{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T04:41:40Z","timestamp":1784004100708,"version":"3.55.0"},"reference-count":52,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100018542","name":"Natural Science Foundation of Sichuan Province","doi-asserted-by":"publisher","award":["2023NSFSC1423"],"award-info":[{"award-number":["2023NSFSC1423"]}],"id":[{"id":"10.13039\/501100018542","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds for the Central Universities, the open fund of state key laboratory of public big data","award":["PBD2023-09"],"award-info":[{"award-number":["PBD2023-09"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62206192"],"award-info":[{"award-number":["62206192"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Intell. Transport. Syst."],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1109\/tits.2024.3420423","type":"journal-article","created":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T18:58:23Z","timestamp":1720551503000},"page":"18264-18278","source":"Crossref","is-referenced-by-count":6,"title":["Long-Term Airport Network Performance Forecasting With Linear Diffusion Graph Networks"],"prefix":"10.1109","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4435-9413","authenticated-orcid":false,"given":"Yuankai","family":"Wu","sequence":"first","affiliation":[{"name":"College of Computer Science, Sichuan University, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1915-9487","authenticated-orcid":false,"given":"Jing","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Public Big Data, Guizhou University, Guiyang, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3629-6109","authenticated-orcid":false,"given":"Xiaoxu","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering and Applied Mechanics, McGill University, Montreal, QC, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7194-5023","authenticated-orcid":false,"given":"Yi","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Computer Science, Sichuan University, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2322-0538","authenticated-orcid":false,"given":"Hongyu","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer Science, Sichuan University, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtrangeo.2020.102749"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.11.015"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1287\/isre.1100.0312"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.2307\/2985674"},{"key":"ref5","volume-title":"Forecasting: Principles and Practice","author":"Hyndman","year":"2021"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1198\/016214507000000257"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.2990960"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.2514\/6.2019-1661"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-1498-8_57"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/KAM.2008.18"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1057\/ejis.2010.11"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.tre.2013.10.008"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1287\/trsc.2020.1026"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/S0304-4076(03)00132-5"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2010.10.002"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2018.2822784"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/4836260"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2019.2954094"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3103502"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.tre.2022.102997"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3286690"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2019.07.001"},{"key":"ref23","article-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","author":"Bai","year":"2018","journal-title":"arXiv:1803.01271"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref25","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. ICLR","author":"Kipf"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26317"},{"key":"ref27","article-title":"Long-term forecasting with TiDE: Time-series dense encoder","author":"Das","year":"2023","journal-title":"arXiv:2304.08424"},{"key":"ref28","first-page":"16211","article-title":"Random walk graph neural networks","volume-title":"Proc. NeurIPS","volume":"33","author":"Nikolentzos"},{"key":"ref29","article-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","volume-title":"Proc. ICLR","author":"Li"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3533382"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"ref32","article-title":"A time series is worth 64 words: Long-term forecasting with transformers","volume-title":"Proc. ICLR","author":"Nie"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1287\/trsc.13.3.201"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2011.05.017"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.2514\/1.13496"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.2514\/6.2009-7129"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1606.09375"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00810"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330884"},{"key":"ref41","first-page":"17804","article-title":"Adaptive graph convolutional recurrent network for traffic forecasting","volume-title":"Proc. NeurIPS","volume":"33","author":"Bai"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/264"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5477"},{"key":"ref44","article-title":"Do we really need graph neural networks for traffic forecasting?","author":"Liu","year":"2023","journal-title":"arXiv:2301.12603"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20881"},{"key":"ref46","first-page":"27268","article-title":"FedFormer: Frequency enhanced decomposed transformer for long-term series forecasting","volume-title":"Proc. ICML","author":"Zhou"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1137\/07070111X"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/S0169-7552(98)00110-X"},{"key":"ref49","volume-title":"Markov Chains","volume":"2","author":"Norris","year":"1998"},{"key":"ref50","first-page":"4171","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","volume-title":"Proc. NAACL-HLT","author":"Devlin"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557702"},{"key":"ref52","article-title":"TimesNet: Temporal 2D-variation modeling for general time series analysis","volume-title":"Proc. ICLR","author":"Wu"}],"container-title":["IEEE Transactions on Intelligent Transportation Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6979\/10742224\/10589713.pdf?arnumber=10589713","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T23:24:30Z","timestamp":1732663470000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10589713\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11]]},"references-count":52,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tits.2024.3420423","relation":{},"ISSN":["1524-9050","1558-0016"],"issn-type":[{"value":"1524-9050","type":"print"},{"value":"1558-0016","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11]]}}}