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Local travel behaviour under continuing COVID-19 waves- A proxy for pandemic fatigue?
Nan Zhang1, Tingrui Hu1, Shujia Shang1
1Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University of Technology, Beijing, China.
Transportation Research Interdisciplinary Perspectives
|January 25, 2023
Summary
Residents reduced local travel during COVID-19 pandemic waves, showing pandemic fatigue. This trend helps predict protective behaviors and inform interventions for future variants like Omicron.
Area of Science:
- Epidemiology
- Public Health
- Transportation Science
Background:
- COVID-19 transmission poses ongoing global health risks.
- Relaxed travel behaviors during the pandemic can escalate SARS-CoV-2 infection rates.
- Understanding travel pattern shifts is crucial for managing viral spread.
Purpose of the Study:
- To analyze changes in local travel behavior across different demographics during four COVID-19 waves in Hong Kong.
- To quantify the extent of travel relaxation and its correlation with pandemic waves.
- To compare Hong Kong's travel trends with global data and discuss implications for future interventions.
Main Methods:
- Utilized 4 billion second-by-second smartcard records from Hong Kong's subway system.
- Analyzed travel behavior changes in adult, child, student, and elderly populations across four pandemic waves (pre-January 2021).
- Compared pandemic-era traffic congestion data with 2019 data to identify global trends.
Main Results:
- Observed a significant, continuous relaxation in local travel behavior across all four pandemic waves.
- Local travel reduction ranged from 51.9% (wave 1) to 20.5% (wave 4).
- Population flow decreased significantly in key areas: workplaces (33.5%), schools (41.9%), shopping (58.1%), amusement (85.4%), and borders (99.6%).
Conclusions:
- Documented 'pandemic fatigue' in local travel behavior, characterized by reduced mobility.
- Findings suggest that quantitative analysis of travel behavior can aid in predicting public response to health crises.
- This data can inform targeted public health interventions for subsequent waves, particularly concerning highly transmissible variants like Omicron.
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