Social-distancing fatigue: Evidence from real-time crowd-sourced traffic data

Area of Science:

• Epidemiology
• Urban Mobility Studies
• Public Health Policy

Background:

• Social distancing policies were implemented globally to curb COVID-19 spread.
• These measures aimed to reduce close contact and human mobility.
• Quantifying mobility changes is crucial for understanding policy impact.

Purpose of the Study:

• To analyze human mobility changes in New York City (NYC) during the COVID-19 pandemic.
• To assess the impact of social distancing interventions on traffic patterns.
• To identify potential social-distancing fatigue.

Main Methods:

• Utilized real-time Google traffic data in NYC as a proxy for human mobility.
• Applied image processing and time series decomposition for high-resolution traffic analysis.
• Employed generalized additive models to quantify traffic changes pre- and post-interventions.

Main Results:

• Mobility significantly decreased following the pandemic declaration on March 14, 2020.
• Traffic levels began to rebound by April 13, 2020, preceding the lifting of stay-at-home orders.
• Weekday rush hour congestion patterns were altered, and by September, traffic reached 75% of pre-pandemic levels.

Conclusions:

• Crowd-sourced traffic data effectively tracked mobility shifts in NYC during the pandemic.
• Findings highlight the phenomenon of social-distancing fatigue and premature mobility increases.
• Data can inform current pandemic responses, future pandemic preparedness, and traffic intervention strategies.