{"id":"https://openalex.org/W572205035","doi":null,"title":"Real-Time Identification of Probe Vehicle Trajectories on the Mixed Corridor","display_name":"Real-Time Identification of Probe Vehicle Trajectories on the Mixed Corridor","publication_year":2014,"publication_date":"2014-01-01","ids":{"openalex":"https://openalex.org/W572205035","mag":"572205035"},"language":"en","primary_location":{"id":"mag:572205035","doi":null,"is_oa":false,"landing_page_url":"https://trid.trb.org/view/1288468","pdf_url":null,"source":{"id":"https://openalex.org/S4306533231","display_name":"Transportation Research Board 93rd Annual MeetingTransportation Research Board","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Transportation Research Board 93rd Annual MeetingTransportation Research Board","raw_type":null},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5102025158","display_name":"Yu Mei","orcid":"https://orcid.org/0000-0003-2620-3589","observed_orcids":["https://orcid.org/0000-0003-2620-3589"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu Mei","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-2620-3589","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058177475","display_name":"Keshuang Tang","orcid":"https://orcid.org/0000-0002-0476-0512","observed_orcids":["https://orcid.org/0000-0002-0476-0512"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Keshuang Tang","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-0476-0512","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5111373625","display_name":"Keping Li","orcid":"https://orcid.org/0000-0002-0754-5033","observed_orcids":["https://orcid.org/0000-0002-0754-5033"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Keping Li","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-0754-5033","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07926933,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.38960000872612,"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"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.38960000872612,"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/T10524","display_name":"Traffic control and management","score":0.2542000114917755,"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/T11106","display_name":"Data Management and Algorithms","score":0.2533999979496002,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/elevated-expressway","display_name":"elevated expressway","score":0.8889999985694885},{"id":"https://openalex.org/keywords/semi-supervised-clustering","display_name":"semi-supervised clustering","score":0.8450000286102295},{"id":"https://openalex.org/keywords/trajectory-classification","display_name":"trajectory classification","score":0.7170000076293945},{"id":"https://openalex.org/keywords/shanghai","display_name":"Shanghai","score":0.4480000138282776}],"concepts":[{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.7684329748153687},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7205676436424255},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5926792621612549},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.5478023886680603},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5220096707344055},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.48577597737312317},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.43396905064582825},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.41178059577941895},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3968479037284851},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3731735348701477},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3141847252845764},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:572205035","doi":null,"is_oa":false,"landing_page_url":"https://trid.trb.org/view/1288468","pdf_url":null,"source":{"id":"https://openalex.org/S4306533231","display_name":"Transportation Research Board 93rd Annual MeetingTransportation Research Board","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Transportation Research Board 93rd Annual MeetingTransportation Research Board","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":1.0,"display_name":"Industry, innovation and infrastructure"},{"id":"https://metadata.un.org/sdg/11","score":1.0,"display_name":"Sustainable cities and communities"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W1645703991","https://openalex.org/W3216876631","https://openalex.org/W2191956626","https://openalex.org/W1533876949","https://openalex.org/W2973046156","https://openalex.org/W2577691995","https://openalex.org/W2049178153","https://openalex.org/W3184219343","https://openalex.org/W4396659645","https://openalex.org/W1507450561"],"abstract_inverted_index":{"One":[0],"of":[1,5,18,68,117,169,230,247],"the":[2,19,22,34,39,52,56,66,78,83,110,115,121,124,144,149,167,170,192,199,203,215,225,228,241,245,248],"unique":[3],"features":[4],"urban":[6],"road":[7,42,112],"networks":[8,162],"in":[9,28,51,65,72,109,227],"China's":[10,73],"mega":[11,74],"cities":[12],"is":[13],"that":[14,191],"a":[15,62,209,218],"high":[16],"proportion":[17],"roads":[20],"are":[21,133,140],"elevated":[23],"expressways,":[24],"which":[25],"often":[26],"exist":[27],"parallel":[29],"with":[30],"and":[31,98,128,136,148,182,187,202,217,244],"close":[32],"to":[33,46,103,142,165,239],"ground":[35],"roads.":[36],"It":[37,206],"creates":[38],"so-called":[40],"mixed":[41,53,111,179],"network.":[43],"Therefore,":[44],"how":[45],"identify":[47],"probe":[48,69,106],"vehicle":[49,70,107,156],"trajectories":[50,108],"area":[54,113,180],"without":[55,114],"GPS":[57],"altitude":[58],"data":[59,157],"has":[60],"become":[61],"technical":[63,85],"problem":[64],"application":[67],"systems":[71],"cities,":[75],"particularly":[76],"for":[77,214,224],"real-time":[79],"applications.":[80],"To":[81],"overcome":[82],"above":[84],"problem,":[86],"this":[87],"paper":[88],"proposes":[89],"three":[90,171],"enhanced":[91],"semi-supervised":[92,125],"clustering":[93,150,212,219],"algorithms,":[94,123],"i.e.,":[95,185],"Constrained-K-Means,":[96],"Seeded-K-Means,":[97],"Semi-Supervised":[99],"Fuzzy":[100],"c-Means":[101],"(SFCM),":[102],"real-timely":[104],"classify":[105],"need":[116],"historical":[118,231],"data.":[119,232],"In":[120],"proposed":[122,249],"learning":[126],"technique":[127],"trajectory":[129],"topological":[130],"judgment":[131],"method":[132],"integrated":[134],"together":[135],"two":[137],"optimization":[138],"methods":[139],"developed":[141],"enhance":[143],"labeled":[145],"samples":[146],"extension":[147],"center":[151],"initialization":[152],"procedures":[153],"respectively.":[154],"Probe":[155],"collected":[158],"on":[159],"Shanghai's":[160],"expressway":[161],"was":[163,236],"used":[164],"compare":[166],"performance":[168],"algorithms":[172],"under":[173],"4":[174],"typical":[175],"cases":[176],"defined":[177],"by":[178,198],"types":[181],"time":[183],"periods,":[184],"off-peak":[186,216],"peak.":[188],"Results":[189],"indicate":[190],"SFCM":[193],"algorithm":[194,201],"performed":[195],"best,":[196],"followed":[197],"Constrained-K-Means":[200],"Seeded-K-Means":[204],"algorithm.":[205],"could":[207],"reach":[208],"nearly":[210],"100%":[211],"purity":[213,220],"greater":[221],"than":[222],"80%":[223],"peak":[226],"absence":[229],"Comprehensive":[233],"scenario":[234],"analysis":[235],"also":[237],"conducted":[238],"specify":[240],"optimal":[242],"parameters":[243],"applicability":[246],"algorithms.":[250]},"counts_by_year":[],"updated_date":"2025-10-10T17:16:08.811792","created_date":"2016-06-24T00:00:00"}
