On-demand shuttle services offer an innovative and sustainable transportation solution, but routing requires near real-time decision-making under various constraints. We present a reinforcement learning approach that learns to fulfill transportation requests in reduced waiting time by integrating predictive demand into positioning of the shuttles. The method was used by the Deutsche Bahn to route autonomous on-demand shuttles in a German town and demonstrates the potential to reduce travel time and traffic and to improve accessibility for impaired passengers.