نوع مقاله : علمی - پژوهشی
نویسندگان
1 حمل و نقل، دانشکده عمران، دانشگاه علم و صنعت ایران، تهران، ایران
2 استادیار- دانشگاه علم و صنعت ایران، دانشکده عمران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Road crashes cause sociological and economical costs to societies, hence in recent years traffic safety studies has been a major concern. studies have been conducted in regards to crash severity modeling, but the majority of theses studies has traditionally used statistical modeling techniques, and the use of machine learning has been limited. Statistical methods have presupposed some conditions regarding variables and their relations, and if these conditions are violated, inference is affected. A wide spectrum of machine learning algorithm could be employed in crash safety modeling; hence the current study aims to comprehensively compare the performance of 4 widely used machine learning algorithms, namely classification and regression tree, artificial neural network, support vector machine and k-nearest neighbors. Results indicate that CART outperformed other models both in terms of accuracy and the prediction of the less populated crash severity level. Furthermore, the variable importance effected was analyzed using SHAP method, where at fault driver’s age, lighting condition and speed limit were found to be most significant across different models.
کلیدواژهها [English]
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