{"id":"https://openalex.org/W4415821150","doi":"https://doi.org/10.1109/tits.2025.3624395","title":"TriDGNet: Triple Feature Encoder-Based Dual Granularity Graph Learning Network for Enhanced Travel Time Estimation","display_name":"TriDGNet: Triple Feature Encoder-Based Dual Granularity Graph Learning Network for Enhanced Travel Time Estimation","publication_year":2025,"publication_date":"2025-11-03","ids":{"openalex":"https://openalex.org/W4415821150","doi":"https://doi.org/10.1109/tits.2025.3624395"},"language":null,"primary_location":{"id":"doi:10.1109/tits.2025.3624395","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3624395","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":"https://openalex.org/A5036596307","display_name":"Jiankai Zuo","orcid":"https://orcid.org/0000-0002-4026-134X"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiankai Zuo","raw_affiliation_strings":["Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112889087","display_name":"Yuxiang Yao","orcid":"https://orcid.org/0009-0002-7136-5819"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuxiang Yao","raw_affiliation_strings":["Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048067916","display_name":"Yaying Zhang","orcid":"https://orcid.org/0000-0001-9552-3274"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaying Zhang","raw_affiliation_strings":["Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5036596307"],"corresponding_institution_ids":["https://openalex.org/I116953780"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.34938851,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"27","issue":"1","first_page":"1606","last_page":"1620"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9290000200271606,"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":0.9290000200271606,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.014999999664723873,"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"}},{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.013799999840557575,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.6703000068664551},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.620199978351593},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5199000239372253},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.5067999958992004},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5065000057220459},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5051000118255615},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43209999799728394},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.41679999232292175},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.3946000039577484}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.703000009059906},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.6703000068664551},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.620199978351593},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5199000239372253},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.5067999958992004},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5065000057220459},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5051000118255615},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4830000102519989},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.46380001306533813},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43700000643730164},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43209999799728394},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.41679999232292175},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39980000257492065},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.3946000039577484},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3921999931335449},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.3862999975681305},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.366100013256073},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3495999872684479},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.34529998898506165},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.3425999879837036},{"id":"https://openalex.org/C2987015589","wikidata":"https://www.wikidata.org/wiki/Q1040098","display_name":"Learning network","level":2,"score":0.3154999911785126},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.3142000138759613},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.3061999976634979},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.2890999913215637},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.28850001096725464},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C176225458","wikidata":"https://www.wikidata.org/wiki/Q595971","display_name":"Graph