{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T00:44:36Z","timestamp":1753922676332,"version":"3.37.3"},"reference-count":44,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Science and Technology","award":["SZJJ2019-22"],"award-info":[{"award-number":["SZJJ2019-22"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Intell. Transport. Syst. Mag."],"published-print":{"date-parts":[[2023,1]]},"DOI":"10.1109\/mits.2022.3158631","type":"journal-article","created":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T19:50:22Z","timestamp":1648669822000},"page":"230-243","source":"Crossref","is-referenced-by-count":2,"title":["A Fusion Deep Learning Model via Sequence-to-Sequence Structure for Multiple-Road-Segment Spot Speed Prediction"],"prefix":"10.1109","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7637-6344","authenticated-orcid":false,"given":"Dongyi","family":"Li","sequence":"first","affiliation":[{"name":"Key Laboratory of Transport Industry of Management, Control and Cycle Repair Technology for Traffic Network Facilities in Ecological Security Barrier Area, School of Transportation Engineering, Chang&#x2019;an University, Xi&#x2019;an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6548-2894","authenticated-orcid":false,"given":"Jianjun","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Transport Industry of Management, Control and Cycle Repair Technology for Traffic Network Facilities in Ecological Security Barrier Area, School of Transportation Engineering, Chang&#x2019;an University, Xi&#x2019;an, China"}]}],"member":"263","reference":[{"issue":"2","key":"ref1","first-page":"95","article-title":"Highway traffic anomaly identification method based on short-term traffic prediction","volume":"32","author":"Tang","year":"2014","journal-title":"Transp. Info. Safe"},{"issue":"11","key":"ref2","first-page":"222","article-title":"Bus travel time prediction algorithm based on multi-line information fusion","volume":"46","author":"Ma","year":"2019","journal-title":"Comput. Sci."},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1061\/(asce)0733-947x(2003)129:6(664)"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.4236\/ijcns.2015.84005"},{"issue":"4","key":"ref5","first-page":"775","article-title":"Traffic flow-big data forecasting method based on spatial-temporal weight correlation","volume":"53","author":"Li","year":"2017","journal-title":"B. J. Uni. Sci. Natur. Acta"},{"issue":"5","key":"ref6","first-page":"122","article-title":"Short-term traffic flow prediction based on K-nearest neighbor algorithm and Support Vector Regression combination","volume":"34","author":"Liu","year":"2017","journal-title":"Highw. Transp. Res. Dev."},{"issue":"1","key":"ref7","first-page":"60","article-title":"Short-term traffic flow forecasting of road network based on GA-LSSVR model","volume":"17","author":"Chen","year":"2017","journal-title":"Transp. Syst. Eng. Inf. Technol."},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2009.11.076"},{"issue":"2","key":"ref9","first-page":"36","article-title":"Short-term traffic volume forecasting based on ARMA and Kalman filter","volume":"38","author":"Yang","year":"2017","journal-title":"Z. Z. Univ. Acta. (Eng. Sci. Ed.)"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/s0968-090x(02)00009-8"},{"issue":"7","key":"ref11","first-page":"129","article-title":"Nonparametric regression short-term traffic flow prediction algorithm based on data reduction and SVM","volume":"37","author":"Wu","year":"2020","journal-title":"Highw. Transp. Res. Dev."},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1061\/9780784479896.026"},{"issue":"5","key":"ref13","first-page":"99","article-title":"A forecast of short-term traffic flow based on GBRBM-DBN model","volume":"36","author":"Feng","year":"2018","journal-title":"Transp. Info. Safe"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2014.2345663"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0119044"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2015.03.014"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/MITS.2019.2903431"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.3390\/s17040818"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.3390\/s17071501"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2018.2867042"},{"key":"ref22","first-page":"802","article-title":"Convolutional LSTM network: A machine learning approach for precipitation nowcasting","volume-title":"Proc. 29th Annu. Conf. Neur. Info. Proc. Syst.","author":"Shi","year":"2015"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/WCSP.2017.8171119"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref25","article-title":"Adam: a method for stochastic optimization","volume-title":"Proc. 3th Int. Conf. Lear. Repr.","author":"Kingma","year":"2015"},{"key":"ref26","first-page":"3104","article-title":"Sequence to sequence learning with neural networks","volume-title":"Proc. 28th Conf. Neur. Info. Pro. Syst.","author":"Sutskever","year":"2014"},{"article-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","year":"2018","author":"Li","key":"ref27"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2015.03.003"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.03.001"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015668"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01252-6_44"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.2997352"},{"issue":"8","key":"ref33","first-page":"1715","article-title":"A sequence to sequence spatial-temporal attention learning model for urban traffic flow prediction","volume":"57","author":"Du","year":"2020","journal-title":"Compu. Res. Dev."},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/MITS.2019.2919593"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/MITS.2019.2926274"},{"issue":"3","key":"ref36","first-page":"47","article-title":"Traffic flow prediction based on hybrid deep learning under connected and automated vehicle environment","volume":"20","author":"Lu","year":"2020","journal-title":"Transp. Syst. Eng. Inf. Technol."},{"key":"ref37","article-title":"Route travel time prediction on deep learning model through spatiotemporal features","volume-title":"J. L. Univ. Acta. (Eng. Tech. Ed.)","author":"Li","year":"2021"},{"issue":"2","key":"ref38","first-page":"303","article-title":"Traffic prediction model based on spatiotemporal attention network","volume":"42","author":"Wang","year":"2021","journal-title":"Chi. Comp. Syst."},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.12.118"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115008"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1002\/ett.3482"},{"issue":"1","key":"ref42","first-page":"81","article-title":"Short-term traffic flow prediction based on deep learning","volume":"18","author":"Wang","year":"2018","journal-title":"Transp. Syst. Eng. Inf. Technol."},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.3390\/s17071501"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.2979634"}],"container-title":["IEEE Intelligent Transportation Systems Magazine"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5117645\/10015073\/09745184.pdf?arnumber=9745184","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,18]],"date-time":"2024-01-18T00:32:42Z","timestamp":1705537962000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9745184\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1]]},"references-count":44,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/mits.2022.3158631","relation":{},"ISSN":["1939-1390","1941-1197"],"issn-type":[{"type":"print","value":"1939-1390"},{"type":"electronic","value":"1941-1197"}],"subject":[],"published":{"date-parts":[[2023,1]]}}}