{"id":"https://openalex.org/W7128069059","doi":"https://doi.org/10.1016/j.trip.2026.101856","title":"Unraveling pedestrian fatality patterns: A comparative study with explainable AI","display_name":"Unraveling pedestrian fatality patterns: A comparative study with explainable AI","publication_year":2026,"publication_date":"2026-02-06","ids":{"openalex":"https://openalex.org/W7128069059","doi":"https://doi.org/10.1016/j.trip.2026.101856"},"language":"en","primary_location":{"id":"doi:10.1016/j.trip.2026.101856","doi":"https://doi.org/10.1016/j.trip.2026.101856","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.trip.2026.101856","pdf_url":null,"source":{"id":"https://openalex.org/S4210230100","display_name":"Transportation Research Interdisciplinary Perspectives","issn_l":"2590-1982","issn":["2590-1982"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-d","jufo-1","ki-jl-1","norway-1","poland-20","vabb-shw"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Transportation Research Interdisciplinary Perspectives","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1016/j.trip.2026.101856","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5106318289","display_name":"Methusela Sulle","orcid":"https://orcid.org/0009-0001-3240-6019","observed_orcids":["https://orcid.org/0009-0001-3240-6019"]},"institutions":[{"id":"https://openalex.org/I139398662","display_name":"South Carolina State University","ror":"https://ror.org/00520ze08","country_code":"US","type":"education","lineage":["https://openalex.org/I139398662"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Methusela Sulle","raw_affiliation_strings":["Department of Engineering, South Carolina State University, Orangeburg, SC, 29117, USA"],"raw_orcid":"https://orcid.org/0009-0001-3240-6019","affiliations":[{"raw_affiliation_string":"Department of Engineering, South Carolina State University, Orangeburg, SC, 29117, USA","institution_ids":["https://openalex.org/I139398662"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091713208","display_name":"Judith Mwakalonge","orcid":"https://orcid.org/0000-0002-7497-6829","observed_orcids":["https://orcid.org/0000-0002-7497-6829"]},"institutions":[{"id":"https://openalex.org/I139398662","display_name":"South Carolina State University","ror":"https://ror.org/00520ze08","country_code":"US","type":"education","lineage":["https://openalex.org/I139398662"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Judith Mwakalonge","raw_affiliation_strings":["Department of Engineering, South Carolina State University, Orangeburg, SC, 29117, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Engineering, South Carolina State University, Orangeburg, SC, 29117, USA","institution_ids":["https://openalex.org/I139398662"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018848498","display_name":"Gurcan Comert","orcid":"https://orcid.org/0000-0002-2373-5013","observed_orcids":["https://orcid.org/0000-0002-2373-5013"]},"institutions":[{"id":"https://openalex.org/I35777872","display_name":"North Carolina Agricultural and Technical State University","ror":"https://ror.org/02aze4h65","country_code":"US","type":"education","lineage":["https://openalex.org/I35777872"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gurcan Comert","raw_affiliation_strings":["Department of Computational Data Science and Engineering, North Carolina A&T State University, Greensboro, NC, 27411, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computational Data Science and Engineering, North Carolina A&T State University, Greensboro, NC, 27411, USA","institution_ids":["https://openalex.org/I35777872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034308043","display_name":"Saidi Siuhi","orcid":"https://orcid.org/0000-0001-8409-9287","observed_orcids":["https://orcid.org/0000-0001-8409-9287"]},"institutions":[{"id":"https://openalex.org/I139398662","display_name":"South Carolina State University","ror":"https://ror.org/00520ze08","country_code":"US","type":"education","lineage":["https://openalex.org/I139398662"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Saidi Siuhi","raw_affiliation_strings":["Department of Engineering, South Carolina State University, Orangeburg, SC, 29117, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Engineering, South Carolina State University, Orangeburg, SC, 29117, USA","institution_ids":["https://openalex.org/I139398662"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025562472","display_name":"Nana Kankam Gyimah","orcid":"https://orcid.org/0009-0008-0601-440X","observed_orcids":["https://orcid.org/0009-0008-0601-440X"]},"institutions":[{"id":"https://openalex.org/I139398662","display_name":"South Carolina State University","ror":"https://ror.org/00520ze08","country_code":"US","type":"education","lineage":["https://openalex.org/I139398662"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nana Kankam Gyimah","raw_affiliation_strings":["Department of Engineering, South Carolina State University, Orangeburg, SC, 29117, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Engineering, South Carolina State University, Orangeburg, SC, 