As society faces global challenges such as population growth and climate change, rethinking cities is now more imperative than ever. The design of cities can not be abstracted from the design of their mobility systems. Therefore, efficient solutions must be found to transport people and goods throughout the city efficiently and ecologically. An autonomous bicycle-sharing system would combine the most relevant benefits of vehicle-sharing, autonomy, and micro-mobility, increasing the efficiency and convenience of bicycle-sharing systems and incentivizing more people to bike and enjoy their cities in an environmentally friendly way. Due to the novelty of introducing autonomous driving technology into bicycle-sharing systems and their inherent complexity, there is a need to quantify the potential impact of autonomy on fleet performance and user experience. This paper presents the results of an agent-based simulation that provides an in-depth understanding of the fleet behavior of autonomous bicycle-sharing systems in realistic scenarios, including a rebalancing system based on demand prediction. In addition, this work describes the impact of different parameters on system efficiency and service quality. Finally, it quantifies the extent to which an autonomous system would outperform current station-based and dockless bicycle-sharing schemes. The obtained results show that with a fleet size three and a half times smaller than a station-based system and eight times smaller than a dockless system, an autonomous system can improve overall performance and user experience even with no rebalancing.

Fast urbanization and climate change require innovative systems for an efficient movement of people and goods in cities. As trends towards vehicle-sharing, autonomous vehicles, and the use of micro-mobility systems gain strength, the intersection of these fields appears as an area of great opportunity. Autonomy could potentially bring the convenience of on-demand mobility into already prevalent shared micro-mobility systems (SMMS), increasing their efficiency and incentivizing more people to use active mobility modes. The novelty of introducing autonomous driving technology into SMMS and their inherent complexity requires tools to assess and quantify the potential impact of autonomy on fleet performance and user experience. This paper presents an ad-hoc agent-based simulator for the assessment of the fleet behavior of autonomous SMMS in realistic scenarios, including a rebalancing system based on demand prediction. It also allows comparing its performance to station-based and dockless schemes. The proposed simulation framework is highly configurable and flexible and works with high resolution and precision geospatial data. The results of studies carried out with this simulation tool could provide valuable insights for many stakeholders, including vehicle design engineers, fleet operators, city planners, and governments.