In this paper, we analyse the specific behaviour of passengers in personal transport commuting to work or school during the COVID-19 pandemic, based on a sample of respondents from two countries. We classified the commuters based on a two-step cluster analysis into groups showing the same characteristics. Data were obtained from an online survey, and the total sample size consists of 2000 respondents. We used five input variables, dividing the total sample into five clusters using a two-step cluster analysis. We observed significant differences between gender, status, and car ownership when using public transport, cars, and other alternative means of transportation for commuting to work and school. We also examined differences between individual groups with the same socioeconomic and socio-demographic factors. In total, the respondents were classified into five clusters, and the results indicate that there are differences between gender and status. We found that ownership of a prepaid card for public transport and social status are the most important factors, as they reach a significance level of 100%, unlike compared to other factors with importance ranging from 60 to 80%. Moreover, the results demonstrate that prepaid cards are preferred mainly by female students. Understanding these factors can help in planning transport policy by knowing the habits of users.

Transport is an inseparable part of the life of all citizens. At the beginning of the year, the COVID-19 pandemic hit the world. Individual states have taken strict measures to prevent its spread among the population. Due to this fact, the government of the Slovak Republic has issued restrictions on the closure of public spaces (schools, shopping centres, restaurants, bars, etc.). These restrictions have had an impact not only on the economic activity of the population but also on their mobility in the form of reduced traffic. This is due to the drastically reduced mobility associated with the coronavirus, such as commuting trips and extremely limited leisure opportunities. Reduced mobility of the population (reduction of the number of vehicles in the traffic flow) can bring positive effects not only on overloaded road network (increased vehicle speed, lower flow) but also on the environment (reduction of noise, emissions, etc.). This article aims at finding out what effect the measures taken had on the quality of traffic flow. The quality of movement was examined in the form of the flow and speed of vehicles on one of the busiest first-class road sections. Descriptive statistics were used to examine the state of the restrictions. The results show that after the introduction of measures against the spread of coronavirus, the intensity and speed of vehicles in the measured section decreased.

The fashion industry is an integral part of people’s lives with different purchasing behaviors depending on age and education. In this paper, we focused on the perception of online shopping for second-hand clothes and shoes among Generation Z. This group has a lot of experience in using technology, so online shopping is a suitable way to buy second-hand clothes. The article aims to analyze the online shopping behavior of Gen Z, focusing on benefits, obstacles, environmental awareness, and preferred shopping platforms. We studied consumer preferences in shopping for second-hand clothes based on a sample consisting of 340 respondents representing Generation Z through an online Google Forms questionnaire published from November to January. The results show that price is the biggest advantage of shopping for second-hand clothes. Moreover, we found that the environmental aspect plays a significant role for all consumers, regardless of the online experience of shopping for second-hand clothes and shoes. The correspondence maps demonstrate that consumers with shopping experience planning a future purchase of second-hand clothing are aware of the environmental impact of purchasing clothing together with potential buyers of second-hand clothing, unlike consumers without any interest in purchasing second-hand clothing. These consumers and potential buyers of second-hand clothing consider environmental protection and high-quality, diverse (unique) clothing to be key benefits, in contrast to a smaller selection and used clothing. These conclusions provide a theoretical basis for understanding the sustainability and shopping requirements of the selected group.

The surveyof traffic intensity is used to obtain information on the number of vehicles on roads during the day. Subsequently, it is possible to derive from this the daily, weekly, and other road traffic intensity information. This survey represents the basis for the calculation of the annual average daily traffic volume and the basic characteristics of traffic flow. The COVID-19 pandemic has caused extensive economic and social damage around the world. These damages have also affected traffic. Changes in traffic behavior have mainly affected the reduction in traffic intensity on road networks. Thanks to the reduction in the demand for transport, there has also been a significant reduction in traffic delays, fuel consumption and emissions. An examination of changes in traffic intensity took place at a selected intersection in 2019, 2020 and 2021. This paper describes the effects of reducing the traffic intensity, fuel consumption and emissions obtained by microsimulation. The results obtained confirmed the reduction in traffic, which also contributed to a significant reduction in vehicle delays.

At the beginning of 2020 there was a spinning point in the travel behavior of people around the world because of the pandemic and its consequences. This paper analyzes the specific behavior of travelers commuting to work or school during the COVID-19 pandemic based on a sample of 2000 respondents from two countries. We obtained data from an online survey, applying multinomial regression analysis. The results demonstrate the multinomial model with an accuracy of almost 70% that estimates the most used modes of transport (walking, public transport, car) based on independent variables. The respondents preferred the car as the most frequently used means of transport. However, commuters without car prefer public transport to walking. This prediction model could be a tool for planning and creating transport policy, especially in exceptional cases such as the limitation of public transport activities. Therefore, predicting travel behavior is essential for policymaking based on people’s travel needs.