database","level":3,"score":0.2777000069618225},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.26899999380111694},{"id":"https://openalex.org/C114809511","wikidata":"https://www.wikidata.org/wiki/Q1412924","display_name":"Flow network","level":2,"score":0.26579999923706055},{"id":"https://openalex.org/C311688","wikidata":"https://www.wikidata.org/wiki/Q2393193","display_name":"Time complexity","level":2,"score":0.2563999891281128},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.25270000100135803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2025.3624395","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3624395","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1888005072","https://openalex.org/W2123705881","https://openalex.org/W2154851992","https://openalex.org/W2393319904","https://openalex.org/W2788997482","https://openalex.org/W2809128166","https://openalex.org/W2809623940","https://openalex.org/W2910952060","https://openalex.org/W2962756421","https://openalex.org/W2976882027","https://openalex.org/W3030299187","https://openalex.org/W3080344546","https://openalex.org/W3081469395","https://openalex.org/W3098944285","https://openalex.org/W3112483519","https://openalex.org/W3168666980","https://openalex.org/W3183749634","https://openalex.org/W3189729741","https://openalex.org/W3194259208","https://openalex.org/W3198080739","https://openalex.org/W3200482837","https://openalex.org/W3202172847","https://openalex.org/W3208396733","https://openalex.org/W3216759837","https://openalex.org/W4283834908","https://openalex.org/W4291127070","https://openalex.org/W4292122507","https://openalex.org/W4293508715","https://openalex.org/W4296079221","https://openalex.org/W4311166792","https://openalex.org/W4318038574","https://openalex.org/W4367051006","https://openalex.org/W4383220217","https://openalex.org/W4383427917","https://openalex.org/W4385568084","https://openalex.org/W4393078682","https://openalex.org/W4396506123","https://openalex.org/W4400877543"],"related_works":[],"abstract_inverted_index":{"The":[0,170,198],"contemporary":[1],"urban":[2],"intelligent":[3],"transportation":[4],"system":[5],"(ITS)":[6],"generates":[7],"an":[8,17,154],"enormous":[9],"amount":[10],"of":[11,20,25,70,120],"trajectory":[12],"data":[13],"daily,":[14],"serving":[15],"as":[16],"essential":[18],"reflection":[19],"traffic":[21],"dynamics.":[22],"Accurate":[23],"estimation":[24],"arrival":[26],"time":[27,105],"by":[28],"mining":[29],"spatio-temporal":[30,118],"features":[31,49],"and":[32,55,127,140],"semantic":[33],"relationships":[34],"from":[35,122,184],"historical":[36],"trajectories":[37,121],"has":[38,191],"become":[39],"increasingly":[40],"vital.":[41],"However,":[42],"most":[43],"existing":[44],"works":[45],"overlook":[46],"the":[47,68,93,117,166],"joint":[48],"between":[50],"links":[51,64,139],"(i.e.,":[52],"road":[53,168],"segments)":[54],"crossroads":[56],"in":[57,76],"trajectories.":[58],"Additionally,":[59],"they":[60],"often":[61],"treat":[62],"all":[63],"uniformly":[65],"without":[66],"considering":[67],"semantics":[69],"critical":[71],"links,":[72],"leading":[73],"to":[74,115,136,160,179],"deficiencies":[75],"captured":[77],"representation.":[78],"To":[79],"address":[80],"these":[81],"issues,":[82],"this":[83],"study":[84],"proposes":[85],"a":[86,110,132,173],"novel":[87],"deep":[88],"encoder":[89,114],"learning":[90,113,147],"framework":[91],"called":[92],"Triple":[94],"Feature":[95],"Encoder-based":[96],"Dual-Granularity":[97],"Graph":[98],"Learning":[99],"Network":[100],"(TriDGNet)":[101],"for":[102],"enhanced":[103],"travel":[104],"estimation.":[106],"Specifically,":[107],"we":[108,130,143],"design":[109],"triple":[111],"feature":[112],"explore":[116],"correlations":[119],"three":[123,195],"perspectives:":[124],"Depth,":[125],"Ensemble,":[126],"Sequence.":[128],"Meanwhile,":[129],"introduce":[131],"consistent":[133],"modeling":[134],"method":[135],"integrate":[137],"both":[138],"crossroads.":[141],"Furthermore,":[142],"construct":[144],"two":[145],"graph":[146,156],"modules":[148],"at":[149],"different":[150],"scales.":[151],"One":[152],"is":[153,172],"edge-enhanced":[155],"attention":[157],"network":[158,177],"(E-GAT)":[159],"capture":[161],"global":[162],"spatial":[163],"dependencies":[164],"across":[165],"entire":[167],"network.":[169],"other":[171],"backtracking-based":[174],"subgraph":[175],"representation":[176],"(BackNet)":[178],"learn":[180],"local":[181],"contextual":[182],"information":[183],"bustling":[185],"links.":[186],"Our":[187],"proposed":[188],"TriDGNet":[189],"model":[190],"been":[192],"evaluated":[193],"on":[194],"extensive":[196],"datasets.":[197],"experimental":[199],"results":[200],"demonstrate":[201],"that":[202],"it":[203],"outperforms":[204],"state-of-the-art":[205],"approaches.":[206]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-11-03T00:00:00"}
