{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T15:39:36Z","timestamp":1781710776100,"version":"3.54.5"},"reference-count":51,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T00:00:00Z","timestamp":1768694400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guizhou Provincial Science and Technology Fund"},{"name":"Fund of the State Key Laboratory of Public Big Data, Guizhou University","award":["PBD2023-35"],"award-info":[{"award-number":["PBD2023-35"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Accurate traffic flow prediction is a core component of intelligent transportation systems, supporting proactive traffic management, resource optimization, and sustainable urban mobility. However, urban traffic networks exhibit heterogeneous multi-scale periodic patterns and time-varying spatial interactions among road segments, which are not sufficiently captured by many existing spatio-temporal forecasting models. To address this limitation, this paper proposes PDR-STGCN (Periodicity-Aware Dynamic Relational Spatio-Temporal Graph Convolutional Network), an enhanced STGCN framework that jointly models multi-scale periodicity and dynamically evolving spatial dependencies for traffic flow prediction. Specifically, a periodicity-aware embedding module is designed to capture heterogeneous temporal cycles (e.g., daily and weekly patterns) and emphasize dominant social rhythms in traffic systems. In addition, a dynamic relational graph construction module adaptively learns time-varying spatial interactions among road nodes, enabling the model to reflect evolving traffic states. Spatio-temporal feature fusion and prediction are achieved through an attention-based Bidirectional Long Short-Term Memory (BiLSTM) network integrated with graph convolution operations. Extensive experiments are conducted on three datasets, including Metro Traffic Los Angeles (METR-LA), Performance Measurement System Bay Area (PEMS-BAY), and a real-world traffic dataset from Guizhou, China. Experimental results demonstrate that PDR-STGCN consistently outperforms state-of-the-art baseline models. For next-hour traffic forecasting, the proposed model achieves average reductions of 16.50% in RMSE, 9.00% in MAE, and 0.34% in MAPE compared with the second-best baseline. Beyond improved prediction accuracy, PDR-STGCN reveals latent spatio-temporal evolution patterns and dynamic interaction mechanisms, providing interpretable insights for traffic system analysis, simulation, and AI-driven decision-making in urban transportation networks.<\/jats:p>","DOI":"10.3390\/systems14010102","type":"journal-article","created":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T08:23:56Z","timestamp":1768811036000},"page":"102","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["PDR-STGCN: An Enhanced STGCN with Multi-Scale Periodic Fusion and a Dynamic Relational Graph for Traffic Forecasting"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3893-1594","authenticated-orcid":false,"given":"Jie","family":"Hu","sequence":"first","affiliation":[{"name":"College of Big Data Statistics, Guizhou University of Finance and Economics, Guiyang 550025, China"},{"name":"State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bingbing","family":"Tang","sequence":"additional","affiliation":[{"name":"College of Big Data Statistics, Guizhou University of Finance and Economics, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Langsha","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Big Data Statistics, Guizhou University of Finance and Economics, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiting","family":"Li","sequence":"additional","affiliation":[{"name":"College of Big Data Statistics, Guizhou University of Finance and Economics, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8725-6660","authenticated-orcid":false,"given":"Jianjun","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8761-5195","authenticated-orcid":false,"given":"Guanci","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Manufacturing Technology of Ministry of Education, Guizhou University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,18]]},"reference":[{"key":"ref_1","unstructured":"Lan, S., Ma, Y., Huang, W., Wang, W., Yang, H., and Li, P. 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