2025-09-03 –, B05-B06
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.
You'll see the hidden patterns that carsharing data reveals when contextualized with urban information. The outcomes visually demonstrate the impact of area use, traffic and weather conditions across the city through comprehensive data visualizations.
Building on existing research, the talk will present opportunities that arise from including user data in the equation and offer starting points for additional predictors.
This session is ideal for data scientists interested in urban analytics, transportation modeling, or real-world applications of predictive modeling in mobility systems.
None
Prerequisites:N/A
Abstract as a tweet (X) or toot (Mastodon):This talk presents an extensive data analysis of Berlin's carsharing market, exploring whether data scraping and machine learning can solve fleet rebalancing challenges through real spatiotemporal analysis of months of carsharing data, combining advanced ML techniques with compelling visualizations.
Florian is a multidisciplinary software engineer with a deep interest in human mobility and infrastructure - both physical and digital. His background in software engineering at CODE Berlin and digital design at IADE Lisbon brings a creative approach to technology and data visualization. Currently, he works as a mobile and backend engineer at TBO Digital.