{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T15:16:43Z","timestamp":1783783003340,"version":"3.55.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>Traffic data imputation is fundamentally important to support various applications in intelligent transportation systems such as traffic flow prediction. However, existing time-to-space sequential methods often fail to effectively extract features in block-wise missing data scenarios. Meanwhile, the static graph structure for spatial feature propagation significantly constrains the model's flexibility in handling the distribution shift issue for the nonstationary traffic data. To address these issues, this paper proposes a Spatio-Temporal Attention Mixture of experts network named STAMImputer for traffic data imputation. Specifically, we introduce a Mixture of Experts (MoE) framework to capture latent spatio-temporal features and their influence weights, effectively imputing block missing. A novel Low-rank guided Sampling Graph ATtention (LrSGAT) mechanism is designed to dynamically balance the local and global correlations across road networks. The sampled attention vectors are utilized to generate dynamic graphs that capture real-time spatial correlations. Extensive experiments are conducted on four traffic datasets for evaluation. The result shows STAMImputer achieves significantly performance improvement compared with existing SOTA approaches. Our codes are available at https:\/\/github.com\/RingBDStack\/STAMImupter.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/382","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"3435-3443","source":"Crossref","is-referenced-by-count":4,"title":["STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation"],"prefix":"10.24963","author":[{"given":"Yiming","family":"Wang","sequence":"first","affiliation":[{"name":"School of Cyber Science and Technology, Beihang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Technology, Beihang University"},{"name":"Hangzhou Innovation Institute of BUAA, Hangzhou, China"},{"name":"Department of Computer Science and Technology, Shantou University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Senzhang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haohua","family":"Du","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Technology, Beihang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunyang","family":"Liu","sequence":"additional","affiliation":[{"name":"Didi Chuxing"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Computing, Macquarie University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanlin","family":"Wu","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","theme":"Artificial Intelligence","location":"Montreal, Canada","acronym":"IJCAI-2025","number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2025,8,16]]},"end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:33:53Z","timestamp":1758627233000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/382"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/382","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}