{"id":"https://openalex.org/W7125916937","doi":"https://doi.org/10.1109/access.2026.3658687","title":"Multi-Task FT-Transformer: A High-Performance and Interpretable Framework for Traffic Accident Severity Prediction","display_name":"Multi-Task FT-Transformer: A High-Performance and Interpretable Framework for Traffic Accident Severity Prediction","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7125916937","doi":"https://doi.org/10.1109/access.2026.3658687"},"language":null,"primary_location":{"id":"doi:10.1109/access.2026.3658687","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3658687","pdf_url":null,"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://doi.org/10.1109/access.2026.3658687","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Yahui Wang","orcid":"https://orcid.org/0000-0003-2913-7011"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yahui Wang","raw_affiliation_strings":["School of Medical Technology, Beijing Institute of Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-2913-7011","affiliations":[{"raw_affiliation_string":"School of Medical Technology, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113285106","display_name":"Zhoushuo Liang","orcid":null},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhoushuo Liang","raw_affiliation_strings":["School of Medical Technology, Beijing Institute of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Medical Technology, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101591895","display_name":"Yue He","orcid":"https://orcid.org/0000-0002-8831-026X"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue He","raw_affiliation_strings":["School of Medical Technology, Beijing Institute of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Medical Technology, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07705818,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"21585","last_page":"21606"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10370","display_name":"Traffic and Road Safety","score":0.8205999732017517,"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":0.8205999732017517,"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":0.029400000348687172,"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/T11487","display_name":"Automotive and Human Injury Biomechanics","score":0.009999999776482582,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"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/interpretability","display_name":"Interpretability","score":0.8511999845504761},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.5440000295639038},{"id":"https://openalex.org/keywords/road-traffic","display_name":"Road traffic","score":0.4036000072956085},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4016000032424927},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.3799999952316284},{"id":"https://openalex.org/keywords/interdependence","display_name":"Interdependence","score":0.34860000014305115},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.3149999976158142}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8511999845504761},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7077000141143799},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.5440000295639038},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4189000129699707},{"id":"https://openalex.org/C2985695025","wikidata":"https://www.wikidata.org/wiki/Q4323994","display_name":"Road traffic","level":2,"score":0.4036000072956085},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4016000032424927},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3799999952316284},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3709999918937683},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3707999885082245},{"id":"https://openalex.org/C185874996","wikidata":"https://www.wikidata.org/wiki/Q269699","display_name":"Interdependence","level":2,"score":0.34860000014305115},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3149999976158142},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.296999990940094},{"id":"https://openalex.org/C3017944768","wikidata":"https://www.wikidata.org/wiki/Q1450463","display_name":"Poison control","level":2,"score":0.2928999960422516},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.2870999872684479},{"id":"https://openalex.org/C2780233690","wikidata":"https://www.wikidata.org/wiki/Q535347","display_name":"Transparency (behavior)","level":2,"score":0.28619998693466187},{"id":"https://openalex.org/C2994001137","wikidata":"https://www.wikidata.org/wiki/Q1358919","display_name":"Bike sharing","level":2,"score":0.2775000035762787},{"id":"https://openalex.org/C2989506057","wikidata":"https://www.wikidata.org/wiki/Q9687","display_name":"Traffic accident","level":2,"score":0.2687999904155731},{"id":"https://openalex.org/C29202148","wikidata":"https://www.wikidata.org/wiki/Q287260","display_name":"Resource allocation","level":2,"score":0.26190000772476196}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/access.2026.3658687","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3658687","pdf_url":null,"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"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3658687","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3658687","pdf_url":null,"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":[],"awards":[{"id":"https://openalex.org/G3702087945","display_name":null,"funder_award_id":"3320012222316","funder_id":"https://openalex.org/F4320327514","funder_display_name":"Beijing Institute of Technology Research Fund