[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124051-en":3,"doc-seo-124051-105":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},124051,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Leveraging Machine Learning to Enhance Road Safety - A Social Marketing Approach","Road safety remains a critical concern worldwide. The research uses Explainable AI (XAI), especially SHAP, to determine key drivers of road-accident severity and to design a social marketing campaign that motivates people to change road-safety behavior. Multiple machine learning models are built from Thailand Ministry of Transport data. Results show the LGBM model achieves 0.85 accuracy and 0.83 F1, while SHAP highlights motorcycle involvement, road code, and total vehicles and people as major factors. A practical promotion framework emphasizes awareness, emotionally impactful messaging, and immediate behavior change, supporting stakeholders in improving interventions.","LEVERAGING MACHINE LEARNING TO ENHANCE ROAD SAFETY: A SOCIAL MARKETING APPROACH  \nWasinee Noonpakdee 1, Manit Satitsamitpong2,*, Prarawan Senachai3, and Kittipong Napontun4  \nAbstract  \nRoad safety remains a critical concern worldwide. This research aims to investigate the use of Explainable AI (XAI) techniques, particularly SHAP (Shapley Additive Explanations), to identify key factors influencing road accident severity and create a social marketing campaign encouraging people to change their behavior in relation to road safety. Several machine learning models were developed using data from Thailand’s Ministry of Transport. The results show that the Light Gradient Boosting Machine (LGBM) model achieved the highest accuracy of 0.85 and an F1 score of 0.83. SHAP analysis revealed that the most significant contributing factors were the number of motorcycle involvements, road code, and the total number of vehicles and people involved in the accident. A practical framework for promoting sustainable road safety was proposed, focusing on raising awareness, delivering emotionally impactful communication, and fostering immediate behavioral change. This research provides valuable insights for strategic road safety initiatives and demonstrates the effectiveness of integrating machine learning with XAI. The findings can guide government authorities, policymakers, insurance companies, and social marketing planners in improving road safety.  \nKeywords: Road accidents, Explainable AI, XAI, SHAP, Severity Prediction, Social Marketing, Insurance Industry  \n1 Asst. Prof. Dr. Wasinee Noonpakdee (First Author) is currently working as a lecturer in the Master of Digital Business Transformation Program at Thammasat University, Thailand. She [received her B.E. degree](received her B.E. degree) in Electrical Engineering from Chulalongkorn University, Thailand, [M.ISM. degree](M.ISM. degree) in Information Systems Management from Carnegie Mellon University, USA, [and D.Sc. degree](and D.Sc. degree) in Information and Telecommunications from the Graduate School of Global Information and Telecommunication Studies (GITS), Waseda University, Japan. Her research interests include optical wireless communications, business intelligence, data analysis, data visualization, machine [learning and data mining. Email: wasinee@citu.tu.ac.th](learning and data mining. Email: wasinee@citu.tu.ac.th)  \n2,* Dr. Manit Satitsamitpong (Corresponding Author) is a lecturer in the Master of Digital Business Transformation Program at Thammasat University, Thailand. He holds a bachelor ’s degree in Computer Science and Engineering from the University of Pennsylvania, an MBA from the University of Illinois, and an MS in Information Technology Management from Georgia Institute of Technology. He received his doctoral degree in Global Information and Telecommunication Studies at Waseda University, Japan. His research interests are in economics and policy issues on digital technology utilization in developing nations as well as in business and technical issues on the impact of digital technology within an organizational context. Email: [manit@citu.tu.ac.th](manit@citu.tu.ac.th)  \n3 Asst. Prof. Dr. Prarawan Senachai (Essentially Intellectual Contributor) is a lecturer in the Department of Marketing at the Faculty of Business Administration and Accountancy at Khon Kaen University in Thailand . She obtained her doctoral degree in Marketing Communication from the Faculty of Arts and Design at the University of Canberra, Australia. Her research interests include Communications & Media, Customer Relationship Management, Service Marketing, and research related to marketing. Email: [prarse@kku.ac.th](prarse@kku.ac.th)  \n4 Kittipong Napontun (Co-Author) is currently an assistant researcher in the Consumer Insights in Sports or Service-Related Business Research Unit at Chulalongkorn University. He is also pursuing a master ’s degree in  \nBranding and Marketing at Chulalongkorn Business [Schoo","cbCaigIInVpNPGni","https://ap.wps.com/l/cbCaigIInVpNPGni","pdf",1165473,1,21,"English","en",105,"# Introduction\n## Global and Thailand road safety context\n## Factors influencing road accidents and impacts on society\n## Need for explainable analysis to support policy and social marketing","[{\"question\":\"What is the main goal of this research?\",\"answer\":\"To use Explainable AI techniques to identify key factors that affect road-accident severity and to design a social marketing approach that encourages behavior change for road safety.\"},{\"question\":\"Which model achieved the best predictive performance?\",\"answer\":\"The Light Gradient Boosting Machine (LGBM) model achieved the highest accuracy of 0.85 and an F1 score of 0.83.\"},{\"question\":\"What factors were found to be most important according to SHAP?\",\"answer\":\"SHAP analysis indicates that the number of motorcycle involvements, road code, and the total number of vehicles and people involved are the most significant contributing factors.\"}]","Leveraging Machine Learning to Enhance Road Safety - 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