On the adoption of e-moped sharing systems

Abstract

Recent years have witnessed the emerging of novel shared mobility solutions that provide diffused on-demand access to transportation. The widespread adoption of these solutions, particularly electric mopeds (e-mopeds), is expected to bring important benefits such as the reduction of noise and atmospheric pollution, and road congestion, with extensive repercussions on liveability and quality of life in urban areas. Currently, almost no effort has been devoted to exploring the adoption patterns of e-moped sharing services, therefore, optimal management and allocation of vehicles appears to be a problem for service managers. In this study, we tried to demonstrate the validity of the hypothesis that the adoption of electric mopeds depends on the built environment and demographic aspects of each neighbourhood. In detail, we singled out three features concerning the area characteristics (distance from centre, walkability, concentration of places) and one about the population (education index). The results obtained on a real world case study show the strong impact these factors have in determining the adoption of e-moped sharing services. Finally, an analysis was conducted on the possible role that the electric moped sharing can play in social equalization by studying the interactions between rich and poor neighbourhoods. The results of the analyses conducted indicate that communities within a city tend to aggregate by wealth and isolate themselves from one another (social isolation): very few interactions, in terms of trajectories, have been observed between the richest and poorest areas of the city under study.

“I want to ride my bicycle”: analysing shared mobility in Italy

“…Moreover, given the position of the available e-scooter, more work was done to develop a better and safer cycling infrastructure (Folco et al, 2022). Other studies have focused on social equalization by studying the interactions between rich and poor neighbourhoods (Fiorini et al, 2022). Mobility data is also used to investigate transport behaviour.…”

On the path to develop a micromobility journey planner for Madrid: A tool to estimate, visualize, and analyze cycling and other shared mobility services’ flow

“…Many studies delved into the topic of journey planners (Arbeláez Vélez, 2023;Broach et al, 2012;Chen et al, 2023;Fiorini et al, 2022;Hochmair, 2005;Hoobroeckx et al, 2023;Hrncir et al, 2014;Liu et al, 2022;Scott et al, 2021;Su et al, 2010;Tal et al, 2013;Tscharaktschiew & Müller, 2021;Turverey et al, 2010;Wortmann et al, 2021;Zhang et al, 2021; (see Table 1), but only a few studies relate to the proposition of innovative journey planners that offer shared micromobility options. One of these studies is (Georgakis et al, 2020), which proposed a MaaS (Mobility as a Service) journey planner, but apart from traditional modes (private car and public transport) it only considers bike-sharing, ride-hailing and car-sharing, not taking into account other shared modes like mopeds or scooters.…”