Understanding Changes in Travel Patterns during the COVID-19 Outbreak in the Three Major Metropolitan Areas of Japan
Takao Dantsuji, Kashin Sugishita, Daisuke Fukuda

TL;DR
This study analyzes how COVID-19 and related policies affected travel patterns in Japan's major metropolitan areas using mobile phone data and network analysis, revealing significant behavioral shifts during and after the state of emergency.
Contribution
It introduces a novel framework applying network science to longitudinal mobility data, identifying six main travel pattern types and their changes during the pandemic in Japan.
Findings
Travel patterns changed from March 2020, coinciding with school closures.
Post-SOE, travel patterns gradually reverted to pre-SOE states.
Voluntary behavioral changes occurred in Nagoya with rising case numbers.
Abstract
Unlike the lockdown measures taken in some countries or cities, the Japanese government declared a "State of Emergency" (SOE) under which people were only requested to reduce their contact with other people by at least 70 %, while some local governments also implemented their own mobility-reduction measures that had no legal basis. The effects of these measures are still unclear. Thus, in this study, we investigate changes in travel patterns in response to the COVID-19 outbreak and related policy measures in Japan using longitudinal aggregated mobile phone data. Specifically, we consider daily travel patterns as networks and analyze their structural changes by applying a framework for analyzing temporal networks used in network science. The cluster analysis with the network similarity measures across different dates showed that there are six main types of mobility patterns in the three…
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Taxonomy
TopicsHuman Mobility and Location-Based Analysis · Data-Driven Disease Surveillance · Complex Network Analysis Techniques
