{"id":"https://openalex.org/W4406610771","doi":"https://doi.org/10.1109/tits.2025.3526217","title":"Automated and Explainable Artificial Intelligence to Enhance Prediction of Pedestrian Injury Severity","display_name":"Automated and Explainable Artificial Intelligence to Enhance Prediction of Pedestrian Injury Severity","publication_year":2025,"publication_date":"2025-01-20","ids":{"openalex":"https://openalex.org/W4406610771","doi":"https://doi.org/10.1109/tits.2025.3526217"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2025.3526217","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3526217","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-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/A5055394222","display_name":"Gian Antariksa","orcid":"https://orcid.org/0000-0001-9193-2687"},"institutions":[{"id":"https://openalex.org/I13511017","display_name":"Texas State University","ror":"https://ror.org/05h9q1g27","country_code":"US","type":"education","lineage":["https://openalex.org/I13511017"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Gian Antariksa","raw_affiliation_strings":["Ingram School of Engineering, Texas State University, San Marcos, TX, USA"],"raw_orcid":"https://orcid.org/0000-0001-9193-2687","affiliations":[{"raw_affiliation_string":"Ingram School of Engineering, Texas State University, San Marcos, TX, USA","institution_ids":["https://openalex.org/I13511017"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061925367","display_name":"Reuben Tamakloe","orcid":"https://orcid.org/0000-0002-9605-8439"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Reuben Tamakloe","raw_affiliation_strings":["Korea Advanced Institute of Science and Technology, Daejeon, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Korea Advanced Institute of Science and Technology, Daejeon, South Korea","institution_ids":["https://openalex.org/I157485424"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057988612","display_name":"Jinli Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I13511017","display_name":"Texas State University","ror":"https://ror.org/05h9q1g27","country_code":"US","type":"education","lineage":["https://openalex.org/I13511017"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jinli Liu","raw_affiliation_strings":["Department of Geography and Environmental Studies, Texas State University, San Marcos, TX, USA"],"raw_orcid":"https://orcid.org/0000-0002-6152-8808","affiliations":[{"raw_affiliation_string":"Department of Geography and Environmental Studies, Texas State University, San Marcos, TX, USA","institution_ids":["https://openalex.org/I13511017"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053621729","display_name":"Subasish Das","orcid":"https://orcid.org/0000-0002-1671-2753"},"institutions":[{"id":"https://openalex.org/I13511017","display_name":"Texas State University","ror":"https://ror.org/05h9q1g27","country_code":"US","type":"education","lineage":["https://openalex.org/I13511017"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Subasish Das","raw_affiliation_strings":["Ingram School of Engineering, Texas State University, San Marcos, TX, USA"],"raw_orcid":"https://orcid.org/0000-0002-1671-2753","affiliations":[{"raw_affiliation_string":"Ingram School of Engineering, Texas State University, San Marcos, TX, USA","institution_ids":["https://openalex.org/I13511017"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5055394222"],"corresponding_institution_ids":["https://openalex.org/I13511017"],"apc_list":null,"apc_paid":null,"fwci":7.1904,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.96775639,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"26","issue":"4","first_page":"5568","last_page":"5584"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9363999962806702,"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":0.9363999962806702,"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/pedestrian","display_name":"Pedestrian","score":0.761864960193634},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5911263823509216},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5250586867332458},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.45313209295272827},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35950249433517456},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2714206576347351},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.23494499921798706}],"concepts":[{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.761864960193634},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5911263823509216},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5250586867332458},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.45313209295272827},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35950249433517456},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2714206576347351},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.23494499921798706}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2025.3526217","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3526217","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":72,"referenced_works":["https://openalex.org/W1964201215","https://openalex.org/W1965555277","https://openalex.org/W1976556551","https://openalex.org/W1995875735","https://openalex.org/W2009702434","https://openalex.org/W2056132907","https://openalex.org/W2084341220","https://openalex.org/W2282821441","https://openalex.org/W2295598076","https://openalex.org/W2317741601","https://openalex.org/W2433672501","https://openalex.org/W2546546968","https://openalex.org/W2562005416","https://openalex.org/W2594561867","https://openalex.org/W2617040354","https://openalex.org/W2736416056","https://openalex.org/W2771144840","https://openalex.org/W2773157312","https://openalex.org/W2796267862","https://openalex.org/W2804189068","https://openalex.org/W2889046169","https://openalex.org/W2891503716","https://openalex.org/W2897805291","https://openalex.org/W2899037650","https://openalex.org/W2914363581","https://openalex.org/W2943891375","https://openalex.org/W2995027684","https://openalex.org/W2996705655","https://openalex.org/W3005068726","https://openalex.org/W3006399881","https://openalex.org/W3012173658","https://openalex.org/W3056490338","https://openalex.org/W3087802854","https://openalex.org/W3093698375","https://openalex.org/W3107511980","https://openalex.org/W3116286104","https://openalex.org/W3164508800","https://openalex.org/W3167883864","https://openalex.org/W3168989165","https://openalex.org/W3173631909","https://openalex.org/W3188490672","https://openalex.org/W3196387683","https://openalex.org/W3197260976","https://openalex.org/W3210964472","https://openalex.org/W3215889917","https://openalex.org/W4205252595","https://openalex.org/W4210908942","https://openalex.org/W4213139545","https://openalex.org/W4220774403","https://openalex.org/W4220845816","https://openalex.org/W4280576042","https://openalex.org/W4287577796","https://openalex.org/W4309946964","https://openalex.org/W4313479587","https://openalex.org/W4316174623","https://openalex.org/W4362640689","https://openalex.org/W4378450674","https://openalex.org/W4379532119","https://openalex.org/W4382677506","https://openalex.org/W4385240668","https://openalex.org/W4386213938","https://openalex.org/W4387346511","https://openalex.org/W4387557093","https://openalex.org/W4390889563","https://openalex.org/W6645328661","https://openalex.org/W6737947904","https://openalex.org/W6745609711","https://openalex.org/W6748281036","https://openalex.org/W6750729320","https://openalex.org/W6757699482","https://openalex.org/W6774581290","https://openalex.org/W6854882949"],"related_works":["https://openalex.org/W2972620127","https://openalex.org/W2981141433","https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296"],"abstract_inverted_index":{"This":[0,79,164],"study":[1],"used":[2],"a":[3],"detailed":[4],"explainable":[5,64],"AI":[6,65,89],"for":[7,205],"automatic":[8],"machine":[9],"learning,":[10],"AutoGluon,":[11],"to":[12,33,73,183],"predict":[13],"pedestrian":[14,34,100,161,178],"injury":[15,42,101,162],"severity":[16,43,102],"using":[17],"data":[18],"collected":[19],"over":[20],"five":[21],"years":[22],"(2016-2021)":[23],"in":[24,60,129,199],"Louisiana.":[25],"The":[26,54,104],"final":[27],"dataset":[28],"includes":[29],"forty":[30],"variables":[31,98,159,185,198],"related":[32],"characteristics,":[35],"environmental":[36,184],"circumstances,":[37],"and":[38,51,144,180,188],"vehicle":[39],"specifications.":[40],"Pedestrian":[41],"was":[44],"divided":[45],"into":[46,170],"three":[47],"categories:":[48],"fatal,":[49],"injury,":[50],"no":[52],"injury.":[53],"novelty":[55],"of":[56,63,126,137,157,196],"this":[57],"approach":[58],"lies":[59],"the":[61,75,84,96,108,124,135,142,158,171,194],"application":[62],"(XAI),":[66],"specifically":[67],"SHAP":[68,150],"(SHapley":[69],"Additive":[70],"exPlanations)":[71],"values,":[72],"interpret":[74],"AutoML":[76],"model\u2019s":[77],"predictions.":[78],"combination":[80],"not":[81],"only":[82],"addressed":[83],"opaqueness":[85],"typically":[86],"associated":[87],"with":[88,119],"\u201cblack":[90],"box\u201d":[91],"models":[92],"but":[93],"also":[94],"illuminated":[95],"critical":[97],"influencing":[99,160],"outcomes.":[103],"results":[105],"revealed":[106],"that":[107],"weighted":[109],"ensemble":[110,127],"model":[111],"emerged":[112],"as":[113],"top":[114],"performers,":[115],"showcasing":[116],"high":[117],"accuracy":[118],"minimal":[120],"prediction":[121,131],"times,":[122],"demonstrating":[123],"potential":[125],"methods":[128],"improving":[130],"outcomes":[132],"by":[133,149],"integrating":[134],"strengths":[136],"various":[138],"individual":[139],"models.":[140],"Furthermore,":[141],"global":[143],"local":[145],"explainability":[146],"analyses":[147],"provided":[148],"values":[151],"afforded":[152],"us":[153],"an":[154],"in-depth":[155],"understanding":[156],"severity.":[163],"dual-level":[165],"explanation":[166],"offered":[167],"valuable":[168],"insights":[169],"complex":[172],"dynamics":[173],"at":[174],"play,":[175],"ranging":[176],"from":[177],"impairment":[179],"driver":[181],"condition":[182],"like":[186],"lighting":[187],"weather":[189],"conditions.":[190],"These":[191],"findings":[192],"underscore":[193],"importance":[195],"specific":[197],"crash":[200],"outcomes,":[201],"offering":[202],"actionable":[203],"intelligence":[204],"targeted":[206],"interventions.":[207]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}