{"id":"https://openalex.org/W7104535704","doi":"https://doi.org/10.1109/tits.2025.3626524","title":"Contrastive Learning-Based Deep Embedded Clustering and the TCN-DMAttention Model for Traffic Congestion Prediction","display_name":"Contrastive Learning-Based Deep Embedded Clustering and the TCN-DMAttention Model for Traffic Congestion Prediction","publication_year":2025,"publication_date":"2025-11-10","ids":{"openalex":"https://openalex.org/W7104535704","doi":"https://doi.org/10.1109/tits.2025.3626524"},"language":null,"primary_location":{"id":"doi:10.1109/tits.2025.3626524","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3626524","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Biao Zhang","orcid":"https://orcid.org/0000-0003-4148-8172"},"institutions":[{"id":"https://openalex.org/I196934937","display_name":"Liaocheng University","ror":"https://ror.org/03yh0n709","country_code":"CN","type":"education","lineage":["https://openalex.org/I196934937"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Biao Zhang","raw_affiliation_strings":["School of Computer Science, Liaocheng University, Liaocheng, China"],"raw_orcid":"https://orcid.org/0000-0003-4148-8172","affiliations":[{"raw_affiliation_string":"School of Computer Science, Liaocheng University, Liaocheng, China","institution_ids":["https://openalex.org/I196934937"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Haoyu Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Haoyu Zhang","raw_affiliation_strings":["City University of Hong Kong, Hong Kong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"City University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Wenlin Li","orcid":"https://orcid.org/0009-0006-4366-1660"},"institutions":[{"id":"https://openalex.org/I4210116924","display_name":"Chinese University of Hong Kong, Shenzhen","ror":"https://ror.org/02d5ks197","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633","https://openalex.org/I180726961","https://openalex.org/I4210116924"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenlin Li","raw_affiliation_strings":["School of Data Science, The Chinese University of Hong Kong, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0006-4366-1660","affiliations":[{"raw_affiliation_string":"School of Data Science, The Chinese University of Hong Kong, Shenzhen, China","institution_ids":["https://openalex.org/I4210116924"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Nisang Chen","orcid":"https://orcid.org/0009-0008-1349-2768"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nisang Chen","raw_affiliation_strings":["Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, China"],"raw_orcid":"https://orcid.org/0009-0008-1349-2768","affiliations":[{"raw_affiliation_string":"Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"last","author":{"id":null,"display_name":"Xuchu Jiang","orcid":"https://orcid.org/0000-0002-6443-9990"},"institutions":[{"id":"https://openalex.org/I158934434","display_name":"Zhongnan University of Economics and Law","ror":"https://ror.org/04yqxxq63","country_code":"CN","type":"education","lineage":["https://openalex.org/I158934434"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuchu Jiang","raw_affiliation_strings":["School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-6443-9990","affiliations":[{"raw_affiliation_string":"School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, China","institution_ids":["https://openalex.org/I158934434"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I196934937"],"apc_list":null,"apc_paid":null,"fwci":0.8988,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.80831202,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":"27","issue":"1","first_page":"1066","last_page":"1078"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9914000034332275,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9914000034332275,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10524","display_name":"Traffic control and management","score":0.00279999990016222,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.0010000000474974513,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6365000009536743},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.6265000104904175},{"id":"https://openalex.org/keywords/traffic-congestion","display_name":"Traffic congestion","score":0.5695000290870667},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5281000137329102},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4390999972820282},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4320000112056732},{"id":"https://openalex.org/keywords/discretization","display_name":"Discretization","score":0.3905999958515167},{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.3903999924659729},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.37369999289512634}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7314000129699707},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6365000009536743},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.6265000104904175},{"id":"https://openalex.org/C2779888511","wikidata":"https://www.wikidata.org/wiki/Q244156","display_name":"Traffic congestion","level":2,"score":0.5695000290870667},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5281000137329102},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4803999960422516},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47450000047683716},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4390999972820282},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4320000112056732},{"id":"https://openalex.org/C73000952","wikidata":"https://www.wikidata.org/wiki/Q17007827","display_name":"Discretization","level":2,"score":0.3905999958515167},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.3903999924659729},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.37369999289512634},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36230000853538513},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.3425999879837036},{"id":"https://openalex.org/C25492975","wikidata":"https://www.wikidata.org/wiki/Q960570","display_name":"Traffic congestion reconstruction with Kerner's three-phase