Adaptive Bus Scheduling

We are preparing a proposal for the DUT Horizon Europe (15-Minute City pathway) to test dynamic, demand-responsive bus timetables within a digital twin of Ingolstadt.

In short, we use partner data (e.g., passenger counts and GPS/GTFS) to forecast passenger flows and bus travel times, then generate daily timetables optimized with reinforcement learning. Our goals are to align capacity with real demand, reduce travel and waiting times, and integrate last-mile options (e-scooters, taxis).