{"id":"https://openalex.org/W2597764780","doi":"https://doi.org/10.1109/vtcfall.2016.7881010","title":"Exploiting Taxi Demand Hotspots Based on Vehicular Big Data Analytics","display_name":"Exploiting Taxi Demand Hotspots Based on Vehicular Big Data Analytics","publication_year":2016,"publication_date":"2016-09-01","ids":{"openalex":"https://openalex.org/W2597764780","doi":"https://doi.org/10.1109/vtcfall.2016.7881010","mag":"2597764780"},"language":"en","primary_location":{"id":"doi:10.1109/vtcfall.2016.7881010","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtcfall.2016.7881010","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 84th Vehicular Technology Conference (VTC-Fall)","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/A5100388671","display_name":"Lu Zhang","orcid":"https://orcid.org/0000-0002-8972-8799"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Lu Zhang","raw_affiliation_strings":["Department of Automation, Key Laboratory of System Control and Information Processing, Shanghai, P.R. China"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Key Laboratory of System Control and Information Processing, Shanghai, P.R. China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038267080","display_name":"Cailian Chen","orcid":"https://orcid.org/0000-0001-6533-8713"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cailian Chen","raw_affiliation_strings":["Department of Automation, Key Laboratory of System Control and Information Processing, Shanghai, P.R. China"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Key Laboratory of System Control and Information Processing, Shanghai, P.R. China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038563534","display_name":"Yiyin Wang","orcid":"https://orcid.org/0000-0002-4464-6589"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yiyin Wang","raw_affiliation_strings":["Department of Automation, Key Laboratory of System Control and Information Processing, Shanghai, P.R. China"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Key Laboratory of System Control and Information Processing, Shanghai, P.R. China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100690710","display_name":"Xinping Guan","orcid":"https://orcid.org/0009-0006-6233-8762"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xinping Guan","raw_affiliation_strings":["Department of Automation, Key Laboratory of System Control and Information Processing, Shanghai, P.R. China"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Key Laboratory of System Control and Information Processing, Shanghai, P.R. China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100388671"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":7.3607,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.96249649,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9994999766349792,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9994999766349792,"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9993000030517578,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9991999864578247,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/dbscan","display_name":"DBSCAN","score":0.9719029068946838},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.824373722076416},{"id":"https://openalex.org/keywords/taxis","display_name":"Taxis","score":0.6657166481018066},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6557492017745972},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.548635721206665},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5342345237731934},{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.5218221545219421},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.44307559728622437},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.44083407521247864},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1751154363155365},{"id":"https://openalex.org/keywords/fuzzy-clustering","display_name":"Fuzzy clustering","score":0.1626778244972229},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.1606035828590393},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07353368401527405}],"concepts":[{"id":"https://openalex.org/C46576248","wikidata":"https://www.wikidata.org/wiki/Q1114630","display_name":"DBSCAN","level":5,"score":0.9719029068946838},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.824373722076416},{"id":"https://openalex.org/C183373512","wikidata":"https://www.wikidata.org/wiki/Q949618","display_name":"Taxis","level":2,"score":0.6657166481018066},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6557492017745972},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.548635721206665},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5342345237731934},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.5218221545219421},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.44307559728622437},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.44083407521247864},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1751154363155365},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.1626778244972229},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.1606035828590393},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07353368401527405},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vtcfall.2016.7881010","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtcfall.2016.7881010","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 84th Vehicular Technology Conference (VTC-Fall)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.5199999809265137,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320334924","display_name":"Program for New Century Excellent Talents in University","ror":"https://ror.org/01mv9t934"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1948764664","https://openalex.org/W1969454839","https://openalex.org/W1988580225","https://openalex.org/W1992181154","https://openalex.org/W2008286666","https://openalex.org/W2018865324","https://openalex.org/W2023596329","https://openalex.org/W2079838248","https://openalex.org/W2539190955"],"related_works":["https://openalex.org/W2731640799","https://openalex.org/W3145095895","https://openalex.org/W2594548639","https://openalex.org/W4387544810","https://openalex.org/W2978498151","https://openalex.org/W2782837293","https://openalex.org/W1946755446","https://openalex.org/W2388377527","https://openalex.org/W565532978","https://openalex.org/W2114323843"],"abstract_inverted_index":{"In":[0,85],"the":[1,5,12,18,22,28,37,70,111],"urban":[2],"transportation":[3],"system,":[4],"unbalanced":[6],"relationship":[7],"between":[8],"taxi":[9,38],"demand":[10,39,67],"and":[11,21,97,125],"number":[13],"of":[14,24,30,41,58,114,122,143],"running":[15],"taxis":[16],"reduces":[17],"drivers'":[19],"income":[20],"levels":[23],"passengers'":[25],"satisfaction.":[26],"With":[27],"help":[29],"vehicular":[31],"global":[32],"positioning":[33],"system":[34],"(GPS)":[35],"data,":[36],"distribution":[40],"city":[42],"can":[43,119],"be":[44],"analyzed":[45],"to":[46,109,128],"provide":[47],"advice":[48],"for":[49,65,99],"drivers.":[50],"A":[51],"clustering":[52,93],"algorithm":[53,94,118],"called":[54,95],"Density-Based":[55],"Spatial":[56],"Clustering":[57],"Applications":[59],"with":[60,106,150],"Noise":[61],"(DBSCAN)":[62],"is":[63,73,80,137],"suitable":[64],"discovering":[66],"hotspots.":[68],"However,":[69],"execution":[71],"efficiency":[72],"still":[74],"a":[75],"big":[76,83],"challenge":[77],"when":[78],"DBSCAN":[79,100,136],"applied":[81],"on":[82],"databases.":[84],"this":[86,117],"paper,":[87],"we":[88],"propose":[89],"an":[90,141],"improved":[91],"density-based":[92],"Grid":[96],"Kd-tree":[98],"(GD-DBSCAN),":[101],"which":[102],"integrates":[103],"partitioning":[104],"method":[105],"kd-tree":[107],"structure":[108],"improve":[110],"computational":[112],"performance":[113,148],"DBSCAN.":[115,151],"Furthermore,":[116],"take":[120],"advantages":[121],"multi":[123],"cores":[124],"shared":[126],"memory":[127],"parallelize":[129],"related":[130],"functions.":[131],"The":[132],"experiment":[133],"shows":[134],"GD-":[135],"efficient,":[138],"it":[139],"has":[140],"improvement":[142],"at":[144],"least":[145],"10%":[146],"in":[147],"compared":[149]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}