A Data-Driven Approach to Analyse the Impacts of the COVID-19 Pandemic on Mobility Behaviour - An Exploration of the Data Landscape

This paper presents an exploration of the data landscape to capture the impact of the COVID-19 pandemic on people’s mobility behaviour in Germany. The data basis consists of a wide range of data sources including floating car data, count data from automated vehicle, bicycle, and pedestrian count stations, number of flight arrivals and departures as well as web-scraped location data of electric motor scooters, kick scooters and bicycles from shared mobility providers. For each source of data, it is elaborated how it can be used to generate insights on changes in mobility behaviour due to the pandemic. In addition, the potential of the combination of information from different sources of data is explored in order to validate the generated insights. In this way it is highlighted what can be learned from the existing data landscape on the pandemic’s impact on mobility behaviour. Furthermore, inaccuracies, ambiguities and information gaps are identified that illustrate where the existing data landscape has to be improved in order to provide more value for transportation research. For this purpose, the findings of this study are situated in the scholarly literature. In particular, the advantages and disadvantages of the data sources used in this paper to the ones used in other studies are discussed in order to reveal how different sources of data can supplement each other and what information are needed to provide a holistic picture of the impact of the COVID-19 pandemic on mobility behaviour.


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Maps; References; Tables;
  • Pagination: 23p

Subject/Index Terms

Filing Info

  • Accession Number: 01852286
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-22-00774
  • Files: TRIS, TRB, ATRI
  • Created Date: Jul 21 2022 11:39AM