Learning-Based Routing for Autonomous Shuttles Under Stochastic Demand Using Generative Adversarial Imitation Learning and Reinforcement Learning

Extensive research has been conducted to develop technologies that enable paratransit systems to operate autonomously, including advanced sensing technologies and associated software. However, there remains a gap in research addressing adaptive operational algorithms for such systems under stochastic and dynamically evolving demand. To address this gap, this study develops an imitation-learning-assisted deep reinforcement learning (DRL) approach for autonomous shuttle routing. The proposed framework integrates generative adversarial imitation learning with proximal policy optimization to enable sequential pickup and drop-off decision-making under stochastic passenger demand without centralized re-optimization. The DRL agent was trained over approximately 1.5 million training steps and evaluated across 1000 episodes with stochastic passenger generation. Its performance was benchmarked against a deterministic dial-a-ride problem (DARP) solver implemented using Google’s OR-Tools, as well as online heuristic baselines. Results indicate that while heuristic methods achieve lower average time-based performance metrics, the proposed approach is capable of learning adaptive routing policies and demonstrates consistent behavior across diverse demand realizations. These findings highlight the feasibility of learning-based routing in controlled environments and provide a foundation for extending such approaches to more complex and realistic autonomous mobility systems.