{"id":"https://openalex.org/W3107149018","doi":"https://doi.org/10.1109/access.2020.3040165","title":"Crash Severity Prediction Using Two-Layer Ensemble Machine Learning Model for Proactive Emergency Management","display_name":"Crash Severity Prediction Using Two-Layer Ensemble Machine Learning Model for Proactive Emergency Management","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3107149018","doi":"https://doi.org/10.1109/access.2020.3040165","mag":"3107149018"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.3040165","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3040165","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09268058.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/ielx7/6287639/6514899/09268058.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5048863880","display_name":"Umer Mansoor","orcid":"https://orcid.org/0000-0002-0733-7162"},"institutions":[{"id":"https://openalex.org/I134085113","display_name":"King Fahd University of Petroleum and Minerals","ror":"https://ror.org/03yez3163","country_code":"SA","type":"education","lineage":["https://openalex.org/I134085113"]}],"countries":["SA"],"is_corresponding":true,"raw_author_name":"Umer Mansoor","raw_affiliation_strings":["Department of Civil & Environmental Engineering, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia"],"affiliations":[{"raw_affiliation_string":"Department of Civil & Environmental Engineering, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia","institution_ids":["https://openalex.org/I134085113"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022201234","display_name":"Nedal T. Ratrout","orcid":"https://orcid.org/0000-0001-9786-5702"},"institutions":[{"id":"https://openalex.org/I134085113","display_name":"King Fahd University of Petroleum and Minerals","ror":"https://ror.org/03yez3163","country_code":"SA","type":"education","lineage":["https://openalex.org/I134085113"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Nedal T. Ratrout","raw_affiliation_strings":["Department of Civil & Environmental Engineering, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia"],"affiliations":[{"raw_affiliation_string":"Department of Civil & Environmental Engineering, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia","institution_ids":["https://openalex.org/I134085113"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032359229","display_name":"Syed Masiur Rahman","orcid":"https://orcid.org/0000-0003-3624-0519"},"institutions":[{"id":"https://openalex.org/I134085113","display_name":"King Fahd University of Petroleum and Minerals","ror":"https://ror.org/03yez3163","country_code":"SA","type":"education","lineage":["https://openalex.org/I134085113"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Seyd Masiur Rahman","raw_affiliation_strings":["Center for Environment & Water, Research Institute, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia"],"affiliations":[{"raw_affiliation_string":"Center for Environment & Water, Research Institute, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia","institution_ids":["https://openalex.org/I134085113"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085010945","display_name":"Khaled Assi","orcid":"https://orcid.org/0000-0001-6569-729X"},"institutions":[{"id":"https://openalex.org/I134085113","display_name":"King Fahd University of Petroleum and Minerals","ror":"https://ror.org/03yez3163","country_code":"SA","type":"education","lineage":["https://openalex.org/I134085113"]}],"countries":["SA"],"is_corresponding":false,"raw_author_name":"Khaled Assi","raw_affiliation_strings":["Department of Civil & Environmental Engineering, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia"],"affiliations":[{"raw_affiliation_string":"Department of Civil & Environmental Engineering, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia","institution_ids":["https://openalex.org/I134085113"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5048863880"],"corresponding_institution_ids":["https://openalex.org/I134085113"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":4.1795,"has_fulltext":true,"cited_by_count":47,"citation_normalized_percentile":{"value":0.93900145,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"8","issue":null,"first_page":"210750","last_page":"210762"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10370","display_name":"Traffic and Road Safety","score":0.9993000030517578,"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"}},"topics":[{"id":"https://openalex.org/T10370","display_name":"Traffic and Road