{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T21:03:46Z","timestamp":1775250226968,"version":"3.50.1"},"reference-count":46,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Comput. Soc. Syst."],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1109\/tcss.2025.3605930","type":"journal-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T17:10:13Z","timestamp":1761066613000},"page":"2030-2046","source":"Crossref","is-referenced-by-count":0,"title":["Selective State-Focused Graph Attention Networks for Multihorizon Traffic Forecasting"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0006-6420-950X","authenticated-orcid":false,"given":"Shikhar","family":"Vashistha","sequence":"first","affiliation":[{"name":"Indian Institute of Technology Roorkee, Roorkee, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-0516-1095","authenticated-orcid":false,"given":"Neetesh","family":"Kumar","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Roorkee, Roorkee, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3054840"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3333824"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557702"},{"key":"ref4","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.129280","article-title":"DSTF: A diversified spatio-temporal feature extraction model for traffic flow prediction","volume":"621","author":"Wang","year":"2025","journal-title":"Neurocomputing"},{"key":"ref5","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.108199","article-title":"Adaptive spatio-temporal graph neural network traffic forecasting","volume":"242","author":"Ta","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403118"},{"key":"ref7","first-page":"11906","article-title":"DSTAGNN: Dynamic spatial-temporal aware graph neural network for traffic flow forecasting","volume-title":"Proc. Mach. Learn. Res. (ICML)","volume":"162","author":"Lan","year":"2022"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2024.3450846"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s12239-024-00130-7"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.3390\/s24206659"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-024-06539-2"},{"key":"ref12","first-page":"1176","article-title":"Preserving spatial-temporal relationship with adaptive node sampling in hierarchical dynamic graph transformers","volume-title":"Proc. 16th Asian Conf. Mach. Learn. (ACML), Hanoi, Vietnam","volume":"260","author":"Hoang"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3390\/math12193159"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.3390\/inventions9050102"},{"key":"ref15","article-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","volume-title":"Proc. ICLR","author":"Li","year":"2018"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/264"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403118"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5477"},{"issue":"1","key":"ref21","first-page":"914","article-title":"Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting","volume-title":"Proc. AAAI","volume":"34","author":"Song","year":"2020"},{"key":"ref22","first-page":"17804","article-title":"Adaptive graph convolutional recurrent network for traffic forecasting","volume-title":"Proc. NeurIPS","volume":"33","author":"Bai","year":"2020"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467430"},{"issue":"5","key":"ref24","first-page":"4189","article-title":"Spatial-temporal fusion graph neural networks for traffic flow forecasting","volume-title":"Proc. AAAI","volume":"35","author":"Li","year":"2021"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3056502"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i6.20587"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.14778\/3551793.3551827"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2022.3224039"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2022.3223918"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2023.3284156"},{"key":"ref31","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2023.101837","article-title":"Integrating the traffic science with representation learning for city-wide network congestion prediction","volume":"99","author":"Zheng","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref32","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2023.102122","article-title":"ADCT-Net: Adaptive traffic forecasting neural network via dual-graphic cross-fused transformer","volume":"103","author":"Kong","year":"2024","journal-title":"Inf. Fusion"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i7.25976"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2024.3476129"},{"key":"ref35","article-title":"Fusion matrix prompt enhanced self-attention spatial-temporal interactive traffic forecasting framework","author":"Liu","year":"2024"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2023.3257759"},{"key":"ref37","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113635","article-title":"Two-way heterogeneity model for dynamic spatiotemporal traffic flow prediction","volume":"320","author":"Zhang","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"ref38","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111946","article-title":"Dynamic spatial aware graph transformer for spatiotemporal traffic flow forecasting","volume":"297","author":"Li","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"ref39","first-page":"1","article-title":"Temporal graph networks for deep learning on dynamic graphs","volume-title":"Proc. ACM SIGKDD","author":"Rossi","year":"2020"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CCGRID64434.2025.00014"},{"key":"ref41","article-title":"How attentive are graph attention networks?","author":"Brody","year":"2022"},{"key":"ref42","article-title":"Caltrans performance measurement system (PEMS)","year":"2023"},{"key":"ref43","article-title":"Simulation of Urban MObility (SUMO)","author":"Krajzewicz","year":"2023"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2024.3362145"},{"key":"ref45","first-page":"1","article-title":"Random walks on graphs: A survey","volume-title":"Combinatorics, Paul Erd\u0151s Is Eighty","volume":"2","author":"Lov\u00e1sz","year":"1993"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1090\/cbms\/092"}],"container-title":["IEEE Transactions on Computational Social Systems"],"original-title":[],"link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/xplorestaging.ieee.org\/ielx8\/6570650\/11471691\/11208846.pdf?arnumber=11208846","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T19:56:06Z","timestamp":1775246166000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/ieeexplore.ieee.org\/document\/11208846\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":46,"journal-issue":{"issue":"2"},"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1109\/tcss.2025.3605930","relation":{},"ISSN":["2329-924X","2373-7476"],"issn-type":[{"value":"2329-924X","type":"electronic"},{"value":"2373-7476","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4]]}}}