Social-distancing Fatigue: Evidence from Real-time Crowd-sourced Traffic Data

Area of Science:

• Urban mobility studies
• Epidemiology
• Public health policy

Background:

• Social distancing policies were implemented globally to mitigate COVID-19 spread.
• Quantifying human mobility is crucial for understanding policy impact and disease transmission.

Approach:

• Utilized real-time Google traffic data in New York City (NYC) as a proxy for human mobility.
• Applied time series decomposition and generalized additive models to analyze traffic patterns pre- and post-intervention.
• Examined changes in rush hour, non-rush hour, weekday, and weekend traffic.

Key Points:

• Mobility significantly decreased following the pandemic declaration on March 14, 2020.
• Traffic began to rebound by April 13, indicating premature increase in mobility or "social-distancing fatigue."
• Weekday rush hour patterns were significantly altered, with September traffic reaching 75% of pre-pandemic levels.

Conclusions:

• Crowd-sourced traffic data effectively describes mobility changes in NYC during the pandemic.
• Findings can inform current pandemic responses and future public health planning.
• Insights are valuable for understanding the effects of large-scale traffic interventions, like congestion pricing.