Impacts of the COVID-19 pandemic in the demand for urban transportation in Budapest

• Published in: Transportation research procedia (Online) 2022, Vol.62, p.99-106
• Main Authors: de Souza, Rodrigo Netto, Mátrai, Tamás
• Format: Article
• Language: English
• Subjects: Budapest; COVID-19; lockdown; macroscopic model; mobility demand
• ISSN: 2352-1465; 2352-1465
• DOI: 10.1016/j.trpro.2022.02.013

Social distancing guidelines established amid the COVID-19 pandemic have decreased the number of trips in urban transportation networks; furthermore, travelers have shifted away from high occupancy modes due to the fear of contagion. This scenario has led to reduced public transportation ridership and increased shares of private cars, cycling and walking in urban areas. In the international literature, predictive models for this scenario of changed travel behavior and imminent needs for operations and planning adjustments, however, are still scarce or limited in scope. Holt-Winter’s multiplicative method was used to extrapolate pre-pandemic datasets as a means to evaluate the impacts of the pandemic in transportation activities in Budapest. Data from March 2020 indicate that stay-at-home orders have resulted in intra-city and commuter traffic reductions of about 35%, while public transportation ticket sales decreased by 90%. Bicycle traffic, on the other hand, increased by about 13% in the same period. These observations suggest that the COVID-19 pandemic has driven significant changes in trip generation and mode choice in Budapest. This study proposes the adjustment of a pre-existing four-step transportation model of Budapest based on the introduction of contextual explanatory variables and on the recalibration of model parameters in order to reflect pandemic-related trends in trip generation and trip distribution. The recalibration and validation of the model were based on data from the first wave of the pandemic in Hungary. Validation results, although limited, suggest that the traditional four-step models are able to capture the impacts on transportation of the atypical scenario of a pandemic with relatively simple adjustments and few data requirements.