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As the demands of multi-scale traffic analysis increase, on-demand interactions and visualizations are expected to be available for transportation participants. We have designed a multi-scale traffic generation system, namely DynaGraph, using a multi-agent framework to process multi-scale traffic data, conduct multi-scale traffic analysis, and present multi-scale visualization results. DynaGraph consists of three essential AI agents: 1) a text-to-demand agent with deep thinking ability to interact with users and extract prediction tasks through texts or voice; 2) a traffic prediction agent that leverages multi-scale traffic data to generate temporal features and similarity, and fuse them with limited spatial features and similarity, to achieve accurate prediction of three tasks; and 3) a suggestion and visualization agent that uses the prediction results to generate suggestions and visualizations, providing users with a comprehensive understanding of traffic conditions. Our DynaGraph as a generic system focuses on addressing concerns about traffic prediction from transportation participants, and conducted extensive experiments on five real-world road datasets to demonstrate its competitive prediction accuracy, scalability, and superior interactive performance.<\/jats:p>","DOI":"10.1145\/3749542","type":"journal-article","created":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T17:15:45Z","timestamp":1756919745000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["DynaGraph: Towards Dynamic Graph Learning for Multi-scale Traffic Generation with Spatial-temporal Agent Framework"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9805-3623","authenticated-orcid":false,"given":"Jinhui","family":"Ouyang","sequence":"first","affiliation":[{"name":"Data Intelligence and Service Collaboration (DISCO) Lab, Hunan University, Changsha, Hunan, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9254-5970","authenticated-orcid":false,"given":"Yijie","family":"Zhu","sequence":"additional","affiliation":[{"name":"Data Intelligence and Service Collaboration (DISCO) Lab, Hunan University, Changsha, Hunan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2091-5163","authenticated-orcid":false,"given":"Hanhui","family":"Deng","sequence":"additional","affiliation":[{"name":"Data Intelligence and Service Collaboration (DISCO) Lab, Hunan University, Changsha, Hunan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0997-558X","authenticated-orcid":false,"given":"Jialyu","family":"He","sequence":"additional","affiliation":[{"name":"Hunan Geospatial Information Engineering and Technology Research Center, The Third Surveying and Mapping Institute of Hunan Province, Changsha, Hunan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8697-1817","authenticated-orcid":false,"given":"Di","family":"Wu","sequence":"additional","affiliation":[{"name":"Data Intelligence and Service Collaboration (DISCO) Lab, Hunan University, Changsha, Hunan, China"}]}],"member":"320","published-online":{"date-parts":[[2025,9,3]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534573"},{"key":"e_1_2_1_2_1","volume-title":"Traffic Flow Prediction Using Graph Convolution Neural Networks. 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