On the performance of shared autonomous bicycles: A simulation study

Abstract

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.