{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T12:20:12Z","timestamp":1770466812822,"version":"3.49.0"},"reference-count":75,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Intell. Transport. Syst."],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1109\/tits.2025.3526217","type":"journal-article","created":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T19:03:23Z","timestamp":1737399803000},"page":"5568-5584","source":"Crossref","is-referenced-by-count":7,"title":["Automated and Explainable Artificial Intelligence to Enhance Prediction of Pedestrian Injury Severity"],"prefix":"10.1109","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9193-2687","authenticated-orcid":false,"given":"Gian","family":"Antariksa","sequence":"first","affiliation":[{"name":"Ingram School of Engineering, Texas State University, San Marcos, TX, USA"}]},{"given":"Reuben","family":"Tamakloe","sequence":"additional","affiliation":[{"name":"Korea Advanced Institute of Science and Technology, Daejeon, South Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6152-8808","authenticated-orcid":false,"given":"Jinli","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Geography and Environmental Studies, Texas State University, San Marcos, TX, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1671-2753","authenticated-orcid":false,"given":"Subasish","family":"Das","sequence":"additional","affiliation":[{"name":"Ingram School of Engineering, Texas State University, San Marcos, TX, USA"}]}],"member":"263","reference":[{"key":"ref1","volume-title":"NHTSA. Traffic Safety Fact (2021 Data): Pedestrian","year":"2021"},{"key":"ref2","volume-title":"The Economic and Societal Impact of Motor Vehicle Crashes, 2010 (Revised)","author":"Blincoe","year":"2015"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.aap.2023.107333"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1080\/19439962.2016.1194353"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.3141\/2519-11"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.aap.2024.107457"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1080\/19439962.2021.1923101"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.aap.2023.107119"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.aap.2022.106610"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.amar.2020.100152"},{"key":"ref11","first-page":"17","article-title":"Predicting the likelihood of aging pedestrian severe crashes using Dirichlet random-effect Bayesian logistic regression model","author":"Kitali","year":"2018"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.aap.2023.107146"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3141\/2237-11"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.amar.2020.100137"},{"key":"ref15","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2021.106261","article-title":"Quantifying and comparing the effects of key risk factors on various types of roadway segment crashes with LightGBM and SHAP","volume":"159","author":"Wen","year":"2021","journal-title":"Accident Anal. Prevention"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1080\/01441647.2021.1954108"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.aap.2018.10.016","article-title":"Crash injury severity analysis using a two-layer stacking framework","volume":"122","author":"Tang","year":"2019","journal-title":"Accident Anal. Prevention"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2874979"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1080\/23249935.2016.1256355"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.aap.2023.106964"},{"key":"ref21","doi-asserted-by":"crossref","DOI":"10.1016\/j.aap.2022.106937","article-title":"Factors affecting injury severity at pedestrian crossing locations with rectangular RAPID flashing beacons (RRFB) using XGBoost and random parameters discrete outcome models","volume":"181","author":"Goswamy","year":"2023","journal-title":"Accident Anal. Prevention"},{"issue":"1","key":"ref22","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1002\/for.2425","article-title":"Severity prediction of traffic accident using an artificial neural network","volume":"36","author":"Alkheder","year":"2017","journal-title":"J. Forecasting"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2870052"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1214\/ss\/1009213726"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.jbusres.2018.05.013","article-title":"The analytics paradigm in business research","volume":"90","author":"Delen","year":"2018","journal-title":"J. Bus. Res."},{"key":"ref26","article-title":"AutoGluon-tabular: Robust and accurate AutoML for structured data","author":"Erickson","year":"2020","journal-title":"arXiv:2003.06505"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.3390\/su14074101"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2021.108019"},{"issue":"38","key":"ref29","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1177\/0361198118794292","article-title":"Machine learning methods to analyze injury severity of drivers from different age and gender groups","volume":"2672","author":"Mafi","year":"2018","journal-title":"Transp. Res. Rec., J. Transp. Res. Board"},{"issue":"2","key":"ref30","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1080\/13588265.2020.1806644","article-title":"Injury severity prediction model for two-wheeler crashes at mid-block road sections","volume":"27","author":"Panicker","year":"2022","journal-title":"Int. J. Crashworthiness"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.3390\/su12041324"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsr.2019.04.008"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2020.106931"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2020.137231"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.3390\/su11247118"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.ssci.2020.104988"},{"key":"ref37","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.jsr.2021.06.002","article-title":"Application of machine learning technique for optimizing roadside design to decrease barrier crash costs, a quantile regression model approach","volume":"78","author":"Rezapour","year":"2021","journal-title":"J. Saf. Res."