{"id":"https://openalex.org/W2577691995","doi":"https://doi.org/10.1109/tkde.2017.2652454","title":"A Systematic Approach to Clustering Whole Trajectories of Mobile Objects in Road Networks","display_name":"A Systematic Approach to Clustering Whole Trajectories of Mobile Objects in Road Networks","publication_year":2017,"publication_date":"2017-01-17","ids":{"openalex":"https://openalex.org/W2577691995","doi":"https://doi.org/10.1109/tkde.2017.2652454","mag":"2577691995"},"language":"en","primary_location":{"id":"doi:10.1109/tkde.2017.2652454","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2017.2652454","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Knowledge and Data Engineering","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/A5110468479","display_name":"Binh Han","orcid":null},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Binh Han","raw_affiliation_strings":["College of Computing, Georgia Institute of Technology, Atlanta, GA"],"affiliations":[{"raw_affiliation_string":"College of Computing, Georgia Institute of Technology, Atlanta, GA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100343991","display_name":"Ling Liu","orcid":"https://orcid.org/0000-0002-4138-3082"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ling Liu","raw_affiliation_strings":["College of Computing, Georgia Institute of Technology, Atlanta, GA"],"affiliations":[{"raw_affiliation_string":"College of Computing, Georgia Institute of Technology, Atlanta, GA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089324850","display_name":"Edward Omiecinski","orcid":null},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Edward Omiecinski","raw_affiliation_strings":["College of Computing, Georgia Institute of Technology, Atlanta, GA"],"affiliations":[{"raw_affiliation_string":"College of Computing, Georgia Institute of Technology, Atlanta, GA","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5110468479"],"corresponding_institution_ids":["https://openalex.org/I130701444"],"apc_list":null,"apc_paid":null,"fwci":2.0342,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":{"value":0.8762851,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"29","issue":"5","first_page":"936","last_page":"949"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9958999752998352,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9945999979972839,"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/cluster-analysis","display_name":"Cluster analysis","score":0.888559103012085},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7279525995254517},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6130902171134949},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5951768755912781},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5677374601364136},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.45571470260620117},{"id":"https://openalex.org/keywords/data-stream-clustering","display_name":"Data stream clustering","score":0.4510743021965027},{"id":"https://openalex.org/keywords/euclidean-space","display_name":"Euclidean space","score":0.43826720118522644},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.4380197525024414},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.4206976890563965},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4079459607601166},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.3781154751777649},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22831091284751892}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.888559103012085},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7279525995254517},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6130902171134949},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5951768755912781},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5677374601364136},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.45571470260620117},{"id":"https://openalex.org/C193143536","wikidata":"https://www.wikidata.org/wiki/Q5227360","display_name":"Data stream clustering","level":5,"score":0.4510743021965027},{"id":"https://openalex.org/C186450821","wikidata":"https://www.wikidata.org/wiki/Q17295","display_name":"Euclidean space","level":2,"score":0.43826720118522644},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.4380197525024414},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.4206976890563965},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4079459607601166},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.3781154751777649},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22831091284751892},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"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/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tkde.2017.2652454","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2017.2652454","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Knowledge and Data Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.7699999809265137,"display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G2036419391","display_name":null,"funder_award_id":"1115375","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W87092222","https://openalex.org/W131856359","https://openalex.org/W1502063784","https://openalex.org/W1533876949","https://openalex.org/W1589877257","https://openalex.org/W1597504361","https://openalex.org/W1864972570","https://openalex.org/W1981398125","https://openalex.org/W1987971958","https://openalex.org/W2004700538","https://openalex.org/W2006686534","https://openalex.org/W2009752994","https://openalex.org/W2047216628","https://openalex.org/W2067193733","https://openalex.org/W2073459066","https://openalex.org/W2099302229","https://openalex.org/W2114004713","https://openalex.org/W2117379916","https://openalex.org/W2118371392","https://openalex.org/W2126194848","https://openalex.org/W2133184712","https://openalex.org/W2147880780","https://openalex.org/W2148380989","https://openalex.org/W2149173084","https://openalex.org/W2154648702","https://openalex.org/W2155919101","https://openalex.org/W2166370472","https://openalex.org/W2167081989","https://openalex.org/W2325963232","https://openalex.org/W3015962962","https://openalex.org/W6603586546","https://openalex.org/W6639107604","https://openalex.org/W6668990524","https://openalex.org/W6677366547","https://openalex.org/W6683353320"],"related_works":["https://openalex.org/W2491448268","https://openalex.org/W2559422900","https://openalex.org/W2892323093","https://openalex.org/W3144143113","https://openalex.org/W2394193399","https://openalex.org/W2181939267","https://openalex.org/W2390610678","https://openalex.org/W3071522575","https://openalex.org/W2363054820","https://openalex.org/W2160785859"],"abstract_inverted_index":{"Most":[0],"of":[1,34,78,195,213],"mobile":[2,35,140],"object":[3,141],"trajectory":[4,21,45],"clustering":[5,13,31,46,136,152,171,204],"analysis":[6],"to":[7,30,132,149],"date":[8],"has":[9,48],"been":[10],"focused":[11],"on":[12,190],"the":[14,59,70,75,92,124,135,144,150,170,175,180],"location":[15],"points":[16,113],"or":[17],"sub-trajectories":[18],"extracted":[19],"from":[20],"data.":[22],"This":[23,128],"paper":[24],"presents":[25],"TraceMob,":[26],"a":[27,43,55,81,106,115,158,164],"systematic":[28],"approach":[29],"whole":[32,44,63,82,103,139],"trajectories":[33,79,104,142,188],"objects":[36],"traveling":[37],"in":[38,91,105,114,123,143,157,173],"road":[39,93,107,146,181,192],"networks.":[40],"TraceMob":[41,200],"as":[42,80],"framework":[47],"three":[49],"unique":[50],"features.":[51],"First,":[52],"we":[53,67,97,162],"design":[54],"quality":[56,172,203],"measure":[57,72],"for":[58,138,154,168],"distance":[60,71],"between":[61],"two":[62],"trajectories.":[64],"By":[65],"quality,":[66],"mean":[68],"that":[69,101,199],"can":[73],"capture":[74],"complex":[76,145],"characteristics":[77],"including":[83],"their":[84,88,120],"varying":[85],"lengths":[86],"and":[87,179,206],"constrained":[89],"movement":[90],"network":[94,108,147,182,193],"space.":[95,127,160,183],"Second,":[96],"develop":[98,163],"an":[99,211],"algorithm":[100],"transforms":[102],"space":[109,117,148,178],"into":[110],"multidimensional":[111,155],"data":[112,156],"euclidean":[116,159],"while":[118],"preserving":[119],"relative":[121],"distances":[122],"transformed":[125,176],"metric":[126,177],"transformation":[129],"enables":[130],"us":[131],"effectively":[133],"shift":[134],"task":[137,153],"traditional":[151],"Third,":[161],"cluster":[165],"validation":[166],"method":[167],"evaluating":[169],"both":[174],"Extensive":[184],"experimental":[185],"evaluation":[186],"with":[187],"generated":[189],"real":[191],"maps":[194],"different":[196],"cities":[197],"shows":[198],"produces":[201],"higher":[202],"results":[205],"outperforms":[207],"existing":[208],"approaches":[209],"by":[210],"order":[212],"magnitude.":[214]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}