{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T16:30:33Z","timestamp":1781368233681,"version":"3.54.1"},"reference-count":84,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2021,11,16]],"date-time":"2021-11-16T00:00:00Z","timestamp":1637020800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2021,11,16]],"date-time":"2021-11-16T00:00:00Z","timestamp":1637020800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J of Intelligent Sys"],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1002\/int.22725","type":"journal-article","created":{"date-parts":[[2021,11,16]],"date-time":"2021-11-16T09:27:29Z","timestamp":1637054849000},"page":"4437-4470","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Toward a real\u2010time Smart Parking Data Management and Prediction (SPDMP) system by attributes representation learning"],"prefix":"10.1155","volume":"37","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0001-6431-8956","authenticated-orcid":false,"given":"Hao (Frank)","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering University of Washington Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruimin","family":"Ke","sequence":"additional","affiliation":[{"name":"Civil Engineering (Smart Cities) University of Texas at El Paso El Paso Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyong","family":"Cui","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering University of Washington Seattle Washington USA"},{"name":"eScience Institue University of Washington Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-4180-5628","authenticated-orcid":false,"given":"Yinhai","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering University of Washington Seattle Washington USA"},{"name":"Department of Electrical and Computer Engineering University of Washington Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karthik","family":"Murthy","sequence":"additional","affiliation":[{"name":"Washington State Department of Transportation (WSDOT) Olympia Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,11,16]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2014.04.002"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2020.102676"},{"key":"e_1_2_11_4_1","doi-asserted-by":"crossref","unstructured":"BertishJ JarrettA KrauseW JaiswalC. Truparking: Smart parking and the internet of things. 2018 9th IEEE Annual Ubiquitous Computing Electronics & Mobile Communication Conference (UEMCON). IEEE; 2018:203\u2010209.","DOI":"10.1109\/UEMCON.2018.8796716"},{"key":"e_1_2_11_5_1","unstructured":"WrennCA.Can Autonomous Technology Reduce the Driver Shortage in the Commercial Trucking Industry. PhD thesis Doctoral dissertation. California Southern University;2017."},{"key":"e_1_2_11_6_1","unstructured":"SprungMJ. Freight facts and figures (2017).U.S. Department of Transportation Bureau of Transportation Statistics; 2018.https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.21949\/1501488"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/MITS.2011.940473"},{"key":"e_1_2_11_8_1","unstructured":"WangH HeW. A reservation\u2010based smart parking system. 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE; 2011:690\u2010695."},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2013.2252428"},{"key":"e_1_2_11_10_1","doi-asserted-by":"crossref","unstructured":"ChengY RauS SrivastavaA et al. Data archiving and performance measurement for a multi\u2010state truck parking information management system (TPIMS). International Conference on Transportation and Development 2020. American Society of Civil Engineers Reston VA; 2020:251\u2010260.","DOI":"10.1061\/9780784483152.022"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)TE.1943-5436.0000756"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1177\/0361198118788185"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2685143"},{"key":"e_1_2_11_14_1","doi-asserted-by":"crossref","unstructured":"VitalFdAA IoannouP&GuptaASurvey on intelligent truck parking: Issues and approaches.IEEE Intell Transp Syst Mag. 2021;13(4):31\u201044.","DOI":"10.1109\/MITS.2019.2926259"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1061\/JTEPBS.0000397"},{"key":"e_1_2_11_16_1","unstructured":"KeR ZhuangY PuZ WangY. A smart efficient and reliable parking surveillance system with edge artificial intelligence on IoT devices. arXiv preprint arXiv:2001.00269.2020."