Mobility chameleons: The current and potential users of shared micromobility

Bike and e-scooter sharing schemes are an emerging addition to modern urban mobility systems. The research aims to identify the mobility and sociodemographic user profile of bike and e-scooter sharing and understand the key determinants of people’s intention to use these modes in the future. To consider the effect of local context on shared micromobility usage, the study exploited survey data (N = 1607) that were collected in three European urban areas (Brussels, Munich, and Vienna). The obtained dataset provided information on respondents’ so
cioeconomic characteristics, mobility capabilities and preferences, including travel frequency by bike and e-scooter sharing services. The survey also collected information on respondents’ likelihood of travelling by shared micromobility in the future. The profile of current users was revealed by conducting a Latent Class Analysis (LCA). The measurement and membership LCA models were estimated based on the mobility and sociodemographic data, respectively. The three-class LCA model uncovered that shared micromobility users are mostly members of the so-called “Mobility chameleons” population group whereas few users belong to the “Car lovers” and the “Public transport fans” classes. Mobility chameleons travel by private, public, and shared modes, as well as by both motorized and active modes. Males, with medium and high income, who own a smartphone, dominate this group. Although mobility chameleons are present in all study areas, their share in the population
varies. Regarding potential future usage, people’s willingness to travel by shared micromobility in the future is much higher than the present usage levels captured. Modelling the potential demand revealed that while many factors similarly influence the likelihood of travelling by bike and e-scooter sharing, the significance and strength of the effects vary for the two modes. The present research findings provide relevant input for modelling as well as predicting shared micromobility usage.