{"type":"ScholarlyArticle","title":"Balancing Imbalance: Data-Scarce Urban Flow Prediction via Spatio-Temporal Balanced Transfer Learning","authors":[{"name":"Xinyan Hao","slug":"xinyan-hao","given":"Xinyan","family":"Hao","affiliation":""},{"name":"Huaiyu Wan","slug":"huaiyu-wan","given":"Huaiyu","family":"Wan","affiliation":""},{"name":"Shengnan Guo","slug":"shengnan-guo","given":"Shengnan","family":"Guo","affiliation":""},{"name":"Youfang Lin","slug":"youfang-lin","given":"Youfang","family":"Lin","affiliation":""}],"abstract":"Advanced deep spatio-temporal networks have become the mainstream for traffic prediction, but the widespread adoption of these models is impeded by the prevalent scarcity of available data. Despite cross-city transfer learning emerging as a common strategy to address this issue, it overlooks the inherent distribution imbalances within each city, which could potentially hinder the generalization capabilities of pre-trained models. To overcome this limitation, we propose a Spatio-Temporal Balanced Transfer Learning (STBaT) framework to enhance existing spatio-temporal prediction networks, ensuring both universality and precision in predictions for new urban environments. A Regional Imbalance Acquisition Module is designed to model the regional imbalances of source cities. Besides, to promote generalizable knowledge acquisition, a Spatio-Temporal Balanced Learning Module is devised to balance the predictive learning process. Extensive experiments on real-world datasets validate the efficacy of our proposed approach compared with several state-of-the-art methods.","doi":"10.24963/ijcai.2025/319","url":"https://ijcai.org/proceedings/2025/319","pdf":"https://ijcai.org/proceedings/2025/0319.pdf","markdown":"https://ijcai.org/proceedings/2025/319.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/319","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Data Mining","track":"Main Track","number":319,"pages":{"first":2865,"last":2873},"keywords":["Data Mining: DM: Mining spatial and/or temporal data"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}