{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/spatio-temporal-graph-convolutional-networks","title":"Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting","arxiv_id":"1709.04875","date":"2017-09-14","proceeding":null,"authors":["Bing Yu","Haoteng Yin","Zhanxing Zhu"],"abstract":"Timely accurate traffic forecast is crucial for urban traffic control and\nguidance. Due to the high nonlinearity and complexity of traffic flow,\ntraditional methods cannot satisfy the requirements of mid-and-long term\nprediction tasks and often neglect spatial and temporal dependencies. In this\npaper, we propose a novel deep learning framework, Spatio-Temporal Graph\nConvolutional Networks (STGCN), to tackle the time series prediction problem in\ntraffic domain. Instead of applying regular convolutional and recurrent units,\nwe formulate the problem on graphs and build the model with complete\nconvolutional structures, which enable much faster training speed with fewer\nparameters. Experiments show that our model STGCN effectively captures\ncomprehensive spatio-temporal correlations through modeling multi-scale traffic\nnetworks and consistently outperforms state-of-the-art baselines on various\nreal-world traffic datasets.","url_abs":"http://arxiv.org/abs/1709.04875v4","url_pdf":"http://arxiv.org/pdf/1709.04875v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"spatio-temporal-graph-convolutional-networks","repo_url":"https://github.com/VeritasYin/STGCN_IJCAI-18","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"spatio-temporal-graph-convolutional-networks","repo_url":"https://github.com/Aguin/STGCN-PyTorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"spatio-temporal-graph-convolutional-networks","repo_url":"https://github.com/hazdzz/STGCN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"spatio-temporal-graph-convolutional-networks","repo_url":"https://github.com/ldphenshuai/STGCN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mindspore","reach":{"status":"unanswered"}},{"paper_slug":"spatio-temporal-graph-convolutional-networks","repo_url":"https://github.com/octoberzzzzz/ml-based-tms-cav-survey","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"spatio-temporal-graph-convolutional-networks","repo_url":"https://github.com/zachysun/taxi_traffic_benchmark","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"spatio-temporal-graph-convolutional-networks","repo_url":"https://github.com/2jungeuni/stgcn-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-forecasting","task_name":"Time Series Forecasting"},{"task_slug":"time-series-prediction","task_name":"Time Series Prediction"},{"task_slug":"traffic-prediction","task_name":"Traffic Prediction"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/time-series-forecasting-on-pemsd7","task":"Time Series Forecasting","dataset":"PeMSD7","model":"STGCN(Cheb)","rank_in_archive_order":2,"of":7,"metrics":{"9 steps MAE":"3.57"},"uses_additional_data":false},{"leaderboard":"/sota/time-series-forecasting-on-pemsd7","task":"Time Series Forecasting","dataset":"PeMSD7","model":"STGCN(1st)","rank_in_archive_order":3,"of":7,"metrics":{"9 steps MAE":"3.79"},"uses_additional_data":false},{"leaderboard":"/sota/traffic-prediction-on-metr-la","task":"Traffic Prediction","dataset":"METR-LA","model":"STGCN","rank_in_archive_order":20,"of":20,"metrics":{"MAE @ 12 step":"4.45"},"uses_additional_data":false},{"leaderboard":"/sota/traffic-prediction-on-pems-m","task":"Traffic Prediction","dataset":"PeMS-M","model":"STGCN","rank_in_archive_order":3,"of":5,"metrics":{"MAE (60 min)":"4.02"},"uses_additional_data":false},{"leaderboard":"/sota/traffic-prediction-on-pems07","task":"Traffic Prediction","dataset":"PeMS07","model":"STGCN","rank_in_archive_order":16,"of":17,"metrics":{"MAE@1h":"25.38"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1709.04875","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1709.04875"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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