{"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/bilinear-spatiotemporal-fusion-network-an","title":"Bilinear Spatiotemporal Fusion Network: An efficient approach for traffic flow prediction","arxiv_id":null,"date":"2025-04-01","proceeding":"Neural Networks 2025 4","authors":["Jing Chen","Shixiang Pan","Weimin Peng","Wenqiang Xu"],"abstract":"Accurate traffic flow forecasting is critical for intelligent transportation systems, yet increasing model complexity in spatiotemporal graph neural networks does not always yield proportional gains. In this paper, we present a Bilinear Spatiotemporal Fusion Network (BLSTF) tailored for stable, periodic traffic scenarios. First, a temporal enhancement module is introduced to mitigate multi-step error accumulation. Second, predefined graph priors with linear feedback leverage known road topologies for straightforward yet effective spatial modeling. Finally, a bilinear fusion mechanism seamlessly integrates refined temporal and spatial features with minimal computational overhead. Extensive experiments on four real-world datasets show that BLSTF outperforms state-of-the-art methods, achieving MAE and MAPE of 14.05 and 13.90% on PEMS03, 17.93 and 12.12% on PEMS04, 18.87 and 7.86% on PEMS07, and 13.49 and 8.71% on PEMS08, demonstrating BLSTF’s potential to deliver accurate, efficient, and interpretable traffic flow forecasts.","url_abs":"https://www.sciencedirect.com/science/article/pii/S0893608025002618?via%3Dihub","url_pdf":"https://www.sciencedirect.com/science/article/pii/S0893608025002618?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":"bilinear-spatiotemporal-fusion-network-an","repo_url":"https://github.com/psx1999/BLSTF","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"spatio-temporal-forecasting","task_name":"Spatio-Temporal Forecasting"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"traffic-prediction","task_name":"Traffic Prediction"}],"methods":[{"method_slug":"mae","method_name":"MAE"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}