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.

对人们的移动模式的定量理解对于许多应用程序至关重要。然而，很难准确估计流动性，尤其是在 COVID-19 大流行爆发等中断期间。在这里，我们调查了使用来自手机、道路交通传感器以及谷歌和 Facebook 等公司的多种数据源在移动模式建模中的使用情况，目的是估计 2020 年初芬兰在交通中断之前和期间的移动流量受疫情影响。我们发现，将手机数据的过去基线与最新的道路交通数据相结合的模型提供了最高的准确性，其次是辐射和重力模型，类似地增加了交通数据。我们的结果强调了公开可用的道路交通数据在移动建模中的有用性，并且，