{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:03:04Z","timestamp":1775815384051,"version":"3.50.1"},"reference-count":76,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"7","license":[{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"publisher","award":["2017YFB1001904"],"award-info":[{"award-number":["2017YFB1001904"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772042"],"award-info":[{"award-number":["61772042"]}],"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":[[2022,7]]},"DOI":"10.1109\/tits.2021.3072118","type":"journal-article","created":{"date-parts":[[2021,4,16]],"date-time":"2021-04-16T20:19:49Z","timestamp":1618604389000},"page":"7743-7758","source":"Crossref","is-referenced-by-count":81,"title":["A Graph-Based Temporal Attention Framework for Multi-Sensor Traffic Flow Forecasting"],"prefix":"10.1109","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9542-0574","authenticated-orcid":false,"given":"Shaokun","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Computer Science, School of EECS, Key Laboratory on High-Confidence Software Technologies (MOE), Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5064-5286","authenticated-orcid":false,"given":"Yao","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of EECS, Key Laboratory on High-Confidence Software Technologies (MOE), Peking University, Beijing, China"}]},{"given":"Peize","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1584-6506","authenticated-orcid":false,"given":"Chuanpan","family":"Zheng","sequence":"additional","affiliation":[{"name":"Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Informatics, Digital Fujian Institute of Urban Traffic Big Data Research, Xiamen University, Xiamen, China"}]},{"given":"Xiangqun","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of EECS, Key Laboratory on High-Confidence Software Technologies (MOE), Peking University, Beijing, China"}]}],"member":"263","reference":[{"key":"ref73","article-title":"Distributed representations of words and phrases and their compositionality","author":"mikolov","year":"2013","journal-title":"arXiv 1310 4546"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2018.03.002"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2018.8518853"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2018.2803085"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974973.87"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2012-65"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.3141\/1678-22"},{"key":"ref75","article-title":"LSTM-based deep learning models for non-factoid answer selection","author":"tan","year":"2015","journal-title":"arXiv 1511 04108"},{"key":"ref38","author":"box","year":"2015","journal-title":"Time Series Analysis Forecasting and Control"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2935152"},{"key":"ref32","article-title":"GaAN: Gated attention networks for learning on large and spatiotemporal graphs","author":"zhang","year":"2018","journal-title":"arXiv 1803 07294"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380186"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2901118"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1080\/0144164042000195072"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2014.01.005"},{"key":"ref35","article-title":"Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting","author":"yu","year":"2017","journal-title":"arXiv 1709 04875"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5477"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2864987"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8682539"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939753"},{"key":"ref27","first-page":"1","article-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","author":"li","year":"2018","journal-title":"Proc 6th Int Conf Learn Represent (ICLR)"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"ref66","first-page":"2235","article-title":"Deepaggregation: A new approach for aggregating incomplete ranked lists using multi-layer graph embedding","author":"vallam","year":"2019","journal-title":"Proc 18th Int Conf Auton Agents MultiAgent Syst"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5438"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-73198-8_10"},{"key":"ref68","article-title":"Modeling spatial-temporal dynamics for traffic prediction","author":"yao","year":"2018","journal-title":"arXiv 1803 01254"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2014.2311123"},{"key":"ref2","first-page":"269","article-title":"A neural network based traffic-flow prediction models","volume":"15","author":"\u00e7etiner","year":"2010","journal-title":"Comput Math Appl"},{"key":"ref1","article-title":"Cross-city transfer learning for deep spatio-temporal prediction","author":"wang","year":"2018","journal-title":"arXiv 1802 00386"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/YAC.2016.7804912"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2015.03.014"},{"key":"ref21","first-page":"865","article-title":"Traffic flow prediction with big data: A deep learning approach","volume":"16","author":"lv","year":"2015","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.3390\/s18072287"},{"key":"ref23","first-page":"2618","article-title":"Deeptransport: Prediction and simulation of human mobility and transportation mode at a citywide level","volume":"16","author":"song","year":"2016","journal-title":"Proc IJCAI"},{"key":"ref26","article-title":"Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework","author":"wu","year":"2016","journal-title":"arXiv 1612 01022"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11836"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ISKE.2017.8258813"},{"key":"ref51","first-page":"802","article-title":"Convolutional LSTM network: A machine learning approach for precipitation nowcasting","author":"xingjian","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2933459"},{"key":"ref58","article-title":"Multitask attention network for lane detection and fitting","author":"wang","year":"2020","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-16142-2_15"},{"key":"ref56","article-title":"Distance-based self-attention network for natural language inference","author":"im","year":"2017","journal-title":"arXiv 1712 02047"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2020.102620"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2017.10.023"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2020.102671"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/264"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2014.2306328"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2016.2643005"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0733-947X(2003)129:6(664)"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2015.2511156"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2016.2639320"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1126\/science.1177170"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1080\/03081068808717359"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1080\/15472450902858368"},{"key":"ref17","first-page":"6","article-title":"Short-term prediction of traffic situation using MLP-neural networks","author":"innamaa","year":"2000","journal-title":"Proc 7th World Congr Intell Transp Syst"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1049\/iet-its.2016.0208"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.3390\/s17040818"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2017.02.024"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1002\/asmb.1937"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2958185"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-017-0487-4"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2013.10.012"},{"key":"ref7","first-page":"184","article-title":"Optimal resource allocation and toll patterns in user-optimised transport networks","volume":"5","author":"dafermos","year":"1971","journal-title":"J Transp Econ Policy"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/WCSP.2017.8171119"},{"key":"ref9","first-page":"53","article-title":"City management platform using big data from people and traffic flows","volume":"64","author":"morioka","year":"2015","journal-title":"Hitachi Rev"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2749964"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1061\/9780784479896.026"},{"key":"ref48","article-title":"Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks","author":"yu","year":"2017","journal-title":"arXiv 1705 02699"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2726546"},{"key":"ref42","article-title":"Low-rank autoregressive tensor completion for multivariate time series forecasting","author":"chen","year":"2020","journal-title":"arXiv 2006 10436"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/S0968-090X(97)82903-8"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2010.12.032"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2014.02.007"}],"container-title":["IEEE Transactions on Intelligent Transportation Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6979\/9826234\/09406409.pdf?arnumber=9406409","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,29]],"date-time":"2022-08-29T21:27:42Z","timestamp":1661808462000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9406409\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":76,"journal-issue":{"issue":"7"},"URL":"https:\/\/doi.org\/10.1109\/tits.2021.3072118","relation":{},"ISSN":["1524-9050","1558-0016"],"issn-type":[{"value":"1524-9050","type":"print"},{"value":"1558-0016","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7]]}}}