Leveraging Machine Learning to Enhance Road Safety: A Social Marketing Approach

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Wasinee Noonpakdee
Manit Satitsamitpong
Prarawan Senachai
Kittipong Napontun

Abstract

Road 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.

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References

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