Safety","score":0.9993000030517578,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9919000267982483,"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/T11824","display_name":"Injury Epidemiology and Prevention","score":0.9793000221252441,"subfield":{"id":"https://openalex.org/subfields/2739","display_name":"Public Health, Environmental and Occupational Health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/crash","display_name":"Crash","score":0.7458133697509766},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7237548828125},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.679541826248169},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5619434118270874},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5582582950592041},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5497874617576599},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5361177325248718},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5126737356185913},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.5032708048820496},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.48157572746276855},{"id":"https://openalex.org/keywords/feedforward-neural-network","display_name":"Feedforward neural network","score":0.46960362792015076},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.42260026931762695},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3742831349372864}],"concepts":[{"id":"https://openalex.org/C183469790","wikidata":"https://www.wikidata.org/wiki/Q333501","display_name":"Crash","level":2,"score":0.7458133697509766},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7237548828125},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.679541826248169},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5619434118270874},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5582582950592041},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5497874617576599},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5361177325248718},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5126737356185913},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.5032708048820496},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.48157572746276855},{"id":"https://openalex.org/C47702885","wikidata":"https://www.wikidata.org/wiki/Q5441227","display_name":"Feedforward neural network","level":3,"score":0.46960362792015076},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.42260026931762695},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3742831349372864},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.3040165","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3040165","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09268058.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:2d085e3c830b4917aba75ac35aa9adc7","is_oa":true,"landing_page_url":"https://doaj.org/article/2d085e3c830b4917aba75ac35aa9adc7","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 8, Pp 210750-210762 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.3040165","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3040165","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09268058.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":[{"score":0.6299999952316284,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[{"id":"https://openalex.org/G8778135102","display_name":null,"funder_award_id":"SR171023","funder_id":"https://openalex.org/F4320322323","funder_display_name":"King Fahd University of Petroleum and Minerals"}],"funders":[{"id":"https://openalex.org/F4320322323","display_name":"King Fahd University of Petroleum and Minerals","ror":"https://ror.org/03yez3163"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3107149018.pdf","grobid_xml":"https://content.openalex.org/works/W3107149018.grobid-xml"},"referenced_works_count":56,"referenced_works":["https://openalex.org/W28412257","https://openalex.org/W382084900","https://openalex.org/W607505555","https://openalex.org/W1480089972","https://openalex.org/W1480376833","https://openalex.org/W1532221965","https://openalex.org/W1554663460","https://openalex.org/W1563088657","https://openalex.org/W1570448133","https://openalex.org/W1598201578","https://openalex.org/W1966398873","https://openalex.org/W1969320611","https://openalex.org/W1970559658","https://openalex.org/W1987193935","https://openalex.org/W2006868542","https://openalex.org/W2007019606","https://openalex.org/W2013845817","https://openalex.org/W2029069332","https://openalex.org/W2038175992","https://openalex.org/W2049720056","https://openalex.org/W2054466380","https://openalex.org/W2063907334","https://openalex.org/W2067710544","https://openalex.org