{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:01:10Z","timestamp":1772553670064,"version":"3.50.1"},"reference-count":47,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Hong Kong General Research Fund","award":["16200120"],"award-info":[{"award-number":["16200120"]}]},{"name":"Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning","award":["2020B121202019"],"award-info":[{"award-number":["2020B121202019"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62202070"],"award-info":[{"award-number":["62202070"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2022M720567"],"award-info":[{"award-number":["2022M720567"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2023,11,1]]},"DOI":"10.1109\/tkde.2022.3233086","type":"journal-article","created":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T18:59:21Z","timestamp":1672426761000},"page":"10967-10980","source":"Crossref","is-referenced-by-count":40,"title":["A Lightweight and Accurate Spatial-Temporal Transformer for Traffic Forecasting"],"prefix":"10.1109","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3950-9360","authenticated-orcid":false,"given":"Guanyao","family":"Li","sequence":"first","affiliation":[{"name":"Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4037-4288","authenticated-orcid":false,"given":"Shuhan","family":"Zhong","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5776-8246","authenticated-orcid":false,"given":"Xingdong","family":"Deng","sequence":"additional","affiliation":[{"name":"Guangzhou Urban Planning and Design Survey Research Institute, Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou, Guangdong, China"}]},{"given":"Letian","family":"Xiang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4207-764X","authenticated-orcid":false,"given":"S.-H. Gary","family":"Chan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9149-3442","authenticated-orcid":false,"given":"Ruiyuan","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science, Chongqing University, Chongqing, China"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangzhou Urban Planning and Design Survey Research Institute, Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou, Guangdong, China"}]},{"given":"Ming","family":"Zhang","sequence":"additional","affiliation":[{"name":"Guangzhou Urban Planning and Design Survey Research Institute, Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou, Guangdong, China"}]},{"given":"Chih-Chieh","family":"Hung","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, National Chung Hsing University, Taichung, Taiwan"}]},{"given":"Wen-Chih","family":"Peng","sequence":"additional","affiliation":[{"name":"Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan"}]}],"member":"263","reference":[{"key":"ref13","article-title":"Spatial-temporal transformer networks for traffic flow forecasting","author":"xu","year":"2020"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403046"},{"key":"ref34","first-page":"1","article-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","author":"li","year":"2018","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.3141\/2024-14"},{"key":"ref37","article-title":"Forecaster: A graph transformer for forecasting spatial and time-dependent data","author":"li","year":"2020","journal-title":"Proc Eur Conf Artif Intell"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/476"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE48307.2020.00178"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5438"},{"key":"ref11","article-title":"Interpretable crowd flow prediction with spatial-temporal self-attention","author":"lin","year":"2020"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00058"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2019.10.071"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00037"},{"key":"ref2","article-title":"Efficient deep learning: A survey on making deep learning models smaller, faster, and better","author":"menghani","year":"2021"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/2629592"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/S0968-090X(02)00009-8"},{"key":"ref39","article-title":"Reformer: The efficient transformer","author":"kitaev","year":"2020","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2012.52"},{"key":"ref38","first-page":"5243","article-title":"Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting","volume":"32","author":"li","year":"2019","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.3141\/2024-11"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1412908112"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6142"},{"key":"ref46","first-page":"802","article-title":"Convolutional LSTM network: A machine learning approach for precipitation nowcasting","author":"xingjian","year":"2015","journal-title":"Proc Adv iNeural Inf Process Syst"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380097"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330884"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00936"},{"key":"ref47","article-title":"Graph attention networks","author":"veli?kovi?","year":"2018","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/2820783.2820837"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.2983763"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403076"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403122"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098018"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-012-0250-5"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467430"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380186"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16542"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013656"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"},{"key":"ref9","first-page":"5998","article-title":"Attention is all you need","author":"vaswani","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/2996913.2997016"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015668"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11836"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/69\/10273671\/10004027.pdf?arnumber=10004027","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T19:31:39Z","timestamp":1698694299000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10004027\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,1]]},"references-count":47,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2022.3233086","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":[[2023,11,1]]}}}