{"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/fastersts-a-faster-spatio-temporal","title":"FasterSTS: A Faster Spatio-Temporal Synchronous Graph Convolutional Networks for Traffic flow Forecasting","arxiv_id":"2501.00756","date":"2025-01-01","proceeding":null,"authors":["Ben-Ao Dai","Nengchao Lyu","Yongchao Miao"],"abstract":"Accurate traffic flow prediction heavily relies on the spatio-temporal correlation of traffic flow data. Most current studies separately capture correlations in spatial and temporal dimensions, making it difficult to capture complex spatio-temporal heterogeneity, and often at the expense of increasing model complexity to improve prediction accuracy. Although there have been groundbreaking attempts in the field of spatio-temporal synchronous modeling, significant limitations remain in terms of performance and complexity control.This study proposes a quicker and more effective spatio-temporal synchronous traffic flow forecast model to address these issues.","url_abs":"https://arxiv.org/abs/2501.00756v1","url_pdf":"https://arxiv.org/pdf/2501.00756v1.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":"fastersts-a-faster-spatio-temporal","repo_url":"https://github.com/Salabh2k2/Awesome-Project-Collection/blob/main/Traffic_flow_Prediction_using_LSTM%2BFasterSTGCN.ipynb","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"traffic-prediction","task_name":"Traffic Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/traffic-prediction-on-pems04","task":"Traffic Prediction","dataset":"PeMS04","model":"FasterSTS","rank_in_archive_order":9,"of":12,"metrics":{"12 Steps MAE":"18.49"},"uses_additional_data":false},{"leaderboard":"/sota/traffic-prediction-on-pems08","task":"Traffic Prediction","dataset":"PeMS08","model":"FasterSTS","rank_in_archive_order":10,"of":13,"metrics":{"MAE@1h":"13.60"},"uses_additional_data":false},{"leaderboard":"/sota/traffic-prediction-on-pemsd4","task":"Traffic Prediction","dataset":"PeMSD4","model":"FasterSTS","rank_in_archive_order":9,"of":13,"metrics":{"12 steps MAE":"18.49"},"uses_additional_data":false},{"leaderboard":"/sota/traffic-prediction-on-pemsd8","task":"Traffic Prediction","dataset":"PeMSD8","model":"FasterSTS","rank_in_archive_order":9,"of":13,"metrics":{"12 steps MAE":"13.60","MAE@1h":"13.60"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}