{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:54:56Z","timestamp":1773802496360,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"18","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>As urban data expands, existing spatio-temporal models encounter challenges such as high context dependency, poor cross-scenario generalization, and inefficient computational performance. To address these issues, we propose UrbanPG, an efficient and scalable framework for spatio-temporal learning. UrbanPG separates task-specific personalized patterns from general patterns, enabling unified spatio-temporal modeling and efficient knowledge generalization across scenarios. The key innovations of UrbanPG include: the development of a lightweight, context-independent general backbone utilizing linear spatio-temporal attention for scalable cross-scenario deployment; a personalized context prompt mechanism designed to model heterogeneity through spatio-temporal embeddings and random perturbation regularization, interacting with the backbone to enhance pattern differentiation; the proposal of multi spatio-temporal learning paradigms for rapid knowledge transfer and generalization to downstream tasks through fine-tuning personalized context prompts while freezing the backbone. Experimental results demonstrate that UrbanPG achieves state-of-the-art performance in large-scale forecasting, few-shot transfer, and continual learning tasks across eight real-world datasets, showcasing exceptional performance, strong generalization, and significant reductions in computational overhead.<\/jats:p>","DOI":"10.1609\/aaai.v40i18.38554","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:38:27Z","timestamp":1773794307000},"page":"15287-15295","source":"Crossref","is-referenced-by-count":0,"title":["UrbanPG: An Efficient Framework with Personalized Context and General Backbone Interaction for Urban Spatio-Temporal Learning"],"prefix":"10.1609","volume":"40","author":[{"given":"Aoyu","family":"Liu","sequence":"first","affiliation":[]},{"given":"Yaying","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38554\/42516","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38554\/42516","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:38:27Z","timestamp":1773794307000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/38554"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i18.38554","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}