[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123967-en":3,"doc-seo-123967-105":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},123967,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Gauging road safety advances using a hybrid EWM–PROMETHEE II–DBSCAN model with machine learning","Road safety improvements reduce socioeconomic harm from traffic accidents and support public health, yet progress must be monitored with methods that are defensible, reliable, and stable. A hybrid decision-making framework combining the entropy weight method (EWM), PROMETHEE II, and DBSCAN is developed to enable road safety monitoring, recalibration, and action planning. DBSCAN is enhanced with machine learning grid search to optimize neighborhood radius and minimum points, improving clustering quality in complex settings. Results from a Southeast Asia case study confirm robustness and inform policy decisions.","TYPE Original Research PUBLISHED 22 August 2024  \nDOI 10.3389/fpubh.2024.1413031  \nOPEN ACCESS  \nEDITED BY  \nJaeyoung Jay Lee,  \nCentral South University, China  \nREVIEWED BY  \nAmjad Pervez,  \nCentral South University, China Dongdong Wang,  \nUniversity of Central Florida, United States  \n*CORRESPONDENCE  \nFaan Chen  \n [faanchen@seas.harvard.edu](faanchen@seas.harvard.edu)  \nRECEIVED 06 April 2024  \nACCEPTED 08 August 2024  \nPUBLISHED 22 August 2024  \nCITATION  \nLi J and Chen F (2024) Gauging road safety advances using a hybrid EWM–PROMETHEE II–DBSCAN model with machine learning. Front. Public Health 12:1413031 .  \ndoi: 10.3389/fpubh.2024.1413031  \nCOPYRIGHT  \n© 2024 Li and Chen. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nGauging road safety advances using a hybrid EWM– PROMETHEE II–DBSCAN model with machine learning  \nJialin Li 1 and Faan Chen 2*  \n1College of Arts and Science, Vanderbilt University, Nashville, TN, United States, 2School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, United States  \nIntroduction: Enhancing road safety conditions alleviates socioeconomic hazards from traffic accidents and promotes public health. Monitoring progress and recalibrating measures are indispensable in this effort. A systematic and scientific decision-making model that can achieve defensible decision outputs with substantial reliability and stability is essential, particularly for road safety system analyses.  \nMethods: We developed a systematic methodology combining the entropy weight method (EWM), preference ranking organization method for enrichment evaluation (PROMETHEE), and density-based spatial clustering of applications with noise (DBSCAN)—referred to as EWM–PROMETHEE II–DBSCAN—to support road safety monitoring, recalibrating measures, and action planning. Notably, we enhanced DBSCAN with a machine learning algorithm (grid search) to determine the optimal parameters of neighborhood radius and minimum number of points, significantly impacting clustering quality.  \nResults: In a real case study assessing road safety in Southeast Asia, the multilevel comparisons validate the robustness of the proposed model, demonstrating its effectiveness in road safety decision-making. The integration of a machine learning tool (grid search) with the traditional DBSCAN clustering technique forms a robust framework, improving data analysis in complex environments. This framework addresses DBSCAN’s limitations in nearest neighbor search and parameter selection, yielding more reliable decision outcomes, especially in small sample scenarios. The empirical results provide detailed insights into road safety performance and potential areas for improvement within Southeast Asia.  \nConclusion: The proposed methodology offers governmental officials and managers a credible tool for monitoring overall road safety conditions. Furthermore, it enables policymakers and legislators to identify strengths and drawbacks and formulate defensible policies and strategies to optimize regional road safety.  \nKEYWORDS  \npublic health, road safety, decision reliability, Southeast Asia, policymaking, machine learning  \nFrontiers in Public Health 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nAs of 2019, road traffic accidents were the 12th leading cause of death for all age groups ( 1) . This significant loss of life severely impacts human development, exacerbates poverty, negatively affects victims and their families, and has an aggregated effect on the gross domestic product of approximately 3% annually (2) . In response to these se","cbCaitmWDf5EUstR","https://ap.wps.com/l/cbCaitmWDf5EUstR","pdf",3192840,1,21,"English","en",105,"# Introduction\n# Methods\n# Results\n# Conclusion","[{\"question\":\"What hybrid model is proposed to gauge road safety advances?\",\"answer\":\"The framework combines the entropy weight method (EWM), PROMETHEE II, and DBSCAN (EWM–PROMETHEE II–DBSCAN) to support monitoring, recalibration, and action planning.\"},{\"question\":\"How is DBSCAN improved in the proposed approach?\",\"answer\":\"DBSCAN is enhanced using machine learning grid search to identify optimal parameters, especially neighborhood radius and the minimum number of points, which improves clustering quality.\"},{\"question\":\"What evidence is provided to support the model’s reliability?\",\"answer\":\"A real case study in Southeast Asia uses multilevel comparisons that validate the robustness of the model and demonstrate effective decision-making under complex conditions.\"}]","Gauging road safety advances using a hybrid EWM–PROMETHEE II–DBSCAN model with machine learning | PDF",1785819480,53,{"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},"gauging-road-safety-advances-using-a-hybrid-ewmpromethee-iidbscan-model-with-machine-learning","",{"@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/gauging-road-safety-advances-using-a-hybrid-ewmpromethee-iidbscan-model-with-machine-learning/123967/",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","2026-08-04",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 hybrid model is proposed to gauge road safety advances?","Question",{"text":75,"@type":76},"The framework combines the entropy weight method (EWM), PROMETHEE II, and DBSCAN (EWM–PROMETHEE II–DBSCAN) to support monitoring, recalibration, and action planning.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is DBSCAN improved in the proposed approach?",{"text":80,"@type":76},"DBSCAN is enhanced using machine learning grid search to identify optimal parameters, especially neighborhood radius and the minimum number of points, which improves clustering quality.",{"name":82,"@type":73,"acceptedAnswer":83},"What evidence is provided to support the model’s reliability?",{"text":84,"@type":76},"A real case study in Southeast Asia uses multilevel comparisons that validate the robustness of the model and demonstrate effective decision-making under complex conditions.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]