Reconstructing mobility from smartphone data: Empirical evidence of the effects of COVID-19 pandemic crisis on working and leisure

Abstract

Identifying daily mobility patterns of commuters can be of great importance to researchers, authorities, operators and transportation service providers. Further, in the light of the global COVID-19 pandemic, understanding the changes of mobility patterns induced by the imposed restrictions to the general public, may have significant impact on how we conceptualize, design and operate the future transportation systems. However, such analyses may require extensive mobility related datasets that are very difficult and expensive to acquire using traditional data collection approaches. In this work, we aim at proposing a methodology to identify and characterize users’ mobility patterns and augment the information captured by classical travel surveys. We apply this framework on anonymized raw smartphone sensors data gathered in Athens (Greece) to identify changes in mobility chains as an effect of COVID-19 restrictions. The methodological framework is based on a mixture of clustering and rule-based approaches. The proposed methodology is able to consistently detect the most frequently visited locations of each user, identify the related primary and secondary activities and finally, construct their daily trip chains. Findings reveal that people travel less frequent, but for longer especially on weekends. During lockdown periods, home-related activities have significantly increased, both in weekdays and weekends. In addition, COVID-19 regulations resulted in a significant reduction of the spatial randomness of the conducted trips. Finally, work-study trip chains have seen evident growth, as an aftermath of tele-working and tele-studying regulations.