DIRECT: Deep Reinforcement Learning for Tourist Route Generation

Abstract

Effective tourist route generation can substantially enhance visitor experiences in urban areas by offering a diverse set of relevant route options while promoting the efficient use of urban infrastructure. However, the generation of tourist routes poses substantial challenges, primarily due to a complex set of often conflicting optimization objectives, and the scarcity of historical user trajectories for training data-driven models. In this article, we propose DIRECT, a geospatially informed novel deep reinforcement learning-based approach for tourist route generation. DIRECT dynamically generates a diverse set of high-quality route alternatives, while respecting user-specified spatio-temporal constraints and category preferences. Crucially, DIRECT does not require any historical user trajectories; instead, it leverages publicly available point-of-interest (POI) data and road network information. The results of our extensive evaluation demonstrate that DIRECT outperforms baseline methods in generating diverse tourist routes.