چکیده مقاله
Road accidents present a significant challenge globally, impacting economies, national resources, and human lives This study focuses on analyzing traffic accident data from North Carolina to identify the factors influencing crash severity Utilizing data collected over several years, five machine learning techniques including Logistic Regression, XGBoost, Random Forest, K Nearest Neighbors, and Decision Tree were rigorously evaluated Among these, XGBoost demonstrated superior performance, achieving an accuracy of 91% Detailed analyses, including Confusion Matrix Analysis and ROC AUC Analysis, further validated the effectiveness of the algorithms Additionally, SHAP values analysis was employed to pinpoint key factors contributing to accident severity These findings highlight the critical role of diverse features in accurately predicting road accident severity and emphasize the potential of advanced machine learning methods in enhancing road safety measures
کلیدواژهها
نویسندگان
Kimia Fazli Garmestani
dept Industrial Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran
Reza Samizadeh
dept Industrial Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran
شیوه ارجاع
Fazli Garmestani, Kimia and Samizadeh, Reza,1403,Identifying Key Factors in Traffic Accident Severity: A SHAP and Machine Learning Approach in North Carolina,The 10th International Conference on Industrial and Systems Engineering,Mashhad
ارائهشده در
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