theory","level":3,"score":0.3391000032424927},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.3334999978542328},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.30959999561309814},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2833000123500824},{"id":"https://openalex.org/C176715033","wikidata":"https://www.wikidata.org/wiki/Q2080768","display_name":"Traffic generation model","level":2,"score":0.2799000144004822},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2727000117301941},{"id":"https://openalex.org/C204673680","wikidata":"https://www.wikidata.org/wiki/Q1628107","display_name":"Airfield traffic pattern","level":2,"score":0.2671999931335449},{"id":"https://openalex.org/C42693407","wikidata":"https://www.wikidata.org/wiki/Q4686317","display_name":"Advanced Traffic Management System","level":3,"score":0.26179999113082886},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.2556000053882599},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.25360000133514404},{"id":"https://openalex.org/C2985695025","wikidata":"https://www.wikidata.org/wiki/Q4323994","display_name":"Road traffic","level":2,"score":0.25209999084472656},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2025.3626524","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3626524","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.701359212398529,"display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G3042819202","display_name":null,"funder_award_id":"62303204","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G376968460","display_name":null,"funder_award_id":"2023KJ206","funder_id":"https://openalex.org/F4320330199","funder_display_name":"Innovation Team Program of Hubei Province"},{"id":"https://openalex.org/G6922126741","display_name":null,"funder_award_id":"52405572","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8482834573","display_name":null,"funder_award_id":"ZR2024MF054","funder_id":"https://openalex.org/F4320324174","funder_display_name":"Natural Science Foundation of Shandong Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320324174","display_name":"Natural Science Foundation of Shandong Province","ror":null},{"id":"https://openalex.org/F4320330199","display_name":"Innovation Team Program of Hubei Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W1967444754","https://openalex.org/W1968969471","https://openalex.org/W1987789609","https://openalex.org/W2011529478","https://openalex.org/W2040870580","https://openalex.org/W2046752641","https://openalex.org/W2110485445","https://openalex.org/W2131767615","https://openalex.org/W2136132422","https://openalex.org/W2165346010","https://openalex.org/W2183679353","https://openalex.org/W2255466643","https://openalex.org/W2563738891","https://openalex.org/W2604472983","https://openalex.org/W2620217464","https://openalex.org/W2785818537","https://openalex.org/W2806382623","https://openalex.org/W2808871417","https://openalex.org/W2907492528","https://openalex.org/W2915057262","https://openalex.org/W2945177784","https://openalex.org/W2955819484","https://openalex.org/W2963285479","https://openalex.org/W2963351448","https://openalex.org/W2963608065","https://openalex.org/W2968259729","https://openalex.org/W2996847713","https://openalex.org/W3001506171","https://openalex.org/W3021810927","https://openalex.org/W3045202147","https://openalex.org/W3090823318","https://openalex.org/W3121099426","https://openalex.org/W3123191313","https://openalex.org/W3126787811","https://openalex.org/W3156636935","https://openalex.org/W3171884590","https://openalex.org/W3174022889","https://openalex.org/W3193056147","https://openalex.org/W3209643259","https://openalex.org/W4239510810","https://openalex.org/W4243150065","https://openalex.org/W4256049924","https://openalex.org/W4319586081","https://openalex.org/W4382239668","https://openalex.org/W4386212336","https://openalex.org/W4407832460","https://openalex.org/W4408146801"],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,9,43,79,88,99,103,129,136,139,149,159,191],"increasing":[2],"number":[3,184],"of":[4,11,45,82,133,138,151,165],"motor":[5],"vehicles":[6],"per":[7],"capita,":[8],"problem":[10,132],"road":[12],"traffic":[13,29,36,62,117,166],"congestion":[14,37,54,80],"is":[15,40,93,109],"becoming":[16],"increasingly":[17,25],"prominent.":[18],"Intelligent":[19],"transportation":[20,47],"systems":[21],"are":[22],"playing":[23],"an":[24,52],"important":[26,113],"role":[27],"in":[28],"management.":[30],"Establishing":[31],"a":[32,61],"reasonable":[33],"and":[34,77,102,135,143,162,177,198],"effective":[35],"prediction":[38,55],"model":[39,67,193],"crucial":[41],"for":[42,57,115],"implementation":[44],"intelligent":[46],"systems.":[48],"This":[49],"article":[50],"proposes":[51],"end-to-end":[53],"framework":[56],"unsupervised":[58],"learning.":[59],"First,":[60],"data":[63,152],"deep":[64],"embedding":[65],"clustering":[66],"based":[68],"on":[69],"contrast":[70],"learning":[71],"(CL-DEC)":[72],"was":[73],"constructed":[74],"to":[75,95,111,147],"discretize":[76],"label":[78],"status":[81],"spatiotemporal":[83],"rasterized":[84],"GPS":[85],"data.":[86,169],"Then,":[87],"Time":[89],"Convolutional":[90],"Network":[91],"(TCN)":[92],"used":[94],"extract":[96],"features":[97,114],"from":[98],"labeled":[100],"data,":[101],"Dependency":[104],"Matrix":[105],"Attention":[106],"Mechanism":[107],"(DMAttention)":[108],"combined":[110],"highlight":[112],"predicting":[116],"congestion.":[118],"In":[119,154],"this":[120],"study,":[121],"we":[122],"found":[123],"that":[124,190],"DEC":[125],"can":[126,157],"effectively":[127],"alleviate":[128],"false":[130],"positive":[131],"CL,":[134],"combination":[137],"two":[140],"improves":[141],"discriminability":[142],"comparability,":[144],"which":[145],"helps":[146],"improve":[148],"accuracy":[150],"annotation.":[153],"addition,":[155],"TCN-DMAttention":[156,192],"balance":[158],"short-term":[160],"fluctuations":[161],"long-term":[163],"trends":[164],"time":[167],"series":[168],"We":[170],"compared":[171],"it":[172],"with":[173],"13":[174],"baseline":[175],"models":[176,204],"conducted":[178],"robustness":[179],"analysis":[180],"using":[181],"20":[182],"random":[183],"seeds.":[185],"The":[186],"experimental":[187],"results":[188],"indicate":[189],"has":[194],"better":[195],"predictive":[196],"performance":[197],"good":[199],"stability":[200],"than":[201],"other":[202],"comparative":[203],"do.":[205]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-01-02T23:11:23.791532","created_date":"2025-11-10T00:00:00"}
