{"type":"ScholarlyArticle","title":"FOGS: First-Order Gradient Supervision with Learning-based Graph for Traffic Flow Forecasting","authors":[{"name":"Xuan Rao","slug":"xuan-rao","given":"Xuan","family":"Rao","affiliation":"University of Electronic Science and Technology of China , Chengdu , China"},{"name":"Hao Wang","slug":"hao-wang","given":"Hao","family":"Wang","affiliation":"Wuhan University , China"},{"name":"Liang Zhang","slug":"liang-zhang","given":"Liang","family":"Zhang","affiliation":"King Abdullah University of Science and Technology , Saudi Arabia"},{"name":"Jing Li","slug":"jing-li","given":"Jing","family":"Li","affiliation":"Inception Institute of Artificial Intelligence , Abu Dhabi , United Arab Emirates"},{"name":"Shuo Shang","slug":"shuo-shang","given":"Shuo","family":"Shang","affiliation":"University of Electronic Science and Technology of China , Chengdu , China"},{"name":"Peng Han","slug":"peng-han","given":"Peng","family":"Han","affiliation":"Aalborg University , Denmark"}],"abstract":"Traffic flow forecasting plays a vital role in the transportation domain. \r\nExisting studies usually manually construct correlation graphs and design sophisticated models for learning spatial and temporal features to predict future traffic states.\r\nHowever, manually constructed correlation graphs cannot accurately extract the complex patterns hidden in the traffic data. \r\nIn addition, it is challenging for the prediction model to fit traffic data due to its irregularly-shaped distribution.\r\nTo solve the above-mentioned problems, in this paper, we propose a novel learning-based method to learn a spatial-temporal correlation graph, which could make good use of the traffic flow data. \r\nMoreover, we propose First-Order Gradient Supervision (FOGS), a novel method for traffic flow forecasting. \r\nFOGS utilizes first-order gradients, rather than specific flows, to train prediction model, which effectively avoids the problem of fitting irregularly-shaped distributions. Comprehensive numerical evaluations on four real-world datasets reveal that the proposed methods achieve state-of-the-art performance and significantly outperform the benchmarks.","doi":"10.24963/ijcai.2022/545","url":"https://ijcai.org/proceedings/2022/545","pdf":"https://ijcai.org/proceedings/2022/0545.pdf","markdown":"https://ijcai.org/proceedings/2022/545.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/545","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":545,"pages":{"first":3926,"last":3932},"keywords":["Data Mining: Mining Spatial and/or Temporal Data","Multidisciplinary Topics and Applications: Transportation"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986737","length_sec":81,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984880","length_sec":294,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Bai et al., 2019] Lei Bai, Lina Yao, Salil S. 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