{"id":"https://openalex.org/W2990509288","doi":"https://doi.org/10.1109/itsc.2019.8917320","title":"Trajectory clustering of road traffic in urban environments using incremental machine learning in combination with hyperdimensional computing","display_name":"Trajectory clustering of road traffic in urban environments using incremental machine learning in combination with hyperdimensional computing","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W2990509288","doi":"https://doi.org/10.1109/itsc.2019.8917320","mag":"2990509288"},"language":"en","primary_location":{"id":"doi:10.1109/itsc.2019.8917320","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2019.8917320","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","raw_type":"proceedings-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/A5039314699","display_name":"Tharindu Bandaragoda","orcid":"https://orcid.org/0000-0001-5047-3496"},"institutions":[{"id":"https://openalex.org/I196829312","display_name":"La Trobe University","ror":"https://ror.org/01rxfrp27","country_code":"AU","type":"education","lineage":["https://openalex.org/I196829312"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Tharindu Bandaragoda","raw_affiliation_strings":["Research Centre for Data Analytics and Cognition, La Trobe University, Melbourne, Australia"],"affiliations":[{"raw_affiliation_string":"Research Centre for Data Analytics and Cognition, La Trobe University, Melbourne, Australia","institution_ids":["https://openalex.org/I196829312"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064845769","display_name":"Daswin De Silva","orcid":"https://orcid.org/0000-0003-3878-5969"},"institutions":[{"id":"https://openalex.org/I196829312","display_name":"La Trobe University","ror":"https://ror.org/01rxfrp27","country_code":"AU","type":"education","lineage":["https://openalex.org/I196829312"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Daswin De Silva","raw_affiliation_strings":["Research Centre for Data Analytics and Cognition, La Trobe University, Melbourne, Australia"],"affiliations":[{"raw_affiliation_string":"Research Centre for Data Analytics and Cognition, La Trobe University, Melbourne, Australia","institution_ids":["https://openalex.org/I196829312"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065925392","display_name":"Denis Kleyko","orcid":"https://orcid.org/0000-0002-6032-6155"},"institutions":[{"id":"https://openalex.org/I190632392","display_name":"Lule\u00e5 University of Technology","ror":"https://ror.org/016st3p78","country_code":"SE","type":"education","lineage":["https://openalex.org/I190632392"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Denis Kleyko","raw_affiliation_strings":["Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology Lulea, Sweden"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology Lulea, Sweden","institution_ids":["https://openalex.org/I190632392"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090043052","display_name":"Evgeny Osipov","orcid":"https://orcid.org/0000-0003-0069-640X"},"institutions":[{"id":"https://openalex.org/I190632392","display_name":"Lule\u00e5 University of Technology","ror":"https://ror.org/016st3p78","country_code":"SE","type":"education","lineage":["https://openalex.org/I190632392"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Evgeny Osipov","raw_affiliation_strings":["Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology Lulea, Sweden"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology Lulea, Sweden","institution_ids":["https://openalex.org/I190632392"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011403654","display_name":"Urban Wiklund","orcid":"https://orcid.org/0000-0002-1313-0934"},"institutions":[{"id":"https://openalex.org/I90267481","display_name":"Ume\u00e5 University","ror":"https://ror.org/05kb8h459","country_code":"SE","type":"education","lineage":["https://openalex.org/I90267481"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Urban Wiklund","raw_affiliation_strings":["Umea University, Umea, Sweden"],"affiliations":[{"raw_affiliation_string":"Umea University, Umea, Sweden","institution_ids":["https://openalex.org/I90267481"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008490552","display_name":"Damminda Alahakoon","orcid":"https://orcid.org/0000-0003-3291-888X"},"institutions":[{"id":"https://openalex.org/I196829312","display_name":"La Trobe University","ror":"https://ror.org/01rxfrp27","country_code":"AU","type":"education","lineage":["https://openalex.org/I196829312"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Damminda Alahakoon","raw_affiliation_strings":["Research Centre for Data Analytics and Cognition, La Trobe University, Melbourne, Australia"],"affiliations":[{"raw_affiliation_string":"Research Centre for Data Analytics and Cognition, La Trobe University, Melbourne, Australia","institution_ids":["https://openalex.org/I196829312"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5039314699"],"corresponding_institution_ids":["https://openalex.org/I196829312"],"apc_list":null,"apc_paid":null,"fwci":3.8041,"has_fulltext":false,"cited_by_count":38,"citation_normalized_percentile":{"value":0.92874037,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1664","last_page":"1670"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9940999746322632,"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/T11106","display_name":"Data Management and Algorithms","score":0.983299970626831,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/profiling","display_name":"Profiling (computer programming)","score":0.782075047492981},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7268061637878418},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6796635389328003},{"id":"https://openalex.org/keywords/traffic-congestion","display_name":"Traffic congestion","score":0.5989432334899902},{"id":"https://openalex.org/keywords/urban-computing","display_name":"Urban computing","score":0.5897772908210754},{"id":"https://openalex.org/keywords/identifier","display_name":"Identifier","score":0.5400517582893372},{"id":"https://openalex.org/keywords/traffic-congestion-reconstruction-with-kerners-three-phase-theory","display_name":"Traffic congestion reconstruction with Kerner's three-phase