{"id":41089,"date":"2026-09-17T11:20:54","date_gmt":"2026-09-17T11:20:54","guid":{"rendered":"https:\/\/lamarr-institute.org\/publication\/direct-deep-reinforcement-learning-for-tourist-route-generation\/"},"modified":"2026-09-17T11:20:54","modified_gmt":"2026-09-17T11:20:54","slug":"direct-deep-reinforcement-learning-for-tourist-route-generation","status":"publish","type":"publication","link":"https:\/\/lamarr-institute.org\/de\/publication\/direct-deep-reinforcement-learning-for-tourist-route-generation\/","title":{"rendered":"DIRECT: Deep Reinforcement Learning for Tourist Route Generation"},"content":{"rendered":"<p>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.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>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 [&hellip;]<\/p>\n","protected":false},"author":14,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[30],"class_list":["post-41089","publication","type-publication","status-publish","hentry","publication-type-article"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/41089","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication"}],"about":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/types\/publication"}],"author":[{"embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/users\/14"}],"version-history":[{"count":0,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/41089\/revisions"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=41089"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=41089"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}