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]
Due to high socio-economical costs of traffic crashes, accident injury prediction is an important aspect in safety related studies. Additional to statistical methods that have been traditionally developed in crash severity modeling, some machine learning algorithms have also been employed in recent years. Neural Network being one such algorithm, have gained recognition in the safety related fields in recent field. However, as the distribution of crash data might be nonlinear and due to a number of complications, like inability to model unobserved heterogeneity, using the crash data in its original form for neural networks, may not yield the best result. So, the current study, inspired by the KNN algorithm, develops a hidden graph structure on the data, and then using Graph Convolutional Neural Networks, tries to extract more meaningful relations between crash severity and other explanatory variable. Different metrics adopted for the current task show that the performance of the proposed model is significantly improved over other machine learning models.
کلیدواژهها [English]
)