{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T11:59:52Z","timestamp":1781006392268,"version":"3.54.1"},"reference-count":34,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.knosys.2026.116133","type":"journal-article","created":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T14:47:54Z","timestamp":1777733274000},"page":"116133","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["ADIG-Net: Spatiotemporally decoupled dynamic graph learning for efficient traffic forecasting"],"prefix":"10.1016","volume":"345","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-8547-4175","authenticated-orcid":false,"given":"Yongcai","family":"Tong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8921-7717","authenticated-orcid":false,"given":"Siyu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4493-7762","authenticated-orcid":false,"given":"Peng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jilong","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3080-9288","authenticated-orcid":false,"given":"Cecheng","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianyun","family":"Luan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"8","key":"10.1016\/j.knosys.2026.116133_bib0001","doi-asserted-by":"crossref","first-page":"3681","DOI":"10.1109\/TKDE.2020.3025580","article-title":"Deep learning for spatio-temporal data mining: a survey","volume":"34","author":"Wang","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"2","key":"10.1016\/j.knosys.2026.116133_bib0002","first-page":"456","article-title":"Urban spatio-temporal prediction: a survey","volume":"37","author":"Jin","year":"2025","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"6","key":"10.1016\/j.knosys.2026.116133_bib0003","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1061\/(ASCE)0733-947X(2003)129:6(664)","article-title":"Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process","volume":"129","author":"Williams","year":"2003","journal-title":"J. Transp. Eng."},{"key":"10.1016\/j.knosys.2026.116133_bib0004","series-title":"Modeling Financial Time Series with S-Plus","first-page":"385","article-title":"Vector autoregressive models for multivariate time series","author":"Zivot","year":"2006"},{"key":"10.1016\/j.knosys.2026.116133_bib0005","first-page":"1655","article-title":"Deep spatio-temporal residual networks for citywide crowd flows prediction","volume":"31","author":"Zhang","year":"2017","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"10.1016\/j.knosys.2026.116133_bib0006","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1145\/3209978.3210006","article-title":"Modeling long- and short-term temporal patterns with deep neural networks","author":"Lai","year":"2018","journal-title":"Proc. 41st Int. ACM SIGIR Conf. Res. Dev. Inf. Retr."},{"key":"10.1016\/j.knosys.2026.116133_bib0007","article-title":"Diffusion convolutional recurrent neural network: data-driven traffic forecasting","author":"Li","year":"2018","journal-title":"Proc. Int. Conf. Learn. Represent."},{"key":"10.1016\/j.knosys.2026.116133_bib0008","first-page":"3634","article-title":"Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting","author":"Yu","year":"2018","journal-title":"Proc. 27th Int. Jt. Conf. Artif. Intell."},{"issue":"3","key":"10.1016\/j.knosys.2026.116133_bib0009","first-page":"1567","article-title":"Dynamic spatial-temporal graph convolutional neural networks for traffic forecasting","volume":"26","author":"Liu","year":"2025","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"10","key":"10.1016\/j.knosys.2026.116133_bib0010","first-page":"10870","article-title":"Multi-STGCnet: a graph convolution based spatial-temporal framework for subway passenger flow forecasting","volume":"24","author":"Ye","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.knosys.2026.116133_bib0011","first-page":"11906","article-title":"DSTAGNN: dynamic spatial-temporal aware graph neural network for traffic flow forecasting","author":"Lan","year":"2022","journal-title":"Proc. Int. Conf. Mach. Learn."},{"key":"10.1016\/j.knosys.2026.116133_bib0012","first-page":"4125","article-title":"Spatio-temporal adaptive embedding makes vanilla transformer SOTA for traffic forecasting","author":"Liu","year":"2023","journal-title":"Proc. 32nd ACM Int. Conf. Inf. Knowl. Manag."},{"key":"10.1016\/j.knosys.2026.116133_bib0013","first-page":"17804","article-title":"Adaptive graph convolutional recurrent network for traffic forecasting","volume":"33","author":"Bai","year":"2020","journal-title":"Adv. Neural Inf. Process Syst."},{"key":"10.1016\/j.knosys.2026.116133_bib0014","first-page":"1907","article-title":"Graph WaveNet for deep spatial-temporal graph modeling","author":"Wu","year":"2019","journal-title":"Proc. 28th Int. Jt. Conf. Artif. Intell."},{"key":"10.1016\/j.knosys.2026.116133_bib0015","first-page":"4365","article-title":"PDFormer: propagation delay-aware dynamic long-range transformer for traffic flow prediction","volume":"37","author":"Jiang","year":"2023","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"10.1016\/j.knosys.2026.116133_bib0016","article-title":"Discrete graph structure learning for forecasting multiple time series","author":"Shang","year":"2021","journal-title":"Proc. Int. Conf. Learn. Represent."