Prediction of traffic accident impact range based on CatBoost ensemble algorithm

Abstract

Aiming at the problem that the traditional algorithm is easy to overfitting, which leads to low prediction accuracy of the model. This paper designs a traffic accident impact range prediction model based on CatBoost ensemble algorithm. The model uses linear fitting for range prediction and uses the ordered boosting method to introduce the prior term and weight coefficient. It can automatically adjust dynamically in each calculation, so as to effectively avoid the condition offset and gradient deviation and reduce the overfitting. Under small-scale training, the algorithm can achieve high accuracy prediction and has strong generalization ability.