In recent years, the shared scooter market has seen tremendous growth along with other micromobility industries as the future means of urban transport. One particularly interesting innovation that companies have begun experimenting with in this field is that of self-driving e-scooters.

This thesis presents a study on the benefits of an autonomous or teleoperated scooter fleet with self-assembly capabilities: the ability to cluster nearby scooters and reduce the number of locations for servicing. To this end, the application is tackled as two separate optimization problems in clustering and routing. The full algorithm pipeline is described and several metrics evaluated against independent variables and algorithm parameters using real-world GBFS scooter data collected over several months.

This thesis shows that self-assembly reduces total service times by as much as 50%, and can serve as a stepping stone for early adoption of the technology while more complex capabilities are being developed.