{"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/pdg2seq-periodic-dynamic-graph-to-sequence","title":"PDG2Seq: Periodic Dynamic Graph to Sequence Model for Traffic Flow Prediction","arxiv_id":null,"date":"2024-12-05","proceeding":"Neural Networks 2024 12","authors":["Jin Fan","Weng","Wenchao","Qikai Chen","Huifeng Wu","Jia Wu"],"abstract":"Traffic flow prediction is the foundation of intelligent traffic management systems. Current methods prioritize the development of intricate models to capture spatio-temporal correlations, yet they often neglect the exploitation of latent features within traffic flow. Firstly, the correlation among different road nodes exhibits dynamism rather than remaining static. Secondly, traffic data exhibits evident periodicity, yet current research lacks the exploration and utilization of periodic features. Lastly, current models typically rely solely on historical data for modeling, resulting in the limitation of accurately capturing future trend changes in traffic flow. To address these findings, this paper proposes a Periodic Dynamic Graph to Sequence Model (PDG2Seq) for traffic flow prediction. PDG2Seq consists of the Periodic Feature Selection Module (PFSM) and the Periodic Dynamic Graph Convolutional Gated Recurrent Unit (PDCGRU) to further extract the spatio-temporal features of the dynamic real-time traffic. The PFSM extracts learned periodic features using time points as indices, while the PDCGRU leverages the extracted periodic features from the PFSM and dynamic features from traffic flow to generate a Periodic Dynamic Graph for extracting spatio-temporal features. In the decoding phase, PDG2Seq utilizes periodic features corresponding to the prediction target to capture future trend changes, leading to more accurate predictions. Comprehensive experiments conducted on four large-scale datasets substantiate the superiority of PDG2Seq over existing state-of-the-art baselines. Related codes are available at https://github.com/wengwenchao123/PDG2Seq.","url_abs":"https://www.sciencedirect.com/science/article/pii/S0893608024008700?via%3Dihub","url_pdf":"https://www.sciencedirect.com/science/article/pii/S0893608024008700?via%3Dihub","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":"pdg2seq-periodic-dynamic-graph-to-sequence","repo_url":"https://github.com/wengwenchao123/PDG2Seq","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"graph-to-sequence","task_name":"Graph-to-Sequence"},{"task_slug":"traffic-prediction","task_name":"Traffic Prediction"},{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[{"method_slug":"feature-selection","method_name":"Feature Selection"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/traffic-prediction-on-pems04","task":"Traffic Prediction","dataset":"PeMS04","model":"PDG2Seq","rank_in_archive_order":6,"of":12,"metrics":{"12 Steps MAE":"18.24"},"uses_additional_data":false},{"leaderboard":"/sota/traffic-prediction-on-pems07","task":"Traffic Prediction","dataset":"PeMS07","model":"PDG2Seq","rank_in_archive_order":4,"of":17,"metrics":{"MAE@1h":"19.28"},"uses_additional_data":false},{"leaderboard":"/sota/traffic-prediction-on-pems08","task":"Traffic Prediction","dataset":"PeMS08","model":"PDG2Seq","rank_in_archive_order":9,"of":13,"metrics":{"MAE@1h":"13.60"},"uses_additional_data":false},{"leaderboard":"/sota/traffic-prediction-on-pemsd3","task":"Traffic Prediction","dataset":"PeMSD3","model":"PDG2Seq","rank_in_archive_order":2,"of":6,"metrics":{"12 steps MAE":"14.62","12 steps MAPE":"14.88","12 steps RMSE":"25.47"},"uses_additional_data":false},{"leaderboard":"/sota/traffic-prediction-on-pemsd4","task":"Traffic Prediction","dataset":"PeMSD4","model":"PDG2Seq","rank_in_archive_order":4,"of":13,"metrics":{"12 steps MAE":"18.24","12 steps MAPE":"12.09","12 steps RMSE":"30.08"},"uses_additional_data":false},{"leaderboard":"/sota/traffic-prediction-on-pemsd8","task":"Traffic Prediction","dataset":"PeMSD8","model":"PDG2Seq","rank_in_archive_order":8,"of":13,"metrics":{"12 steps MAE":"13.60","12 steps MAPE":"8.99","12 steps RMSE":"23.37"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}