/W2068614815","https://openalex.org/W2074058676","https://openalex.org/W2099816914","https://openalex.org/W2100372437","https://openalex.org/W2112076978","https://openalex.org/W2119821739","https://openalex.org/W2149706766","https://openalex.org/W2161309151","https://openalex.org/W2736114313","https://openalex.org/W2750591756","https://openalex.org/W2756624799","https://openalex.org/W2790979755","https://openalex.org/W2890907316","https://openalex.org/W2893845879","https://openalex.org/W2896641569","https://openalex.org/W2897805291","https://openalex.org/W2899037650","https://openalex.org/W2901772120","https://openalex.org/W2921467240","https://openalex.org/W2930108727","https://openalex.org/W2942533449","https://openalex.org/W2944077210","https://openalex.org/W2947982105","https://openalex.org/W2949062331","https://openalex.org/W3080568059","https://openalex.org/W3144049167","https://openalex.org/W4236137412","https://openalex.org/W4236362309","https://openalex.org/W4252402220","https://openalex.org/W4292890447","https://openalex.org/W4388297464","https://openalex.org/W6676769703","https://openalex.org/W6821113053"],"related_works":["https://openalex.org/W2905156999","https://openalex.org/W4229460275","https://openalex.org/W4296079469","https://openalex.org/W1987518466","https://openalex.org/W3135046080","https://openalex.org/W3023033471","https://openalex.org/W2014852328","https://openalex.org/W2794896638","https://openalex.org/W2809161969","https://openalex.org/W2891633941"],"abstract_inverted_index":{"Many":[0],"unfortunate":[1],"victims":[2],"in":[3,63,161,266],"road":[4,40,68,110,115],"traffic":[5,41,69,111],"crashes":[6],"do":[7],"not":[8,17],"receive":[9],"ideal":[10],"treatment":[11],"because":[12],"their":[13],"injury":[14],"severity":[15,25,97,269],"is":[16,61,156,175,225],"understood":[18],"at":[19],"an":[20,144],"early":[21],"stage.":[22],"Swift":[23],"crash":[24,42,70,96,112,132,230,268,277,282],"prediction":[26,195],"enables":[27],"trauma":[28,287],"and":[29,43,87,138,190,216,296],"emergency":[30,48,53],"centers":[31,288],"to":[32,50,66,292],"estimate":[33],"the":[34,46,91,95,100,131,148,162,166,171,199,204,222,228,236,245,250,254,281],"potential":[35],"damage":[36],"resulting":[37],"from":[38,122,235,280],"a":[39],"accordingly":[44],"dispatch":[45],"proper":[47],"units":[49],"provide":[51],"appropriate":[52,295],"treatment.":[54,299],"A":[55],"two-layer":[56,149,172,200,246,260],"ensemble":[57,150,173,201,247,261],"machine":[58,78],"learning":[59,79],"model":[60,174,202,224,248,262],"proposed":[62,259],"this":[64,285],"study":[65],"predict":[67],"severity.":[71],"The":[72,105,168,194,219,241,258],"first":[73],"layer":[74,93,164],"integrates":[75],"four":[76],"base":[77,179,206],"models:":[80,180],"k-nearest":[81,181],"neighbor,":[82,182],"decision":[83,183],"tree,":[84,184],"adaptive":[85,185],"boosting,":[86,186],"support":[88,187],"vector":[89,188],"machine;":[90],"second":[92,163],"classifies":[94],"based":[98,209,273],"on":[99,210,274],"feedforward":[101,191],"neural":[102,192],"network":[103],"model.":[104,167],"models":[106,208],"are":[107,141],"developed":[108,223],"using":[109,227],"data":[113],"of":[114,126,165,170,221],"intersections":[116],"over":[117],"6":[118],"years":[119],"(2011-2016)":[120],"obtained":[121,140,234,279],"Great":[123],"Britain's":[124],"Department":[125],"Transport":[127],"online":[128],"database.":[129],"Only":[130],"features":[133],"that":[134,198,244],"can":[135],"be":[136,264,290],"instantaneously":[137],"easily":[139],"used":[142,157],"as":[143],"input.":[145],"To":[146],"simplify":[147],"model,":[151],"principal":[152],"component":[153],"analysis":[154],"technique":[155],"for":[158,232,253,294],"dimensionality":[159],"reduction":[160],"performance":[169,212,252],"compared":[176],"with":[177,270],"five":[178,205],"machine,":[189],"network.":[193],"results":[196],"reveal":[197],"outperforms":[203],"classification":[207],"two":[211],"indicators:":[213],"testing":[214],"accuracy":[215,272],"F1":[217],"score.":[218],"transferability":[220],"tested":[226],"3-year":[229],"dataset":[231,256],"Canada":[233],"National":[237],"Crash":[238],"Database":[239],"Online.":[240],"outcome":[242],"indicates":[243],"shows":[249],"best":[251],"Canadian":[255],"also.":[257],"would":[263,289],"beneficial":[265],"predicting":[267],"high":[271],"limited":[275],"initial":[276],"information":[278],"location.":[283],"Using":[284],"information,":[286],"able":[291],"prepare":[293],"prompt":[297],"medical":[298]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":4}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}