},{"key":"ref38","first-page":"1","article-title":"Electric vehicle\u2019s range and state of charge estimations using AutoML","volume-title":"Proc. IEEE Transp. Electrific. Conf. Expo (ITEC)","author":"Witvoet"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/3584371.3613051"},{"issue":"5","key":"ref40","doi-asserted-by":"crossref","first-page":"6911","DOI":"10.1007\/s11356-021-15895-y","article-title":"Global burden analysis and AutoGluon prediction of accidental carbon monoxide poisoning by global burden of disease study 2019","volume":"29","author":"Liu","year":"2022","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.nhres.2021.07.002"},{"issue":"1","key":"ref42","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/s44212-022-00017-x","article-title":"Dynamic population mapping with AutoGluon","volume":"1","author":"Song","year":"2022","journal-title":"Urban Informat."},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.aap.2019.105405"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/j.aap.2021.106153"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.aap.2021.106545"},{"key":"ref46","volume-title":"Crash Data Reports","year":"2024"},{"issue":"2","key":"ref47","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1007\/s42421-023-00076-9","article-title":"Causal analysis and classification of traffic crash injury severity using machine learning algorithms","volume":"5","author":"Chakraborty","year":"2023","journal-title":"Data Sci. Transp."},{"key":"ref48","first-page":"18","article-title":"Ensemble selection from libraries of models","volume-title":"Proc. 21st Int. Conf. Mach. Learn. (ICML)","author":"Caruana"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1080\/17457300.2018.1456466"},{"key":"ref50","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.aap.2013.03.035","article-title":"Predicting crash likelihood and severity on freeways with real-time loop detector data","volume":"57","author":"Xu","year":"2013","journal-title":"Accident Anal. Prevention"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-006-6226-1"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729694"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1002\/j.1538-7305.1948.tb01338.x"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-38747-0_2"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref56","first-page":"1","article-title":"CatBoost: Unbiased boosting with categorical features","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Prokhorenkova"},{"key":"ref57","first-page":"1","article-title":"LightGBM: A highly efficient gradient boosting decision tree","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Ke"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"ref59","first-page":"1","article-title":"A unified approach to interpreting model predictions","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Lundberg"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.3390\/e23010018"},{"key":"ref61","article-title":"Consistent individualized feature attribution for tree ensembles","author":"Lundberg","year":"2018","journal-title":"arXiv:1802.03888"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsr.2023.08.010"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1080\/15389588.2017.1329535"},{"key":"ref64","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.jsr.2017.02.011","article-title":"Hierarchical ordered model for injury severity of pedestrian crashes in South Korea","volume":"61","author":"Kim","year":"2017","journal-title":"J. Saf. Res."},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1080\/19439962.2018.1551257"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.3141\/2659-17"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1080\/15389588.2022.2059474"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.3141\/2148-13"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1080\/15389588.2022.2100362"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1177\/03611981221076120"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsr.2022.01.008"},{"issue":"2","key":"ref72","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.iatssr.2023.03.002","article-title":"Exploring association of contributing factors to pedestrian fatal and severe injury crashes under dark-no-streetlight condition","volume":"47","author":"Hossain","year":"2023","journal-title":"IATSS Res."},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.3141\/2659-19"},{"key":"ref74","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.jsr.2021.11.001","article-title":"Exploring the impacts of built environment on pedestrian injury severity involving distracted driving","volume":"80","author":"Khan","year":"2022","journal-title":"J. Saf. Res."},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1080\/15389588.2017.1354371"}],"container-title":["IEEE Transactions on Intelligent Transportation Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6979\/10945245\/10847308.pdf?arnumber=10847308","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,29]],"date-time":"2025-03-29T06:07:30Z","timestamp":1743228450000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10847308\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4]]},"references-count":75,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tits.2025.3526217","relation":{},"ISSN":["1524-9050","1558-0016"],"issn-type":[{"value":"1524-9050","type":"print"},{"value":"1558-0016","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4]]}}}