Mepparambath, R. M., Nguyen Huynh, H., Oon, J., Song, J., Zhu, R., & Feng, L. (2023). The impact of COVID-19 pandemic on the fundamental urban mobility theories using transit data from Singapore. Transportation Research Interdisciplinary Perspectives, 21, 100883. https://doi.org/10.1016/j.trip.2023.100883
Abstract:
The COVID-19 pandemic has caused changes in urban mobility patterns due to the temporary movement restrictive policies imposed by local governments and the long-term hybrid work arrangements adapted by companies. This study re-examines the fundamental theories of human mobility in Singapore at the pre-pandemic, pandemic and endemic stages. In particular, we estimated three well-established mobility models in complexity science: gravity model, radiation model and visitation law for investigating the effects of COVID-19 pandemic in different stages, using the transit Origin-Destination (O-D) data collected in Singapore. Overall, the results show that the three mobility theories remain valid throughout the different periods with visitation law outperforming the gravity and the radiation models. At the pre-pandemic stage, the performance of the gravity and radiation models were comparable. The performance of the gravity model has improved compared to its pre-pandemic performance, showing 6–18% improvement at the pandemic stage and 9–15% improvement at the post pandemic stage, whereas the performance of the radiation model hasn’t changed much, making gravity model a better-performing model than the radiation model for these periods. Our results also show that in the gravity and radiation models, gross floor area is a better predictor for transit flows than the population, whereas in the visitation law use of population variable results in better predictions. Our study is a first attempt in providing a revised understanding of the performance of the fundamental human mobility laws in the post-pandemic world.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
This research / project is supported by the A*STAR - Social Sciences Innovation Seed Fund (SSISF)
Grant Reference no. : C211618002