29117, USA","institution_ids":["https://openalex.org/I139398662"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5106318289"],"corresponding_institution_ids":["https://openalex.org/I139398662"],"apc_list":{"value":2400,"currency":"USD","value_usd":2400},"apc_paid":{"value":2400,"currency":"USD","value_usd":2400},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"36","issue":null,"first_page":"101856","last_page":"101856"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10370","display_name":"Traffic and Road Safety","score":0.9990000128746033,"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":0.9990000128746033,"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/T10298","display_name":"Urban Transport and Accessibility","score":9.999999747378752e-05,"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"}},{"id":"https://openalex.org/T11824","display_name":"Injury Epidemiology and Prevention","score":9.999999747378752e-05,"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/pedestrian-fatalities","display_name":"pedestrian fatalities","score":0.9760000109672546},{"id":"https://openalex.org/keywords/pedestrian-vehicle-crashes","display_name":"pedestrian-vehicle crashes","score":0.9620000123977661},{"id":"https://openalex.org/keywords/contributing-factors","display_name":"contributing factors","score":0.4580000042915344},{"id":"https://openalex.org/keywords/explainable-ai","display_name":"explainable AI","score":0.8169999718666077},{"id":"https://openalex.org/keywords/shap","display_name":"SHAP","score":0.7799999713897705},{"id":"https://openalex.org/keywords/xgboost","display_name":"XGBoost","score":0.9520000219345093},{"id":"https://openalex.org/keywords/age-as-risk-factor","display_name":"age as risk factor","score":0.6129999756813049},{"id":"https://openalex.org/keywords/alcohol-and-drug-use","display_name":"alcohol and drug use","score":0.8379999995231628},{"id":"https://openalex.org/keywords/low-visibility","display_name":"low visibility","score":0.7839999794960022},{"id":"https://openalex.org/keywords/pedestrian-infrastructure","display_name":"pedestrian infrastructure","score":0.6819999814033508},{"id":"https://openalex.org/keywords/road-safety-policy","display_name":"road safety policy","score":0.7590000033378601},{"id":"https://openalex.org/keywords/fatality-analysis-reporting-system","display_name":"Fatality Analysis Reporting System","score":0.7269999980926514}],"concepts":[{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.6194000244140625},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3847000002861023},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.36820000410079956},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3167000114917755},{"id":"https://openalex.org/C138816342","wikidata":"https://www.wikidata.org/wiki/Q189603","display_name":"Public health","level":2,"score":0.287200003862381},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.2768999934196472},{"id":"https://openalex.org/C3017944768","wikidata":"https://www.wikidata.org/wiki/Q1450463","display_name":"Poison control","level":2,"score":0.2727999985218048},{"id":"https://openalex.org/C2779473830","wikidata":"https://www.wikidata.org/wiki/Q1540899","display_name":"MEDLINE","level":2,"score":0.25279998779296875},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.25189998745918274},{"id":"https://openalex.org/C74909509","wikidata":"https://www.wikidata.org/wiki/Q10387","display_name":"Gerontology","level":1,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.trip.2026.101856","doi":"https://doi.org/10.1016/j.trip.2026.101856","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.trip.2026.101856","pdf_url":null,"source":{"id":"https://openalex.org/S4210230100","display_name":"Transportation Research Interdisciplinary Perspectives","issn_l":"2590-1982","issn":["2590-1982"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-d","jufo-1","ki-jl-1","norway-1","poland-20","vabb-shw"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Transportation Research Interdisciplinary Perspectives","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.trip.2026.101856","doi":"https://doi.org/10.1016/j.trip.2026.101856","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.trip.2026.101856","pdf_url":null,"source":{"id":"https://openalex.org/S4210230100","display_name":"Transportation Research Interdisciplinary Perspectives","issn_l":"2590-1982","issn":["2590-1982"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-d","jufo-1","ki-jl-1","norway-1","poland-20","vabb-shw"],"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Transportation Research Interdisciplinary Perspectives","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.8560000061988831},{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.761900007724762},{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.4074000120162964}],"study_designs":[],"awards":[{"id":"https://openalex.org/G1896203066","display_name":"Collaborative Research: SHF: Small: Sub-millisecond Topological Feature Extractor for High-Rate Machine Learning","funder_award_id":"2234920","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2773737606","display_name":"CISE-MSI: DP: CPS: Statistical and Artificial Intelligence-based Cyberattack Detection Models for Connected Vehicles","funder_award_id":"2131080","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4723751444","display_name":"EXCELLENCE