},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2976433"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.1061\/JTEPBS.0000073"},{"key":"e_1_2_11_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/78.650093"},{"key":"e_1_2_11_20_1","unstructured":"ZarembaW SutskeverI VinyalsO. Recurrent neural network regularization. arXiv preprint arXiv:1409.2329.2014."},{"key":"e_1_2_11_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2015.03.014"},{"key":"e_1_2_11_22_1","unstructured":"CuiZ KeR PuZ WangY. Deep bidirectional and unidirectional LSTM recurrent neural network for network\u2010wide traffic speed prediction. arXiv preprint arXiv:1801.02143.2018."},{"key":"e_1_2_11_23_1","unstructured":"YangH LiuC GottsackerC BanX ZhangC WangY. Cell\u2010speed prediction neural network (CPNN): a deep learning approach for trip\u2010based speed prediction. Tech. rep.;2019."},{"key":"e_1_2_11_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2019.08.010"},{"key":"e_1_2_11_25_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1012074215150"},{"key":"e_1_2_11_26_1","doi-asserted-by":"crossref","unstructured":"HoiSC LiuW LyuMR MaW\u2010Y. Learning distance metrics with contextual constraints for image retrieval. 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06). Vol. 2 IEEE;2006:2072\u20102078.","DOI":"10.1109\/CVPR.2006.167"},{"key":"e_1_2_11_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/1823746.1823752"},{"key":"e_1_2_11_28_1","doi-asserted-by":"crossref","unstructured":"LiY ZhuZ KongD XuM ZhaoY. Learning heterogeneous spatial\u2010temporal representation for bike\u2010sharing demand prediction. Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 33.2019:1004\u20101011.","DOI":"10.1609\/aaai.v33i01.33011004"},{"key":"e_1_2_11_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-018-0616-8"},{"key":"e_1_2_11_30_1","doi-asserted-by":"crossref","unstructured":"LiJ WeiY LiangX et al. Deep attribute\u2010preserving metric learning for natural language object retrieval. Proceedings of the 25th ACM International Conference on Multimedia.2017:181\u2010189.","DOI":"10.1145\/3123266.3123439"},{"key":"e_1_2_11_31_1","doi-asserted-by":"publisher","DOI":"10.1561\/2200000019"},{"key":"e_1_2_11_32_1","doi-asserted-by":"crossref","unstructured":"CakirF HeK XiaX KulisB SclaroffS. Deep metric learning to rank. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2019:1861\u20101870.","DOI":"10.1109\/CVPR.2019.00196"},{"key":"e_1_2_11_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2011.2134107"},{"key":"e_1_2_11_34_1","unstructured":"HuangT\u2010W CaiJ YangH HsuH\u2010M HwangJ\u2010N. Multi\u2010view vehicle re\u2010identification using temporal attention model and metadata re\u2010ranking. In CVPR Workshops. Vol. 2.2019."},{"key":"e_1_2_11_35_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-45135-5_10"},{"key":"e_1_2_11_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-019-09744-1"},{"key":"e_1_2_11_37_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-12528-8_1"},{"key":"e_1_2_11_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2019.06.016"},{"key":"e_1_2_11_39_1","doi-asserted-by":"crossref","unstructured":"RevathiG DhulipalaVS. Smart parking systems and sensors: a survey. 2012 International Conference on Computing Communication and Applications. IEEE;2012:1\u20105.","DOI":"10.1109\/ICCCA.2012.6179195"},{"key":"e_1_2_11_40_1","doi-asserted-by":"crossref","unstructured":"KhannaA AnandR. IoT based smart parking system. 2016 International Conference on Internet of Things and Applications (IOTA). IEEE;2016:266\u2010270.","DOI":"10.1109\/IOTA.2016.7562735"},{"key":"e_1_2_11_41_1","doi-asserted-by":"crossref","unstructured":"AydinI KarakoseM KarakoseE. A navigation and reservation based smart parking platform using genetic optimization for smart cities. 2017 5th International Istanbul Smart Grid and Cities Congress and Fair (ICSG). IEEE;2017:120\u2010124.","DOI":"10.1109\/SGCF.2017.7947615"},{"key":"e_1_2_11_42_1","first-page":"26","volume-title":"Sustain Transp","author":"Simons D","year":"2012"},{"key":"e_1_2_11_43_1","doi-asserted-by":"publisher","DOI":"10.1049\/iet-its.2017.0406"},{"key":"e_1_2_11_44_1","unstructured":"GhentP MitchellD SedadiA. La express parkTM\u2010curbing downtown congestion through intelligent parking management. 