[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119522-en":3,"doc-seo-119522-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119522,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Advancements in Machine Learning for Traffic Accident Severity Prediction - A Comprehensive Review","This literature review categorizes machine learning studies in traffic accident severity prediction, delivering a structured overview of key applications and recent advancements. It compares major model families, highlighting how Random Forest, XGBoost, and Support Vector Machines (SVM) perform for severity estimation. The review further examines studies driven by specific factors, such as road characteristics, weather, and vehicle properties, and discusses crash-type-specific modeling for pedestrian, vehicle, and collision scenarios. Hybrid and ensemble methods are analyzed as avenues to improve accuracy, robustness, and practical applicability for real-world traffic safety management.","[https://doi.org/10.3311/PPtr.40369](https://doi.org/10.3311/PPtr.40369)  \nCreative Commons Attribution b  \n|347  \nPeriodica Polytechnica Transportation Engineering, 53(3), pp. 347–355, 2025  \nAdvancements in Machine Learning for Traffic Accident Severity Prediction: A Comprehensive Review  \nNoura Hamdan1, Tibor Sipos1,2*  \n1 Department of Transport Technology and Economics, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary  \n2 KTI -Hungarian Institute for Transport Sciences and Logistics Ltd., Than Károly str. 3–5., H-1119 Budapest, Hungary  \n* Corresponding author, e-mail: [sipos.tibor@kjk.bme.hu](sipos.tibor@kjk.bme.hu)  \nReceived: 06 March 2025, Accepted: 30 May 2025, Published online: 13 June 2025  \nAbstract  \nThis literature review categorizes machine learning studies in traffic accident severity prediction, providing a comprehensive overview of the diverse applications and advancements in this field. It begins with a comparative analysis of machine learning models, highlighting the performance of various algorithms such as Random Forest, XGBoost, and Support Vector Machines (SVM) in predicting accident severity. The review also explores factor-specific studies, emphasizing the influence of road, environmental, and vehicle-related factors on crash outcomes. These studies demonstrate the critical role offactors such as road type, weather conditions, and vehicle characteristics in determining accident severity. Additionally, crash-type-specific prediction models have been developed, showcasing the ability of machine learning models to tailor predictions based on the nature of the crash, whether involving pedestrians, vehicles, or specific collision types. The review also examines hybrid and ensemble approaches, which combine multiple algorithms to enhance prediction accuracy. These approaches leverage the strengths of individual models to improve overall performance, offering a promising direction for future research. By categorizing the studies into these key areas, this review provides a structured understanding of the state-ofthe-art in machine learning applications for traffic accident severity prediction and identifies opportunities for further development to enhance prediction robustness, accuracy, and applicability in real-world traffic safety management.  \nKeywords  \ntraffic accident severity, machine learning models, comparative analysis, hybrid approaches, prediction models  \n1 Introduction  \nAs the global momentum toward sustainable development intensifies, the intersection of traffic safety and sustainability has emerged as an area of critical importance (Török, 2017) . The application of machine learning (ML) models to predict traffic accident severity offers substantial potential to mitigate fatalities and injuries, while simultaneously contributing to the development of safer, more resilient transportation systems that promote long-term environmental and social sustainability (Hee et al., 2024) . A comprehensive understanding and accurate prediction of traffic accident severity is essential for implementing targeted preventive measures and reducing fatalities on the road (Abdullah and Sipos, 2022) . The severity of injuries resulting from road accidents is influenced by a multifaceted interaction of factors, including driver behavior, road infrastructure, vehicle characteristics, and environmental conditions (Jima and Sipos, 2022; Ötvös and Török, 2024) .  \nIdentifying and comprehending these factors is pivotal to enhancing road safety and enacting effective interventions aimed at mitigating crash severity (Sipos et al., 2021; Wang et al., 2023; Zou et al., 2021) .  \nHistorically, traffic injury severity analysis has predominantly relied on statistical methods such as logistic regression and ordered probit models (Garrido et al., 2014; Rifaat and Chin, 2007). While these models have provided valuable ins","cbCailJrFQKE9y8L","https://ap.wps.com/l/cbCailJrFQKE9y8L","pdf",590283,1,9,"English","en",105,"# Introduction\n## Research motivation and significance\n## Traditional statistical approaches vs. machine learning\n# Review scope and research questions\n## Whether studies developed severity prediction models\n## How model performance comparisons were conducted\n## Which models achieved best performance","[{\"question\":\"What is the main focus of the review on traffic accident severity prediction?\",\"answer\":\"The review focuses on how machine learning methods are used to predict traffic accident injury severity, summarizing applications, advancements, and directions for future work.\"},{\"question\":\"Which machine learning algorithms are highlighted in the comparative analysis?\",\"answer\":\"The review highlights Random Forest, XGBoost, and Support Vector Machines (SVM) as representative models discussed for severity prediction performance.\"},{\"question\":\"How do hybrid and ensemble approaches contribute to severity prediction?\",\"answer\":\"Hybrid and ensemble approaches combine multiple algorithms to leverage individual strengths, aiming to improve prediction accuracy and overall robustness for traffic safety management.\"}]","Advancements in Machine Learning for Traffic Accident Severity Prediction - A Comprehensive Review | PDF",1785724753,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"advancements-in-machine-learning-for-traffic-accident-severity-prediction-a-comprehensive-review","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/advancements-in-machine-learning-for-traffic-accident-severity-prediction-a-comprehensive-review/119522/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main focus of the review on traffic accident severity prediction?","Question",{"text":75,"@type":76},"The review focuses on how machine learning methods are used to predict traffic accident injury severity, summarizing applications, advancements, and directions for future work.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are highlighted in the comparative analysis?",{"text":80,"@type":76},"The review highlights Random Forest, XGBoost, and Support Vector Machines (SVM) as representative models discussed for severity prediction performance.",{"name":82,"@type":73,"acceptedAnswer":83},"How do hybrid and ensemble approaches contribute to severity prediction?",{"text":84,"@type":76},"Hybrid and ensemble approaches combine multiple algorithms to leverage individual strengths, aiming to improve prediction accuracy and overall robustness for traffic safety management.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]