Abstract Summary
As evidenced by their rapid adoption in recent years, shared micro-mobility services have resonated with consumers as first and last-mile solutions by penetrating the traditional transportation framework. A recent upsurge in investments from tech industry competitors like Uber, Lyft, Ford, Alphabet, and other venture capital firms points to the likelihood of even more rapid growth in shared micromobility services in the future. Unlike the station-based bike-sharing, which gained steady traction by evolving over a dozen years, e-scooter as a micromobility mode has a recent phenomenon. Often cities are making instant decisions without the benefit of mode choice behaviour. The advent of e-scooters led to the near disappearance of dockless bikesharing system and rapid decline in trips using dock-based bikesharing. Regardless of their mixed reception from the public due to numerous safety concerns, e-scooters outperformed other micromobility services doubling the overall micromobility ridership to 84 million trips in just one year [1]. Very little is known about the adaptable behavior and mode-choice preferences of these growing micromobility users that are critical for policymaking, planning and operations. Several researchers have surveyed users, quantified demand-supply dynamics, modeled user behavior, developed predictive models, and documented noteworthy findings of station-based bikesharing services in the past few years [2]–[5]. These studies addressed user demographics and their mode-choice preferences and the spatial equity of service.