theory","score":0.46500974893569946},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.46403053402900696},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4255271255970001},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.42430901527404785},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.3842880427837372},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3840351104736328},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.23109644651412964},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1425156593322754}],"concepts":[{"id":"https://openalex.org/C187191949","wikidata":"https://www.wikidata.org/wiki/Q1138496","display_name":"Profiling (computer programming)","level":2,"score":0.782075047492981},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7268061637878418},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6796635389328003},{"id":"https://openalex.org/C2779888511","wikidata":"https://www.wikidata.org/wiki/Q244156","display_name":"Traffic congestion","level":2,"score":0.5989432334899902},{"id":"https://openalex.org/C2778459138","wikidata":"https://www.wikidata.org/wiki/Q7900107","display_name":"Urban computing","level":2,"score":0.5897772908210754},{"id":"https://openalex.org/C154504017","wikidata":"https://www.wikidata.org/wiki/Q853614","display_name":"Identifier","level":2,"score":0.5400517582893372},{"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":0.46500974893569946},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.46403053402900696},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4255271255970001},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.42430901527404785},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.3842880427837372},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3840351104736328},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.23109644651412964},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1425156593322754},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc.2019.8917320","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2019.8917320","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.5600000023841858}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W252542266","https://openalex.org/W1610291649","https://openalex.org/W1673310716","https://openalex.org/W1801809520","https://openalex.org/W1941758531","https://openalex.org/W1976755580","https://openalex.org/W1981398125","https://openalex.org/W1989491491","https://openalex.org/W2004353783","https://openalex.org/W2032131451","https://openalex.org/W2036785686","https://openalex.org/W2062103413","https://openalex.org/W2068074762","https://openalex.org/W2070862086","https://openalex.org/W2091168119","https://openalex.org/W2106100548","https://openalex.org/W2127292806","https://openalex.org/W2148238513","https://openalex.org/W2161863692","https://openalex.org/W2164223054","https://openalex.org/W2168701898","https://openalex.org/W2217607432","https://openalex.org/W2263031682","https://openalex.org/W2277132981","https://openalex.org/W2277710627","https://openalex.org/W2463743813","https://openalex.org/W2562609490","https://openalex.org/W2575125657","https://openalex.org/W2596628535","https://openalex.org/W2701877577","https://openalex.org/W2731510061","https://openalex.org/W2734775449","https://openalex.org/W2773696957","https://openalex.org/W2775120828","https://openalex.org/W2796260258","https://openalex.org/W2799109291","https://openalex.org/W2805089611","https://openalex.org/W2805904203","https://openalex.org/W2950268605","https://openalex.org/W2960280836","https://openalex.org/W3124315448","https://openalex.org/W6637131181","https://openalex.org/W6683941694","https://openalex.org/W6910478492"],"related_works":["https://openalex.org/W4378651134","https://openalex.org/W4252684102","https://openalex.org/W2352307597","https://openalex.org/W1979633005","https://openalex.org/W2163724607","https://openalex.org/W3135403405","https://openalex.org/W1980092392","https://openalex.org/W2608983118","https://openalex.org/W37347591","https://openalex.org/W3203695246"],"abstract_inverted_index":{"Road":[0],"traffic":[1,25,55,59,74,86],"congestion":[2,26,87],"in":[3,73,168],"urban":[4],"environments":[5],"poses":[6],"an":[7],"increasingly":[8],"complex":[9],"challenge":[10],"of":[11,48,58,105,147,163,171],"detection,":[12],"profiling":[13,57,75],"and":[14,22,30,41,89,114,141],"prediction.":[15],"Although":[16,44],"public":[17],"policy":[18],"promotes":[19],"transport":[20],"alternatives":[21],"new":[23],"infrastructure,":[24],"is":[27],"highly":[28],"prevalent":[29],"continues":[31],"to":[32,100],"be":[33,123],"the":[34,129,161,164,169],"lead":[35],"cause":[36],"for":[37,84],"numerous":[38],"social,":[39],"economic":[40],"environmental":[42],"issues.":[43],"a":[45,78,144],"significant":[46],"volume":[47],"research":[49],"has":[50,60],"been":[51],"reported":[52],"on":[53,143],"road":[54,85,166],"prediction,":[56],"received":[61],"much":[62],"less":[63],"attention.":[64],"In":[65],"this":[66],"paper":[67],"we":[68],"address":[69],"two":[70],"key":[71],"problems":[72],"by":[76,128],"proposing":[77],"novel":[79],"unsupervised":[80],"incremental":[81],"learning":[82],"approach":[83,95,138],"detection":[88],"profiling,":[90],"dynamically":[91],"over":[92,126],"time.":[93],"This":[94],"uses":[96],"(a)":[97],"hyperdimensional":[98],"computing":[99],"enable":[101],"capture":[102],"variable-length":[103],"trajectories":[104],"commuter":[106],"trips":[107],"represented":[108],"as":[109],"vehicular":[110,151],"movement":[111,152],"across":[112],"intersections,":[113],"(b)":[115],"transforms":[116],"these":[117],"into":[118],"feature":[119],"vectors":[120],"that":[121],"can":[122],"incrementally":[124],"learned":[125],"time":[127],"Incremental":[130],"Knowledge":[131],"Acquiring":[132],"Self-Learning":[133],"(IKASL)":[134],"algorithm.":[135],"The":[136],"proposed":[137],"was":[139],"tested":[140],"evaluated":[142],"dataset":[145],"consisting":[146],"approximately":[148],"190":[149],"million":[150],"records":[153],"obtained":[154],"from":[155],"1,400":[156],"Bluetooth":[157],"identifiers":[158],"placed":[159],"at":[160],"intersections":[162],"arterial":[165],"network":[167],"State":[170],"Victoria,":[172],"Australia.":[173]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":7}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
