A data-driven framework for the safe integration of micro-mobility into the transport system: Comparing bicycles and e-scooters in field trials

Abstract

Introduction: Recent advances in technology create new opportunities for micro-mobility solutions even as they pose new challenges to transport safety. For instance, in the last few years, e-scooters have become increasingly popular in several cities worldwide; however, in many cases, the municipalities were simply unprepared for the new competition for urban space between traditional road users and e-scooters, so that bans became a necessary, albeit drastic, solution. In many countries, traditional vehicles (such as bicycles) may not be intrinsically safer than e-scooters but are considered less of a safety threat, possibly because-for cyclists-social norms, traffic regulations, and access to infrastructure are established, reducing the number of negative stakeholders. Understanding e-scooter kinematics and e-scooterist behavior may help resolve conflicts among road users, by favoring a data-driven integration of these new e-vehicles into the transport system. In fact, regulations and solutions supported by data are more likely to be acceptable and effective for all stakeholders. As new personal-mobility solutions enter the market, e-scooters may just be the beginning of a micro-mobility revolution.

Method: This paper introduces a framework (including planning, execution, analysis, and modeling) for a data-driven evaluation of micro-mobility vehicles. The framework leverages our experience assessing bicycle dynamics in real traffic to make objective and subjective comparisons across different micro-mobility solutions. In this paper, we use the framework to compare bicycles and e-scooters in field tests.

Results: The preliminary results show that e-scooters may be more maneuverable and comfortable than bicycles, although the former require longer braking distances.

Practical applications: Data collected from e-scooters may, in the short term, facilitate policy making, geo-fencing solutions, and education; in the long run, the same data will promote the integration of e-scooters into a cooperative transport system in which connected automated vehicles share the urban space with micro-mobility vehicles. Finally, the framework and the models presented in this paper may serve as a reference for the future assessment of new micro-mobility vehicles and their users' behavior (although advances in technology and novel micro-mobility solutions will inevitably require some adjustments).

Keywords: Automated connected vehicles; Electric vehicles; Intelligent transport system; Micro-mobility; Traffic safety; Vehicle classification.