{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T16:18:50Z","timestamp":1784996330836,"version":"3.55.0"},"reference-count":53,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976247"],"award-info":[{"award-number":["61976247"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1109\/tkde.2023.3283520","type":"journal-article","created":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T17:40:02Z","timestamp":1686159602000},"page":"62-76","source":"Crossref","is-referenced-by-count":42,"title":["CityTrans: Domain-Adversarial Training With Knowledge Transfer for Spatio-Temporal Prediction Across Cities"],"prefix":"10.1109","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4626-4373","authenticated-orcid":false,"given":"Xiaocao","family":"Ouyang","sequence":"first","affiliation":[{"name":"School of Computing and Artificial Intelligence, Institute of Artificial Intelligence, Southwest Jiaotong University, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6134-6094","authenticated-orcid":false,"given":"Yan","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computing and Artificial Intelligence, Institute of Artificial Intelligence, Southwest Jiaotong University, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8000-0320","authenticated-orcid":false,"given":"Wei","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computing and Artificial Intelligence, Institute of Artificial Intelligence, Southwest Jiaotong University, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8272-4330","authenticated-orcid":false,"given":"Yiling","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computing and Artificial Intelligence, Institute of Artificial Intelligence, Southwest Jiaotong University, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9492-3807","authenticated-orcid":false,"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"Research Institute of Artificial Intelligence, Zhejiang Lab, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9031-107X","authenticated-orcid":false,"given":"Wei","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computing and Artificial Intelligence, Institute of Artificial Intelligence, Southwest Jiaotong University, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"},{"key":"ref2","first-page":"1","article-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Li"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/264"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11836"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106286"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2954510"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-022-08036-0"},{"key":"ref8","first-page":"802","article-title":"Convolutional LSTM network: A machine learning approach for precipitation nowcasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Shi"},{"key":"ref9","first-page":"1","article-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","volume-title":"Proc. 28th Conf. Neural Inf. Process. Syst. Workshop Deep Learn.","author":"Chung"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"ref12","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016"},{"key":"ref13","first-page":"17804","article-title":"Adaptive graph convolutional recurrent network for traffic forecasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Bai"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939830"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/262"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313577"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482000"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2891537"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011020"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2022.3179781"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.3002718"},{"key":"ref24","article-title":"Graph attention networks","author":"Veli\u010dkovi\u0107","year":"2017"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3274895.3274896"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5477"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330884"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5480"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3034312"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403294"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467236"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM51629.2021.00131"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411965"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.03.068"},{"key":"ref36","first-page":"97","article-title":"Learning transferable features with deep adaptation networks","volume-title":"Proc. 32nd Int. Conf. Mach. Learn.","author":"Long"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2868685"},{"key":"ref38","first-page":"2672","article-title":"Generative adversarial nets","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Goodfellow"},{"issue":"1","key":"ref39","first-page":"2096","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"Ganin","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref40","first-page":"1647","article-title":"Conditional adversarial domain adaptation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Long"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2022.3144250"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.316"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3008010"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3026079"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482315"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3055207"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOM50583.2021.9439123"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557294"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2935152"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5438"},{"key":"ref51","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","volume-title":"Proc. 32nd Int. Conf. Mach. Learn.","author":"Ioffe"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.3141\/1748-12"},{"issue":"11","key":"ref53","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/69\/10348031\/10145833.pdf?arnumber=10145833","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T17:39:34Z","timestamp":1715189974000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10145833\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1]]},"references-count":53,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2023.3283520","relation":{},"ISSN":["1041-4347","1558-2191","2326-3865"],"issn-type":[{"value":"1041-4347","type":"print"},{"value":"1558-2191","type":"electronic"},{"value":"2326-3865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1]]}}}