19th ITS World CongressERTICO\u2010ITS EuropeEuropean CommissionITS AmericaITS Asia\u2010Pacific.2012."},{"key":"e_1_2_11_45_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0968-090X(03)00004-4"},{"key":"e_1_2_11_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2015.2428705"},{"key":"e_1_2_11_47_1","doi-asserted-by":"publisher","DOI":"10.1080\/15472450.2019.1579092"},{"key":"e_1_2_11_48_1","doi-asserted-by":"crossref","unstructured":"AkhawajiR SedkyM SolimanA\u2010H. Illegal parking detection using Gaussian mixture model and Kalman filter. 2017 IEEE\/ACS 14th International Conference on Computer Systems and Applications (AICCSA). IEEE;2017:840\u2010847.","DOI":"10.1109\/AICCSA.2017.212"},{"key":"e_1_2_11_49_1","doi-asserted-by":"crossref","unstructured":"XuB WolfsonO YangJ StennethL PhilipSY NelsonPC. Real\u2010time street parking availability estimation. 2013 IEEE 14th International Conference on Mobile Data Management. Vol. 1. IEEE;2013:16\u201025.","DOI":"10.1109\/MDM.2013.12"},{"key":"e_1_2_11_50_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trb.2018.04.001"},{"key":"e_1_2_11_51_1","doi-asserted-by":"crossref","unstructured":"TilahunSL&DiMarzoSerugendoGCooperative multiagent system for parking availability prediction based on time varying dynamic Markov chains.J Adv Transp. 2017:1760842.https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1155\/2017\/1760842","DOI":"10.1155\/2017\/1760842"},{"key":"e_1_2_11_52_1","doi-asserted-by":"crossref","unstructured":"YuF GuoJ ZhuX ShiG. Real time prediction of unoccupied parking space using time series model. 2015 International Conference on Transportation Information and Safety (ICTIS). IEEE;2015:370\u2010374.","DOI":"10.1109\/ICTIS.2015.7232145"},{"key":"e_1_2_11_53_1","doi-asserted-by":"crossref","unstructured":"ZhangF FengN LiuY et al. Pewlstm: Periodic LSTM with weather\u2010aware gating mechanism for parking behavior prediction. IJCAI;2020:4424\u20104430.","DOI":"10.24963\/ijcai.2020\/610"},{"key":"e_1_2_11_54_1","doi-asserted-by":"crossref","unstructured":"ZhangW LiuH LiuY ZhouJ XiongH. Semi\u2010supervised hierarchical recurrent graph neural network for city\u2010wide parking availability prediction. Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 34;2020:1186\u20101193.","DOI":"10.1609\/aaai.v34i01.5471"},{"key":"e_1_2_11_55_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trpro.2020.03.113"},{"key":"e_1_2_11_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3025589"},{"key":"e_1_2_11_57_1","doi-asserted-by":"crossref","unstructured":"AroraN CookJ KumarR et al. Hard to park? Estimating parking difficulty at scale. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining;2019:2296\u20102304.","DOI":"10.1145\/3292500.3330767"},{"key":"e_1_2_11_58_1","doi-asserted-by":"crossref","unstructured":"GhosalSS BaniA AmroussA ElHallaouiI. A deep learning approach to predict parking occupancy using cluster augmented learning method. 2019 International Conference on Data Mining Workshops (ICDMW). IEEE;2019:581\u2010586.","DOI":"10.1109\/ICDMW.2019.00088"},{"key":"e_1_2_11_59_1","doi-asserted-by":"crossref","unstructured":"ArjonaJ LinaresMP CasanovasJ. A deep learning approach to real\u2010time parking availability prediction for smart cities. Proceedings of the Second International Conference on Data Science E\u2010Learning and Information Systems.2019:1\u20107.","DOI":"10.1145\/3368691.3368707"},{"key":"e_1_2_11_60_1","doi-asserted-by":"crossref","unstructured":"RongY XuZ YanR MaX. Du\u2010parking: Spatio\u2010temporal big data tells you realtime parking availability. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining;2018:646\u2010654.","DOI":"10.1145\/3219819.3219876"},{"key":"e_1_2_11_61_1","doi-asserted-by":"publisher","DOI":"10.1080\/15472450.2015.1037955"},{"key":"e_1_2_11_62_1","doi-asserted-by":"crossref","unstructured":"YangM TuW WangJ XuF ChenX. Attention based LSTM for target dependent sentiment classification. Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 31.2017.","DOI":"10.1609\/aaai.v31i1.11061"},{"key":"e_1_2_11_63_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.01.078"},{"key":"e_1_2_11_64_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.05.028"},{"key":"e_1_2_11_65_1","unstructured":"VaswaniA ShazeerN ParmarN et al. Attention is all you need. Advances in Neural Information Processing Systems.2017:5998\u20106008."