vol. 19(5), pages 1-15, February.
    12. Bhat, Chandra R., 2008. "The multiple discrete-continuous extreme value (MDCEV) model: Role of utility function parameters, identification considerations, and model extensions," Transportation Research Part B: Methodological, Elsevier, vol. 42(3), pages 274-303, March.
    13. Yusak Susilo & Kay Axhausen, 2014. "Repetitions in individual daily activity–travel–location patterns: a study using the Herfindahl–Hirschman Index," Transportation, Springer, vol. 41(5), pages 995-1011, September.
    14. Park, Keunhyun & Chamberlain, Brent & Song, Ziqi & Nasr Esfahani, Hossein & Sheen, Jeff & Larsen, Teresa & Long Novack, Valerie & Licon, Carlos & Christensen, Keith, 2022. "A double jeopardy: COVID-19 impacts on the travel behavior and community living of people with disabilities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 156(C), pages 24-35.
    15. Mohammadian, Abolfazl & Doherty, Sean T., 2006. "Modeling activity scheduling time horizon: Duration of time between planning and execution of pre-planned activities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(6), pages 475-490, July.
    16. Enam, Annesha & Konduri, Karthik C. & Pinjari, Abdul R. & Eluru, Naveen, 2018. "An integrated choice and latent variable model for multiple discrete continuous choice kernels: Application exploring the association between day level moods and discretionary activity engagement choi," Journal of choice modelling, Elsevier, vol. 26(C), pages 80-100.
    17. Dane, Gamze & Arentze, Theo A. & Timmermans, Harry J.P. & Ettema, Dick, 2014. "Simultaneous modeling of individuals’ duration and expenditure decisions in out-of-home leisure activities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 70(C), pages 93-103.
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