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Psychological anxiety and transport mode choice during the early COVID-19 outbreak: a machine learning approach to crisis-time urban mobility
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DOI：10.1080/12265934.2026.2667317.png)
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Abstract
En 中文
This study explores how psychological anxiety shaped individual transport mode choices during the early stages of the COVID-19 outbreak, prior to the implementation of nationwide restriction policies. Focusing on the intersection of public health and urban mobility, we investigate how factors such as awareness, fear, and coronaphobia influenced modal shift decisions in the absence of formal restrictions. A web-based survey was conducted in Daegu, South Korea – one of the first metropolitan areas to experience a major outbreak. Data were collected from 417 residents, covering psychological perceptions, transport usage before and after the outbreak, and preferred safety measures. We applied an ensemble classification model using the AdaBoost algorithm to predict modal shifts across various trip purposes, leveraging both sociodemographic and psychological indicators. Results show that heightened psychological anxiety led to significant reductions in public transit use, with a corresponding increase in the use of private cars, bicycles, and walking. These shifts occurred even without policy-enforced restrictions, suggesting that behavioural response to health risk perception plays a critical role in shaping urban transport demand. The study highlights the importance of integrating psychological dimensions into transport planning, particularly during crisis conditions. Findings support the development of adaptive, health-sensitive mobility strategies capable of responding not only to government interventions but also to individual psychological reactions in times of public health emergencies.
Highlights
Psychological anxiety shaped urban travel choices during the coronavirus outbreak.
Fear of infection reduced public transport use and encouraged safer travel modes.
A two-stage framework assessed whether and how travelers changed modes.
Machine learning predicted pandemic transport mode shifts with high overall accuracy.
Findings support health-sensitive planning for resilient urban transport systems.
Keywords:
COVID-19 outbreak
transport mode choice
psychological anxiety
coronaphobia
behavioral response
machine learning
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