{"id":"https://openalex.org/W3124035017","doi":"https://doi.org/10.1109/globecom42002.2020.9322255","title":"Root Cause Identification for Road Network Congestion Using the Gradient Boosting Decision Trees","display_name":"Root Cause Identification for Road Network Congestion Using the Gradient Boosting Decision Trees","publication_year":2020,"publication_date":"2020-12-01","ids":{"openalex":"https://openalex.org/W3124035017","doi":"https://doi.org/10.1109/globecom42002.2020.9322255","mag":"3124035017"},"language":"en","primary_location":{"id":"doi:10.1109/globecom42002.2020.9322255","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom42002.2020.9322255","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100454817","display_name":"Yue Chen","orcid":"https://orcid.org/0000-0002-5079-0579"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Chen","raw_affiliation_strings":["State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, Shaanxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015340798","display_name":"Changle Li","orcid":"https://orcid.org/0000-0003-2568-8908"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changle Li","raw_affiliation_strings":["State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, Shaanxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034502293","display_name":"Wenwei Yue","orcid":"https://orcid.org/0000-0002-1890-5911"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenwei Yue","raw_affiliation_strings":["State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, Shaanxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101540880","display_name":"Hehe Zhang","orcid":"https://orcid.org/0000-0001-8309-5487"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hehe Zhang","raw_affiliation_strings":["State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, Shaanxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036000787","display_name":"Guoqiang Mao","orcid":"https://orcid.org/0000-0002-3598-4949"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoqiang Mao","raw_affiliation_strings":["State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, Shaanxi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":0.3945,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.60259594,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"01","last_page":"06"},"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/T10524","display_name":"Traffic control and management","score":0.9936000108718872,"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9901999831199646,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6734532117843628},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.633715808391571},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.596640408039093},{"id":"https://openalex.org/keywords/root-cause","display_name":"Root cause","score":0.5789387822151184},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5738019943237305},{"id":"https://openalex.org/keywords/traffic-congestion","display_name":"Traffic congestion","score":0.5504792332649231},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.5118228793144226},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.44243040680885315},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36287838220596313},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.27182307839393616},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.22918999195098877},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12396752834320068},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.10028529167175293}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6734532117843628},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.633715808391571},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.596640408039093},{"id":"https://openalex.org/C84945661","wikidata":"https://www.wikidata.org/wiki/Q7366567","display_name":"Root cause","level":2,"score":0.5789387822151184},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5738019943237305},{"id":"https://openalex.org/C2779888511","wikidata":"https://www.wikidata.org/wiki/Q244156","display_name":"Traffic congestion","level":2,"score":0.5504792332649231},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.5118228793144226},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.44243040680885315},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36287838220596313},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.27182307839393616},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.22918999195098877},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12396752834320068},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.10028529167175293},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globecom42002.2020.9322255","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom42002.2020.9322255","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2020 - 2020 IEEE Global Communications Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.8199999928474426,"display_name":"Sustainable cities and communities"}],"awards":[{"id":"https://openalex.org/G4905520761","display_name":null,"funder_award_id":"U1801266","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1554944419","https://openalex.org/W1594031697","https://openalex.org/W1678356000","https://openalex.org/W2029657050","https://openalex.org/W2070493638","https://openalex.org/W2085569158","https://openalex.org/W2106100548","https://openalex.org/W2167109052","https://openalex.org/W2169037497","https://openalex.org/W2171033594","https://openalex.org/W2177299793","https://openalex.org/W2472245890","https://openalex.org/W2529041510","https://openalex.org/W2567881713","https://openalex.org/W2954494060","https://openalex.org/W3011019592","https://openalex.org/W3047778044"],"related_works":["https://openalex.org/W2967733078","https://openalex.org/W3204430031","https://openalex.org/W3137904399","https://openalex.org/W4310492845","https://openalex.org/W2885778889","https://openalex.org/W4310224730","https://openalex.org/W4289703016","https://openalex.org/W2885516856","https://openalex.org/W3094138326","https://openalex.org/W1968832299"],"abstract_inverted_index":{"Identifying":[0],"the":[1,10,32,48,65,74,119,126,140,147],"root":[2,43,66,141],"cause":[3,67],"in":[4,36,133],"urban":[5],"road":[6,38,69,99],"networks":[7],"and":[8,72,94,117,138,157],"ranking":[9],"influential":[11,75,151],"factors":[12,76],"can":[13,129],"benefit":[14],"traffic":[15,19,29,103],"management":[16],"for":[17],"improving":[18],"condition.":[20],"Traditional":[21],"congestion":[22,71,144],"identification":[23],"studies":[24],"paid":[25],"attention":[26],"to":[27,47,63,115],"identify":[28,64,139],"bottlenecks,":[30],"namely":[31],"most":[33],"vulnerable":[34],"points":[35],"a":[37,55],"network,":[39,87],"without":[40],"consideration":[41],"of":[42,68,80,96,111,143,150],"causes":[44,142],"that":[45,125],"leading":[46],"congestion.":[49],"In":[50],"this":[51],"paper,":[52],"we":[53],"propose":[54],"gradient":[56],"boosting":[57],"decision":[58],"trees":[59],"(GBDTs)":[60],"based":[61],"method":[62],"network":[70],"rank":[73],"using":[77,109],"different":[78,88,102],"types":[79],"explanatory":[81],"variables.":[82],"Based":[83],"on":[84,98],"Sioux":[85],"Falls":[86],"signal":[89,158],"control":[90,159],"strategies":[91],"at":[92],"intersections":[93],"number":[95],"lanes":[97],"segments":[100],"under":[101],"flows":[104],"are":[105],"conducted":[106],"as":[107,154],"samples":[108],"Simulation":[110,122],"Urban":[112],"Mobility":[113],"(SUMO)":[114],"train":[116],"test":[118],"GBDT":[120,127],"model.":[121],"results":[123],"indicate":[124],"model":[128],"achieve":[130],"superior":[131],"performance":[132],"average":[134],"travel":[135],"speed":[136],"prediction":[137],"by":[145],"prioritizing":[146],"relative":[148],"importance":[149],"factors,":[152],"such":[153],"lane":[155],"numbers":[156],"strategies,":[160],"compared":[161],"with":[162],"other":[163],"algorithms.":[164]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}