Estimating inter-regional mobility during disruption: Comparing and combining different data sources
journal contributionposted on 2023-02-14, 10:09 authored by Sara Heydari, Zhiren Huang, Takayuki Hiraoka, Alejandro Ponce de León Chávez, Tapio Ala-NissilaTapio Ala-Nissila, Lasse Leskelä, Mikko Kivelä, Jari Saramäki
A quantitative understanding of people's mobility patterns is crucial for many applications. However, it is difficult to accurately estimate mobility, in particular during disruption such as the onset of the COVID-19 pandemic. Here, we investigate the use of multiple sources of data from mobile phones, road traffic sensors, and companies such as Google and Facebook in modelling mobility patterns, with the aim of estimating mobility flows in Finland in early 2020, before and during the disruption induced by the pandemic. We find that the highest accuracy is provided by a model that combines a past baseline from mobile phone data with up-to-date road traffic data, followed by the radiation and gravity models similarly augmented with traffic data. Our results highlight the usefulness of publicly available road traffic data in mobility modelling and, in general, pave the way for a data fusion approach to estimating mobility flows.
Strategic Research Council at the Academy of Finland (NetResilience consortium, Grant Nos. 345188 and 345183)
- Mathematical Sciences
Published inTravel Behaviour and Society
Pages93 - 105
- VoR (Version of Record)
Rights holder© The Authors
Publisher statementThis is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).