Mobility Dynamics amid COVID-19 with a Case Study in Tennessee

Area of Science:

• Transportation Science
• Urban Planning
• Epidemiology

Background:

• The COVID-19 pandemic drastically altered daily life and mobility patterns globally.
• Social distancing measures, including stay-at-home orders, were implemented to curb virus transmission, leading to substantial traffic declines.
• Understanding the nuances of traffic recovery is crucial for urban planning and public health response.

Purpose of the Study:

• To analyze county-level changes in vehicle miles traveled (VMT) during the COVID-19 pandemic.
• To identify key socioeconomic and demographic factors influencing traffic decline and recovery.
• To explore spatial heterogeneity in mobility changes using geographically weighted regression (GWR).

Main Methods:

• Selected 95 counties in Tennessee as the study area.
• Employed geographically weighted regression (GWR) models to analyze spatial variations.
• Examined correlations between VMT changes and factors like road density, income, unemployment, and demographics.

Main Results:

• Several factors, including non-freeway road density, median household income, unemployment rates, population density, age demographics, work-from-home prevalence, and commute times, significantly correlated with VMT changes.
• GWR models effectively captured spatial heterogeneity and local variations in these relationships.
• Traffic recovery patterns exhibited significant diversity across different counties.

Conclusions:

• Socioeconomic and spatial factors play a critical role in shaping traffic decline and recovery dynamics during pandemics.
• The study's findings suggest that recovery phases can be estimated based on identified spatial attributes.
• The developed model offers a valuable tool for agencies and researchers to manage and predict mobility changes during future public health crises.