in RESEARCH: SECURING MACHINE LEARNING AGAINST ADVERSARIAL ATTACKS FOR CONNECTED AND AUTONOMOUS VEHICLES","funder_award_id":"2200457","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5514462513","display_name":null,"funder_award_id":"P382G320015","funder_id":"https://openalex.org/F4320306106","funder_display_name":"U.S. Department of Education"},{"id":"https://openalex.org/G8490549455","display_name":"Catalyst Project: Data Science and Machine Learning \u2013 An Interdisciplinary STEM Training Project for the Future Workforce","funder_award_id":"2305470","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306084","display_name":"U.S. Department of Energy","ror":"https://ror.org/01bj3aw27"},{"id":"https://openalex.org/F4320306106","display_name":"U.S. Department of Education","ror":"https://ror.org/021adze67"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W2289935502","https://openalex.org/W2888487581","https://openalex.org/W2908955506","https://openalex.org/W2949702042","https://openalex.org/W2985518173","https://openalex.org/W2997318585","https://openalex.org/W3004576188","https://openalex.org/W3014082649","https://openalex.org/W3106606749","https://openalex.org/W3166610085","https://openalex.org/W3194037109","https://openalex.org/W3194224674","https://openalex.org/W4205450148","https://openalex.org/W4294839477","https://openalex.org/W4322765468","https://openalex.org/W4363674835","https://openalex.org/W4380550626","https://openalex.org/W4386648688","https://openalex.org/W4388081031","https://openalex.org/W4389513437","https://openalex.org/W4390926748","https://openalex.org/W4391857295","https://openalex.org/W4392559626","https://openalex.org/W4393333755","https://openalex.org/W4394596851","https://openalex.org/W4399840553","https://openalex.org/W4402862472","https://openalex.org/W4403522703","https://openalex.org/W4406610771","https://openalex.org/W4408410212","https://openalex.org/W4409315124","https://openalex.org/W4413769181"],"related_works":["https://openalex.org/W4205252595","https://openalex.org/W7210286935","https://openalex.org/W7002367443","https://openalex.org/W7141852514","https://openalex.org/W4401325929","https://openalex.org/W2985713312","https://openalex.org/W3157465985","https://openalex.org/W4416174033","https://openalex.org/W4404965705","https://openalex.org/W4409315124"],"abstract_inverted_index":{"Road":[0],"fatalities":[1,40,168,216],"pose":[2],"significant":[3,133],"public":[4,219],"safety":[5,200],"and":[6,23,88,125,129,158,175,183,194,209,217],"health":[7],"challenges":[8],"worldwide,":[9],"with":[10,46,59,177,180],"pedestrians":[11],"being":[12],"particularly":[13],"vulnerable":[14],"in":[15,21,172,197],"vehicle\u2013pedestrian":[16],"crashes":[17],"due":[18],"to":[19,33,38,55,91,95,214],"disparities":[20],"physical":[22],"performance":[24],"characteristics.":[25],"This":[26],"study":[27,74],"employs":[28],"explainable":[29],"artificial":[30],"intelligence":[31],"(XAI)":[32],"identify":[34],"key":[35],"factors":[36,94],"contributing":[37,93],"pedestrian":[39,96,136,167,207],"across":[41],"the":[42,47,56,60,67,73,102],"five":[43,57],"U.S.":[44],"states":[45,58],"highest":[48],"crash":[49],"rates":[50],"(2018\u20132022).":[51],"It":[52],"compares":[53],"them":[54],"lowest":[61],"fatality":[62],"rates.":[63],"Using":[64],"data":[65,100],"from":[66],"Fatality":[68],"Analysis":[69],"Reporting":[70],"System":[71],"(FARS),":[72],"applies":[75],"machine":[76],"learning":[77],"techniques":[78],"\u2014":[79,90],"including":[80],"Decision":[81],"Trees,":[82,85],"Gradient":[83],"Boosting":[84],"Random":[86],"Forests,":[87],"XGBoost":[89,139],"predict":[92],"fatalities.":[97,137],"To":[98],"address":[99],"imbalance,":[101],"Synthetic":[103],"Minority":[104],"Over-sampling":[105],"Technique":[106],"(SMOTE)":[107],"is":[108],"utilized,":[109],"while":[110],"SHapley":[111],"Additive":[112],"Explanations":[113],"(SHAP)":[114],"values":[115],"enhance":[116],"model":[117,140],"interpretability.":[118],"The":[119,138],"results":[120],"indicate":[121],"that":[122,166],"age,":[123],"alcohol":[124],"drug":[126],"use,":[127],"location,":[128],"environmental":[130],"conditions":[131],"are":[132,169],"predictors":[134],"of":[135,147,150,153,156,162],"outperformed":[141],"others,":[142],"achieving":[143],"a":[144],"balanced":[145],"accuracy":[146,149],"98%,":[148],"90%,":[151,157],"precision":[152],"92%,":[154],"recall":[155],"an":[159],"F1":[160],"score":[161],"91%.":[163],"Findings":[164],"reveal":[165],"more":[170],"common":[171],"mid-block":[173],"locations":[174],"areas":[176],"poor":[178],"visibility,":[179],"older":[181],"adults":[182],"substance-impaired":[184],"individuals":[185],"at":[186],"higher":[187],"risk.":[188],"These":[189],"insights":[190],"can":[191],"inform":[192],"policymakers":[193],"urban":[195],"planners":[196],"implementing":[198],"targeted":[199],"measures,":[201],"such":[202],"as":[203],"improved":[204],"lighting,":[205],"enhanced":[206],"infrastructure,":[208],"stricter":[210],"traffic":[211],"law":[212],"enforcement,":[213],"reduce":[215],"improve":[218],"safety.":[220]},"counts_by_year":[],"updated_date":"2026-09-21T07:43:02.447757","created_date":"2026-02-07T00:00:00"}