{"id":"https://openalex.org/W4206126688","doi":"https://doi.org/10.1080/08839514.2021.2018643","title":"Prediction in Traffic Accident Duration Based on Heterogeneous Ensemble Learning","display_name":"Prediction in Traffic Accident Duration Based on Heterogeneous Ensemble Learning","publication_year":2022,"publication_date":"2022-01-08","ids":{"openalex":"https://openalex.org/W4206126688","doi":"https://doi.org/10.1080/08839514.2021.2018643"},"language":"en","primary_location":{"id":"doi:10.1080/08839514.2021.2018643","is_oa":true,"landing_page_url":"https://doi.org/10.1080/08839514.2021.2018643","pdf_url":null,"source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1080/08839514.2021.2018643","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100294312","display_name":"Yuexu Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuexu Zhao","raw_affiliation_strings":["College of Economics, Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Economics, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076740334","display_name":"Wei Deng","orcid":"https://orcid.org/0000-0001-8685-1751"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wei Deng","raw_affiliation_strings":["College of Economics, Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Economics, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5076740334"],"corresponding_institution_ids":["https://openalex.org/I50760025"],"apc_list":{"value":2195,"currency":"USD","value_usd":2195},"apc_paid":{"value":2195,"currency":"USD","value_usd":2195},"fwci":4.3187,"has_fulltext":false,"cited_by_count":42,"citation_normalized_percentile":{"value":0.94802302,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"36","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9993000030517578,"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":0.9993000030517578,"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/T10370","display_name":"Traffic and Road Safety","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/T10809","display_name":"Occupational Health and Safety Research","score":0.967199981212616,"subfield":{"id":"https://openalex.org/subfields/3614","display_name":"Radiological and Ultrasound Technology"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8135225772857666},{"id":"https://openalex.org/keywords/duration","display_name":"Duration (music)","score":0.7019457817077637},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.6519772410392761},{"id":"https://openalex.org/keywords/accident","display_name":"Accident (philosophy)","score":0.5909631848335266},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5021562576293945},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5004937648773193},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.46132487058639526},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.45323774218559265},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44924139976501465},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3981063663959503}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8135225772857666},{"id":"https://openalex.org/C112758219","wikidata":"https://www.wikidata.org/wiki/Q16038819","display_name":"Duration (music)","level":2,"score":0.7019457817077637},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6519772410392761},{"id":"https://openalex.org/C2780289543","wikidata":"https://www.wikidata.org/wiki/Q424630","display_name":"Accident (philosophy)","level":2,"score":0.5909631848335266},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5021562576293945},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5004937648773193},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.46132487058639526},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.45323774218559265},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44924139976501465},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3981063663959503},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/08839514.2021.2018643","is_oa":true,"landing_page_url":"https://doi.org/10.1080/08839514.2021.2018643","pdf_url":null,"source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:c398296713c04a2dbb4b690faf8b480f","is_oa":false,"landing_page_url":"https://doaj.org/article/c398296713c04a2dbb4b690faf8b480f","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Applied Artificial Intelligence, Vol 36, Iss 1 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1080/08839514.2021.2018643","is_oa":true,"landing_page_url":"https://doi.org/10.1080/08839514.2021.2018643","pdf_url":null,"source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.6899999976158142,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W28412257","https://openalex.org/W370167726","https://openalex.org/W1678356000","https://openalex.org/W1975185440","https://openalex.org/W1990836268","https://openalex.org/W1995840715","https://openalex.org/W2012367234","https://openalex.org/W2023312838","https://openalex.org/W2028138594","https://openalex.org/W2046309797","https://openalex.org/W2051350735","https://openalex.org/W2064186732","https://openalex.org/W2072069601","https://openalex.org/W2078584509","https://openalex.org/W2109772039","https://openalex.org/W2120498062","https://openalex.org/W2122111042","https://openalex.org/W2122825543","https://openalex.org/W2130366906","https://openalex.org/W2135310819","https://openalex.org/W2148143831","https://openalex.org/W2551960028","https://openalex.org/W2562618250","https://openalex.org/W2567881713","https://openalex.org/W2604272474","https://openalex.org/W2612690371","https://openalex.org/W2794582213","https://openalex.org/W2891057194","https://openalex.org/W2912083425","https://openalex.org/W2912213068","https://openalex.org/W2914874661","https://openalex.org/W2973432741","https://openalex.org/W2974690168","https://openalex.org/W2999615587","https://openalex.org/W3007339935","https://openalex.org/W3021654819","https://openalex.org/W3043890802","https://openalex.org/W3100199031","https://openalex.org/W4234432229"],"related_works":["https://openalex.org/W4285046548","https://openalex.org/W4313488044","https://openalex.org/W3124390867","https://openalex.org/W4281757034","https://openalex.org/W4311847748","https://openalex.org/W4285741730","https://openalex.org/W2810053714","https://openalex.org/W4281560664","https://openalex.org/W3136979370","https://openalex.org/W3214927170"],"abstract_inverted_index":{"Based":[0],"on":[1,19,41],"millions":[2],"of":[3,27,35,45,57,62,74,108,118,167,169,176,189],"traffic":[4],"accident":[5,14,28,47,109,143,191,196],"data":[6,70,75,111],"in":[7,31],"the":[8,25,32,36,42,46,98,116,142,149,173,177,181,190,195],"United":[9],"States,":[10],"we":[11,39,68,85,124],"build":[12,134],"an":[13],"duration":[15,29,110],"prediction":[16,30,155],"model":[17,139,150,178],"based":[18],"heterogeneous":[20,136],"ensemble":[21,137],"learning":[22,138],"to":[23,133,140,162,194],"study":[24],"problem":[26],"initial":[33,100],"stage":[34,44],"accident.":[37],"First,":[38],"focus":[40],"earlier":[43],"development,":[48],"and":[49,64,79,93,121,130,172,185],"select":[50],"some":[51],"effective":[52],"information":[53,96,101],"from":[54,97,115],"five":[55],"aspects":[56],"traffic,":[58],"location,":[59,183],"weather,":[60],"points":[61],"interest":[63],"time":[65],"attribute.":[66],"Then,":[67],"improve":[69],"quality":[71],"by":[72],"means":[73],"cleaning,":[76],"outlier":[77],"processing":[78],"missing":[80],"value":[81],"processing.":[82],"In":[83],"addition,":[84],"encode":[86],"category":[87,91],"features":[88],"for":[89],"high-frequency":[90],"variables":[92],"extract":[94],"deeper":[95],"limited":[99],"through":[102],"feature":[103,174],"extraction.":[104],"A":[105],"pre-processing":[106],"scheme":[107],"is":[112],"established.":[113],"Finally,":[114],"perspective":[117],"model,":[119],"sample":[120],"parameter":[122],"diversity,":[123],"use":[125],"XGBoost,":[126],"LightGBM,":[127],"CatBoost,":[128],"stacking":[129],"elastic":[131],"network":[132],"a":[135,164],"predict":[141],"duration.":[144,197],"The":[145],"results":[146],"show":[147],"that":[148,180],"not":[151],"only":[152],"has":[153],"good":[154],"accuracy":[156],"but":[157],"can":[158],"synthesize":[159],"multiple":[160],"models":[161],"give":[163],"comprehensive":[165],"degree":[166],"importance":[168,175],"influencing":[170],"factors,":[171],"shows":[179],"time,":[182],"weather":[184],"relevant":[186],"historical":[187],"statistics":[188],"are":[192],"important":[193]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":17},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":5}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}