},{"key":"e_1_2_11_66_1","unstructured":"DevlinJ ChangM\u2010W LeeK ToutanovaK. Bert: Pre\u2010training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.2018."},{"key":"e_1_2_11_67_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2018.2831447"},{"key":"e_1_2_11_68_1","unstructured":"ZhangL ZhuG MeiL ShenP ShahSAA BennamounM. Attention in convolutional LSTM for gesture recognition. Proceedings of the 32nd International Conference on Neural Information Processing Systems.2018:1957\u20101966."},{"key":"e_1_2_11_69_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.04.014"},{"key":"e_1_2_11_70_1","doi-asserted-by":"crossref","unstructured":"LiuF ZhouX&CaoJet al. Anomaly detection in quasi\u2010periodic time series based on automatic data segmentation and attentional LSTM\u2010CNN.IEEE Trans Knowl Data Eng. 2020.https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1109\/TKDE.2020.3014806","DOI":"10.1109\/TKDE.2020.3014806"},{"key":"e_1_2_11_71_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2021.04.053"},{"key":"e_1_2_11_72_1","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1253"},{"key":"e_1_2_11_73_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41019-020-00151-z"},{"key":"e_1_2_11_74_1","doi-asserted-by":"crossref","unstructured":"WangD ZhangJ CaoW LiJ ZhengY. When will you arrive? Estimating travel time based on deep neural networks. AAAI. Vol. 18.2018:1\u20108.","DOI":"10.1609\/aaai.v32i1.11877"},{"key":"e_1_2_11_75_1","unstructured":"GalY GhahramaniZ. A theoretically grounded application of dropout in recurrent neural networks. Advances in Neural Information Processing Systems.2016:1019\u20101027."},{"key":"e_1_2_11_76_1","unstructured":"RongX. word2vec parameter learning explained. arXiv preprint arXiv:1411.2738.2014."},{"key":"e_1_2_11_77_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_2_11_78_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2582924"},{"key":"e_1_2_11_79_1","unstructured":"DozatT. Incorporating nesterov momentum into adam."},{"key":"e_1_2_11_80_1","unstructured":"GhahramaniZ HintonGE. Parameter estimation for linear dynamical systems. Tech. rep. Technical Report CRG\u2010TR\u201096\u20102 Dept. of Computer Science University of Toronto;1996."},{"key":"e_1_2_11_81_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2014.02.006"},{"key":"e_1_2_11_82_1","doi-asserted-by":"publisher","DOI":"10.3390\/s20010322"},{"key":"e_1_2_11_83_1","doi-asserted-by":"crossref","unstructured":"ShaoW ZhangY GuoB QinK ChanJ&SalimFDParking availability prediction with long short term memory model. In: Li S. ed. Green Pervasive and Cloud Computing. GPC 2018.Lecture Notes in Computer Science. Vol 11204. Springer Cham.https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/978-3-030-15093-8_9","DOI":"10.1007\/978-3-030-15093-8_9"},{"key":"e_1_2_11_84_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2868685"},{"key":"e_1_2_11_85_1","doi-asserted-by":"crossref","unstructured":"YangH LiuC ZhuangY et al.Truck parking pattern aggregation and availability prediction by deep learning.IEEE Trans Intell Transp Syst.2021.https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1109\/TITS.2021.3117290","DOI":"10.1109\/TITS.2021.3117290"}],"container-title":["International Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/int.22725","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/int.22725","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/int.22725","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T07:40:51Z","timestamp":1726126851000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/onlinelibrary.wiley.com\/doi\/10.1002\/int.22725"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,16]]},"references-count":84,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["10.1002\/int.22725"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/int.22725","archive":["Portico"],"relation":{},"ISSN":["0884-8173","1098-111X"],"issn-type":[{"value":"0884-8173","type":"print"},{"value":"1098-111X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,16]]},"assertion":[{"value":"2021-03-31","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-10-09","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-11-16","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}