Do People Desire to Cycle More During the COVID-19 Pandemic? Investigating the Role of Behavioural Characteristics through a Structural Model

Abstract

Background: Most cycling behaviour studies have defined it using objective variables and focused on normal conditions. Objective: This study applies latent class analysis to a sample of 375 survey respondents in Tehran, the Capital city of Iran, exploring the variables influencing cycling behaviour during pandemic covid-19. Methods: We made a statistical comparison among the data obtained from the questionnaires and the statistical data of the 2016 census. A structural equation modeling (SEM) was developed. Results: Fourteen indicators define three latent variables. Cycling behaviour is defined by these three latent factors and three indicators. This paper goes through each of the indicators and their impact on latent variables. The findings show that latent factors have a direct impact on cycling behaviour. Conclusion: Structural equation modeling (SEM) is a great tool for defining cyclist behaviour analysis that shows the positive and negative influence of variables on cycling rate during a covid-19 pandemic. There are some limitations in the area of this study in developing countries discussed in the paper.