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Estimating Mode of Transport in Daily Mobility during the COVID-19 Pandemic Using a Multinomial Logistic Regression
Jaroslav Mazanec1, Veronika Harantová2, Vladimíra Štefancová3
1Department of Quantitative Methods and Economic Informatics, Faculty of Operation and Economics of Transport and Communications, University of Zilina, 01026 Zilina, Slovakia.
Summary
The COVID-19 pandemic shifted travel behavior. A study found a predictive model accurately estimates transport choices, with cars preferred, but public transport vital for non-car owners.
Area of Science:
- Transportation Science
- Public Health
- Behavioral Economics
Background:
- The COVID-19 pandemic significantly altered global travel patterns starting in 2020.
- Commuting behaviors for work and school were particularly impacted by pandemic-related restrictions and concerns.
Purpose of the Study:
- To analyze changes in traveler behavior during the COVID-19 pandemic.
- To develop a predictive model for transportation mode choice among commuters.
- To inform transport policy and planning, especially during public health crises.
Main Methods:
- An online survey was conducted with 2000 respondents across two countries.
- Multinomial regression analysis was applied to the collected survey data.
- Independent variables were used to predict the most utilized modes of transport.
Main Results:
- The developed multinomial model achieved nearly 70% accuracy in predicting transport mode.
- Cars were identified as the most frequently preferred mode of transport.
- Commuters without access to a car prioritized public transport over walking.
Conclusions:
- Predictive models of travel behavior are crucial for effective transport policy.
- Findings highlight the importance of public transport for non-car owners, especially during disruptions.
- The model can aid in planning and policy creation for exceptional circumstances affecting mobility.
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