},{"key":"10.1016\/j.knosys.2026.116133_bib0017","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.130297","article-title":"DSSTN: dynamic selective spatio-temporal modeling network for traffic flow prediction","volume":"299","author":"Yang","year":"2026","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.knosys.2026.116133_bib0018","first-page":"4454","article-title":"Spatial-temporal identity: a simple yet effective baseline for multivariate time series forecasting","author":"Shao","year":"2022","journal-title":"Proc. 31st ACM Int. Conf. Inf. Knowl. Manag."},{"key":"10.1016\/j.knosys.2026.116133_bib0019","first-page":"1720","article-title":"Urban traffic prediction from spatio-temporal data using deep meta learning","author":"Pan","year":"2019","journal-title":"Proc. 25th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min."},{"key":"10.1016\/j.knosys.2026.116133_bib0020","first-page":"4350","article-title":"Spatio-temporal self-supervised learning for traffic flow prediction","author":"Ji","year":"2023","journal-title":"Proc. 37th AAAI Conf. Artif. Intell."},{"key":"10.1016\/j.knosys.2026.116133_bib0021","first-page":"3438","article-title":"Measuring and relieving the over-smoothing problem for graph neural networks from the topological view","volume":"34","author":"Chen","year":"2020","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"10.1016\/j.knosys.2026.116133_bib0022","article-title":"GraphSAINT: graph sampling based inductive learning method","author":"Zeng","year":"2020","journal-title":"Proc. Int. Conf. Learn. Represent."},{"issue":"8","key":"10.1016\/j.knosys.2026.116133_bib0023","first-page":"8234","article-title":"Adaptive multi-receptive field spatial-temporal graph convolutional network for traffic forecasting","volume":"25","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.knosys.2026.116133_bib0024","first-page":"753","article-title":"Connecting the dots: multivariate time series forecasting with graph neural networks","author":"Wu","year":"2020","journal-title":"Proc. 26th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min."},{"issue":"3","key":"10.1016\/j.knosys.2026.116133_bib0025","first-page":"789","article-title":"Temporal heterogeneity-aware dynamic graph neural networks for traffic flow forecasting","volume":"37","author":"Zhou","year":"2025","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.knosys.2026.116133_bib0026","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112788","article-title":"A novel spatio-temporal feature interleaved contrast learning neural network from a robustness perspective","volume":"309","author":"Liu","year":"2025","journal-title":"Knowl.-Based. Syst."},{"key":"10.1016\/j.knosys.2026.116133_bib0027","first-page":"1234","article-title":"GMAN: a graph multi-attention network for traffic prediction","volume":"34","author":"Zheng","year":"2020","journal-title":"Proc. Thirty-Fourth AAAI Conf. Artif. Intell."},{"key":"10.1016\/j.knosys.2026.116133_bib0028","first-page":"922","article-title":"Attention based spatial-temporal graph convolutional networks for traffic flow forecasting","volume":"33","author":"Guo","year":"2019","journal-title":"Proc. Thirty-Third AAAI Conf. Artif. Intell."},{"key":"10.1016\/j.knosys.2026.116133_bib0029","first-page":"2965","article-title":"Historical inertia: a neglected but powerful baseline for long sequence time-series forecasting","author":"Cui","year":"2021","journal-title":"Proc. 30th ACM Int. Conf. Inf. Knowl. Manag."},{"key":"10.1016\/j.knosys.2026.116133_bib0030","doi-asserted-by":"crossref","first-page":"1724","DOI":"10.3115\/v1\/D14-1179","article-title":"Learning phrase representations using RNN encoder-decoder for statistical machine translation","author":"Cho","year":"2014","journal-title":"Proc. 2014 Conf. Empir. Methods Nat. Lang. Process."},{"key":"10.1016\/j.knosys.2026.116133_bib0031","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1145\/3447548.3467330","article-title":"ST-norm: spatial and temporal normalization for multi-variate time series forecasting","author":"Deng","year":"2021","journal-title":"Proc. 27th ACM SIGKDD Conf. Knowl. Discov. Data Min."},{"key":"10.1016\/j.knosys.2026.116133_bib0032","first-page":"6425","article-title":"Towards spatio-temporal aware traffic time series forecasting","volume":"36","author":"Cirstea","year":"2022","journal-title":"Proc. 36th AAAI Conf. Artif. Intell."},{"key":"10.1016\/j.knosys.2026.116133_bib0033","first-page":"8078","article-title":"Spatio-temporal meta-graph learning for traffic forecasting","volume":"37","author":"Jiang","year":"2023","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"10.1016\/j.knosys.2026.116133_bib0034","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1145\/3637528.3671961","article-title":"Heterogeneity-informed meta-parameter learning for spatiotemporal time series forecasting","author":"Dong","year":"2024","journal-title":"Proc. 30th ACM SIGKDD Conf. Knowl. Discov. Data Min."}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126008592?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126008592?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T11:22:11Z","timestamp":1781004131000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126008592"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":34,"alternative-id":["S0950705126008592"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116133","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"ADIG-Net: Spatiotemporally decoupled dynamic graph learning for efficient traffic forecasting","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116133","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"116133"}}