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Examining the long term impacts of COVID-19 using an integrated transport and land-use modelling system

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  • Muhammad Ahsanul Habib
  • Md Asif Hasan Anik

Abstract

This paper investigates the long-term impacts of COVID-19 on transport and land-use systems through understanding changes in households’ long-term choices, such as residential location choice, travel tool ownership and type choice. For that, it first develops two possible scenarios, which are, (i) without COVID-19 pandemic, (ii) with COVID-19 pandemic. Specific behavioural attributes of households associated with each scenario are modelled and simulated up to the year 2030 within an integrated transport, land-use and energy (iTLE) model. iTLE is an agent-based microsimulation model developed for Nova Scotia, Canada. Results show that, if COVID-19 persists, most people who do not own a car will continue restraining themselves from purchasing one up to the year 2027. Most households having heads aged between 30 and 75 years and a yearly income above $100,000 will purchase new vehicles in year 2022, making the vehicle purchase rate doubled in the province. In terms of vehicle type choice decision, the use of SUVs will increase by 25% and 27% by 2025 and 2030, respectively, compared to year 2020. The pandemic will cause a 0%–74% increase in vehicle ownership in suburban areas by 2030. Additionally, the ability to ‘work from home’ and online school facilities may influence people to live further away from downtown. The results of this study will offer transport and land-use planners insights into how households’ long-term decision making may evolve in the future due to the COVID-19 crisis and help develop policies to continue focusing on the sustainability goals for communities.

Suggested Citation

  • Muhammad Ahsanul Habib & Md Asif Hasan Anik, 2021. "Examining the long term impacts of COVID-19 using an integrated transport and land-use modelling system," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 25(3), pages 323-346, July.
  • Handle: RePEc:taf:rjusxx:v:25:y:2021:i:3:p:323-346
    DOI: 10.1080/12265934.2021.1951821
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    Cited by:

    1. Xu, Ningzhe & Nie, Qifan & Liu, Jun & Jones, Steven, 2024. "Linking short- and long-term impacts of the COVID-19 pandemic on travel behavior and travel preferences in Alabama: A machine learning-supported path analysis," Transport Policy, Elsevier, vol. 151(C), pages 46-62.
    2. Nicholas S. Caros & Jinhua Zhao, 2022. "Preparing urban mobility for the future of work," Papers 2201.01321, arXiv.org.

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