Optimizing Shared Micro-Mobility Services: Edge-Enabled Rebalance for Dock-Based Systems

The user experience is an important aspect of micro-mobility fleet operations, and placing micro-vehicles in a suitable and optimized manner is a key element to enhancing user service. This paper aims to establish an effective methodology for optimizing shared micro-mobility rebalance operations through spatio-temporal prediction of user demand in dock-based systems. It is based on forecasting the occupancy levels of each station to avoid completely empty and jammed stations in the future, ensuring service availability throughout the day. Since the processed data is local, we employ edge computing, yielding a scalable solution that minimizes latency and enhances reliability, making it suitable for urban environments with fluctuating conditions. The results demonstrate significant improvements in service availability, validating the efficiency of our edge-adapted prediction model for dock-based micro-mobility fleets.