Free-floating carsharing systems struggle to balance vehicle supply and demand, which often results in inefficient fleet distribution and reduced vehicle utilization. This talk explores how data scraping can be used to model vehicle demand and user behavior, enabling targeted incentives to encourage self-balancing vehicle flows.

Using information scraped from a major mobility provider over multiple months, the presentation provides spatiotemporal analyses and machine learning results to determine whether it's practically possible to offer low-friction discounts that lead to improved fleet balance.