{"id":"https://openalex.org/W2985685076","doi":"https://doi.org/10.1109/access.2019.2952359","title":"A Multi-Index Fusion Clustering Strategy for Traffic Flow State Identification","display_name":"A Multi-Index Fusion Clustering Strategy for Traffic Flow State Identification","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2985685076","doi":"https://doi.org/10.1109/access.2019.2952359","mag":"2985685076"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2952359","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2952359","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08894353.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08894353.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101815217","display_name":"Di Bao","orcid":"https://orcid.org/0000-0001-7370-5907"},"institutions":[{"id":"https://openalex.org/I24407930","display_name":"Hunan University of Science and Engineering","ror":"https://ror.org/04ymz0q33","country_code":"CN","type":"education","lineage":["https://openalex.org/I24407930"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Di Bao","raw_affiliation_strings":["School of Economics and Management, Hunan University of Science and Engineering, Yongzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Economics and Management, Hunan University of Science and Engineering, Yongzhou, China","institution_ids":["https://openalex.org/I24407930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5101815217"],"corresponding_institution_ids":["https://openalex.org/I24407930"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.951,"has_fulltext":true,"cited_by_count":12,"citation_normalized_percentile":{"value":0.76675399,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"7","issue":null,"first_page":"166404","last_page":"166409"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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/T10524","display_name":"Traffic control and management","score":0.9987999796867371,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9918000102043152,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.8197120428085327},{"id":"https://openalex.org/keywords/traffic-flow","display_name":"Traffic flow (computer networking)","score":0.6518670916557312},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5957362651824951},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5332289338111877},{"id":"https://openalex.org/keywords/fuzzy-clustering","display_name":"Fuzzy clustering","score":0.4760006368160248},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4737568497657776},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.4723342955112457},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4500560760498047},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.4139365553855896},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33130162954330444},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.30539947748184204}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8197120428085327},{"id":"https://openalex.org/C207512268","wikidata":"https://www.wikidata.org/wiki/Q3074551","display_name":"Traffic flow (computer networking)","level":2,"score":0.6518670916557312},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5957362651824951},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5332289338111877},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.4760006368160248},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4737568497657776},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.4723342955112457},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4500560760498047},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.4139365553855896},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33130162954330444},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30539947748184204},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","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},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2019.2952359","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2952359","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08894353.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:2f1b30103825451d88ded37ff4a722b3","is_oa":true,"landing_page_url":"https://doaj.org/article/2f1b30103825451d88ded37ff4a722b3","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 7, Pp 166404-166409 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2952359","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2952359","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8600701/08894353.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2985685076.pdf","grobid_xml":"https://content.openalex.org/works/W2985685076.grobid-xml"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W128075908","https://openalex.org/W1963691321","https://openalex.org/W1966188721","https://openalex.org/W1967618364","https://openalex.org/W1974157418","https://openalex.org/W1997127115","https://openalex.org/W2000034425","https://openalex.org/W2024603446","https://openalex.org/W2035126393","https://openalex.org/W2047098977","https://openalex.org/W2055130908","https://openalex.org/W2082474358","https://openalex.org/W2132140174","https://openalex.org/W2167588718","https://openalex.org/W2360964400","https://openalex.org/W2383618946","https://openalex.org/W2577074172","https://openalex.org/W2748471238","https://openalex.org/W2790765873","https://openalex.org/W2886978258","https://openalex.org/W2887160159","https://openalex.org/W2911547100","https://openalex.org/W6605119144","https://openalex.org/W7055398752"],"related_works":["https://openalex.org/W2945382830","https://openalex.org/W4224807364","https://openalex.org/W2596632494","https://openalex.org/W2535986621","https://openalex.org/W1980197432","https://openalex.org/W2382432689","https://openalex.org/W2000612978","https://openalex.org/W4388110928","https://openalex.org/W1483228865","https://openalex.org/W4312412183"],"abstract_inverted_index":{"This":[0,168],"paper":[1],"attempts":[2],"to":[3,75,90,124,166],"improve":[4],"the":[5,14,21,45,49,52,64,77,92,98,108,114,119,125,131,153,158,171,177,181,187],"identification":[6,22,161],"accuracy":[7,162],"of":[8,16,51,60,67,82,110,138,176,180,190],"traffic":[9,86,100,147,159,191],"flow":[10,87,101,148,160,192],"states,":[11],"and":[12,40,89],"disclose":[13],"impacts":[15],"different":[17,85],"evaluation":[18,46,182],"indices":[19,54],"on":[20],"results.":[23],"To":[24],"this":[25,35],"end,":[26],"a":[27,58,68],"multi-index":[28,132],"fusion":[29,133],"clustering":[30,134,189],"strategy":[31],"is":[32,169],"proposed":[33],"in":[34],"research.":[36],"Firstly,":[37],"flow,":[38],"velocity":[39],"occupancy":[41],"were":[42,55,122],"selected":[43],"as":[44],"indices.":[47],"Then,":[48,118],"weights":[50,121],"three":[53],"initialized":[56],"by":[57,107],"group":[59],"experts.":[61],"After":[62],"that,":[63],"objective":[65],"function":[66],"weight":[69,116],"optimization":[70],"model":[71,104],"was":[72,105],"set":[73],"up":[74],"maximize":[76],"distance":[78],"between":[79,95,146],"projection":[80,93],"centers":[81],"samples":[83,96],"under":[84,97],"states":[88,149],"minimize":[91],"variance":[94],"same":[99],"state.":[102],"The":[103,136],"solved":[106],"method":[109,144,173],"Lagrange":[111],"multipliers,":[112],"producing":[113],"optimal":[115,120],"combination.":[117],"introduced":[123],"fuzzy":[126],"c-means":[127],"(FCM)":[128],"clustering,":[129],"forming":[130],"method.":[135],"results":[137],"example":[139],"analysis":[140],"show":[141],"that":[142],"our":[143],"differentiated":[145],"more":[150],"accurately":[151],"than":[152],"original":[154,178],"FCM":[155],"clustering.":[156],"And":[157],"improved":[163,172],"from":[164],"94.0%":[165],"96.6%.":[167],"because":[170],"retains":[174],"most":[175],"features":[179],"indices,":[183],"which":[184],"further":[185],"facilitates":[186],"accurate":[188],"states.":[193]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}