Program for Young Scholars"},{"id":"https://openalex.org/G4842918202","display_name":null,"funder_award_id":"9244037","funder_id":"https://openalex.org/F4320334977","funder_display_name":"Beijing Municipal Natural Science Foundation"},{"id":"https://openalex.org/G7489302641","display_name":null,"funder_award_id":"HFNKL2023WW02","funder_id":"https://openalex.org/F4320316328","funder_display_name":"National Key Laboratory of Human Factors Engineering"}],"funders":[{"id":"https://openalex.org/F4320316328","display_name":"National Key Laboratory of Human Factors Engineering","ror":null},{"id":"https://openalex.org/F4320327514","display_name":"Beijing Institute of Technology Research Fund Program for Young Scholars","ror":null},{"id":"https://openalex.org/F4320334977","display_name":"Beijing Municipal Natural Science Foundation","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":57,"referenced_works":["https://openalex.org/W2012897436","https://openalex.org/W2031509004","https://openalex.org/W2140891054","https://openalex.org/W2162635816","https://openalex.org/W2517822188","https://openalex.org/W2562137921","https://openalex.org/W2608783517","https://openalex.org/W2621409665","https://openalex.org/W2921467240","https://openalex.org/W2930108727","https://openalex.org/W2935760944","https://openalex.org/W2963542740","https://openalex.org/W2963677766","https://openalex.org/W2972411936","https://openalex.org/W2992034134","https://openalex.org/W3003177912","https://openalex.org/W3010114514","https://openalex.org/W3011594739","https://openalex.org/W3012383032","https://openalex.org/W3013324596","https://openalex.org/W3018720890","https://openalex.org/W3024400626","https://openalex.org/W3031359518","https://openalex.org/W3048065666","https://openalex.org/W3085046840","https://openalex.org/W3087802854","https://openalex.org/W3091723690","https://openalex.org/W3165451569","https://openalex.org/W3183803003","https://openalex.org/W3184497139","https://openalex.org/W3192778626","https://openalex.org/W3199808110","https://openalex.org/W3206704750","https://openalex.org/W3213551175","https://openalex.org/W4200414190","https://openalex.org/W4206130810","https://openalex.org/W4226487320","https://openalex.org/W4256270777","https://openalex.org/W4280608029","https://openalex.org/W4285041593","https://openalex.org/W4308478579","https://openalex.org/W4311098406","https://openalex.org/W4320004384","https://openalex.org/W4362676646","https://openalex.org/W4366816254","https://openalex.org/W4376464656","https://openalex.org/W4385245566","https://openalex.org/W4386783381","https://openalex.org/W4389410665","https://openalex.org/W4391070409","https://openalex.org/W4391131848","https://openalex.org/W4391573688","https://openalex.org/W4394596851","https://openalex.org/W4394989702","https://openalex.org/W4405270855","https://openalex.org/W4411150584","https://openalex.org/W4411624672"],"related_works":[],"abstract_inverted_index":{"Traffic":[0],"accidents":[1,90],"are":[2],"a":[3,56,84,159],"significant":[4],"concern":[5],"worldwide,":[6],"leading":[7],"to":[8,66,129,138],"severe":[9],"injuries,":[10,32,71,120],"fatalities,":[11,33,72,119],"and":[12,34,73,104,121,142,155,192,204,211],"property":[13,35,74,122,195],"damage.":[14,196],"Predicting":[15],"the":[16,28,39,68,115,163,170],"severity":[17,69,150,180],"of":[18,41,70,162],"these":[19],"outcomes":[20],"is":[21,81],"essential":[22],"for":[23,47,118,185,189,194],"enhancing":[24,152],"road":[25,89,102,209],"safety.":[26],"However,":[27],"complex":[29],"interdependencies":[30],"among":[31],"damage,":[36,123],"coupled":[37],"with":[38,62,183],"influence":[40,148],"various":[42],"dynamic":[43],"factors,":[44],"pose":[45],"challenges":[46],"prediction.":[48],"To":[49],"address":[50],"this":[51],"issue,":[52],"This":[53,197],"study":[54],"proposes":[55],"multi-task":[57,108,172],"feature":[58,140],"token":[59],"transformer":[60],"model":[61,80,153,174],"uncertainty-weighted":[63],"loss":[64],"optimization":[65],"predict":[67],"damage":[75],"in":[76,91,125,178],"traffic":[77,213],"accidents.":[78],"The":[79,107,166],"trained":[82],"on":[83],"large-scale":[85],"dataset":[86],"comprising":[87],"urban":[88],"China,":[92],"incorporating":[93],"critical":[94],"factors":[95,147],"such":[96],"as":[97],"driver":[98],"characteristics,":[99],"vehicle":[100],"types,":[101],"conditions,":[103],"environmental":[105],"influences.":[106],"learning":[109],"framework":[110,198],"enhances":[111],"information":[112],"sharing":[113],"across":[114],"prediction":[116,181],"tasks":[117],"resulting":[124],"improved":[126],"accuracy":[127,177],"compared":[128],"single-task":[130],"frameworks.":[131],"Furthermore,":[132],"we":[133],"apply":[134],"SHapley":[135],"Additive":[136],"Explanations":[137],"quantify":[139],"importance":[141],"analyze":[143],"how":[144],"different":[145],"input":[146],"accident":[149],"predictions,":[151],"transparency":[154],"interpretability":[156],"by":[157],"providing":[158],"clear":[160],"understanding":[161],"decision-making":[164],"process.":[165],"results":[167],"show":[168],"that":[169],"proposed":[171],"FT-Transformer":[173],"achieves":[175],"good":[176],"all":[179],"tasks,":[182],"70.31%":[184],"injury":[186],"severity,":[187,191],"90.38%":[188],"death":[190],"80.79%":[193],"helps":[199],"policymakers":[200],"target":[201],"high-risk":[202],"areas":[203],"optimize":[205],"resource":[206],"allocation,":[207],"improving":[208],"safety":[210],"reducing":[212],"incidents.":[214]},"counts_by_year":[],"updated_date":"2026-02-13T13:36:01.753593","created_date":"2026-01-29T00:00:00"}
