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The effect of dynamic lockdowns on public transport demand in times of COVID-19: Evidence from smartcard data
Governments around the globe have taken different measures to tackle the COVID-19 pandemic, including the lockdown of people to decrease infections. The effect of such a strategy on transport demand is important not only for the current pandemic but also to understand changes in transport use and fo...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9283383/ https://www.ncbi.nlm.nih.gov/pubmed/35855479 http://dx.doi.org/10.1016/j.tranpol.2022.06.012 |
Sumario: | Governments around the globe have taken different measures to tackle the COVID-19 pandemic, including the lockdown of people to decrease infections. The effect of such a strategy on transport demand is important not only for the current pandemic but also to understand changes in transport use and for future emergencies. We analyse a 2019–2020 database of smartcard data of trips from the city of Santiago, Chile, which followed a dynamic lockdown strategy in which its municipalities were temporarily restricted. We use this variation over time across municipalities to study the effect of lockdowns on public transportation using trips on buses and metro, accounting for the variation of municipalities that were under lockdown in a given day. We found a decrease of 72.3% at the beginning of the pandemic when schools suspended in-person classes, while the dynamic lockdowns reduced public transport demand by 12.1%. We also found that the effect of lockdowns decreased after the fifth week of their application, suggesting a short-term effectiveness of such policy to reduce mobility. Regarding sociodemographic effects, we found that lockdowns have a stronger impact on reducing public transport demand in municipalities with a larger proportion of the elderly population (2% additional reduction per 1% increase in the share of the elderly population) and high-income households (16% additional reduction for 1000 USD increase in GDP per capita). |
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