Exploring the Highway Travel Patterns Affected by COVID-19 through Outbreak to Recovery Stages – A Case Study in Guizhou Province

Weizheng LIU; Yanyan CHEN

doi:10.7307/ptt.v36i3.482

Authors

Weizheng LIU School of Urban Transportation, Beijing University of Technology; CCCC Asset Management Company Limited, Beijing
Yanyan CHEN School of Urban Transportation, Beijing University of Technology

DOI:

https://doi.org/10.7307/ptt.v36i3.482

Keywords:

COVID-19 pandemic, highway transaction dataset, travel behaviour, complex network analysis, community detection

Abstract

The examination of highway travel behaviour during the COVID-19 pandemic can provide valuable insights into the impacts of the pandemic and associated policies on human mobility patterns. This paper proposes a comprehensive examination, measurement and characterisation approach in the perspective of network and community structure. To capture the changes in travel behaviour, four stages were defined based on four consecutive Augusts from 2019 to 2022, during which varying levels of restrictions were implemented. The findings reveal interesting trends in travel patterns. In 2020, after the clearance of pandemic cases, there was a remarkable increase of over 10% in highway trips. However, in 2021, with the emergence of COVID-19 variants, there was a significant decline of over 30% in highway trips. By employing complex network analysis, key metrics of the primary network, including link weight, node flux and network connectivity, exhibited a notable decrease during the pandemic. These changes in network properties also reflect the spatial heterogeneity of highway travel demand. Moreover, the outcomes of community detection shed light on the evolution of the highway community structure, highlighting the efficacy of a community-collaboration strategy for highway management during public emergency events, as it fosters strong local interaction within the community.

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