COVID-19 lockdown measures and travel behavior: The case of Thessaloniki, Greece

Travel Demand Prediction during COVID-19 Pandemic: Educational and Working Trips at the University of Padova

“…Consequently, in order to contain the spread of SARS-CoV-2, many governments implemented severe countermeasures [7]. These non-pharmaceutical interventions were country-specific [7][8][9], however, they globally aimed at limiting interpersonal interactions to reduce human-to-human transmission of the virus [10][11][12]. These measures included social distancing, quarantine for infected people, local lockdowns allowing only essential travel, closures of universities and schools, restrictions on commercial and recreational activities (e.g., non-essential stores, restaurants, theatres), remote working and online learning [1,8,9,[12][13][14][15].…”

“…These non-pharmaceutical interventions were country-specific [7][8][9], however, they globally aimed at limiting interpersonal interactions to reduce human-to-human transmission of the virus [10][11][12]. These measures included social distancing, quarantine for infected people, local lockdowns allowing only essential travel, closures of universities and schools, restrictions on commercial and recreational activities (e.g., non-essential stores, restaurants, theatres), remote working and online learning [1,8,9,[12][13][14][15]. In Italy, different response measures were enforced, depending on the trend of total registered infected cases; in particular, after the first death in this country on 23 February 2020, the Italian government locally applied strict interventions to reduce the spread of the virus [16], which were gradually expanded to the whole territory.…”

“…Countermeasures against the diffusion of COVID-19 had a significant impact on mobility [1,12,13,19] because of direct limitations or bans on transport services, forbidden trips, the closure of activities, as well as self-imposed individual restrictions [20]. Many authors reported significant changes in users' travel habits worldwide [7,8,21,22], with variations in the frequency of trips, travel purposes and modal split [12], which may have varied in accordance with country-specific factors [23,24] and socio-economic characteristics of citizens [25].…”

“…Countermeasures against the diffusion of COVID-19 had a significant impact on mobility [1,12,13,19] because of direct limitations or bans on transport services, forbidden trips, the closure of activities, as well as self-imposed individual restrictions [20]. Many authors reported significant changes in users' travel habits worldwide [7,8,21,22], with variations in the frequency of trips, travel purposes and modal split [12], which may have varied in accordance with country-specific factors [23,24] and socio-economic characteristics of citizens [25]. As regards European nations, comparing trip statistics before and after the pandemic: in Germany, about 60% of persons changed transport mode [14], in particular, the number of people performing monomodal trips rose from 68% to 83% [26]; in Greece, the average number of daily trips decreased by 50% [12]; in the Netherlands, about 80% of people reduced their out-of-home activities and the number of trips decreased by 55% compared to 2019 [9]; in Poland, 50% of people reduced their travel time by more than two thirds [20]; commuting trips dropped by 80% in Spain [13]; in Italy, the number of people reporting no daily trips increased by 27% [24] and the number of internal trips decreased by 50% [27,28], while commuting trips were reduced by 69% [8]; even in Sweden, where restrictions were voluntary, 86% of the population changed their commuting trips with 66% of people who commuted for 5 days a week prior to the pandemic deciding not to travel [29].…”

“…Irawan et al [46] developed a structural equation model to analyse variations in activity and travel behaviour after the COVID-19 outbreak in Indonesia. Politis et al [12,25] applied a generalized linear model and ordinary-least squares regressions to predict trip frequency and duration in Greece. Scorrano and Danielis [17] modelled mode choice before and during the COVID-19 pandemic using revealed-preferences and stated-preferences data from an Italian city.…”