Multi-class identification of urban bus bunching rate based on XGBoost

Abstract

It is of great practical significance to identify the state of bus bunching in advance so as to take reasonable measures. In order to improve the identification performance, a identification model based on Extreme Gradient Boosting (XGBoost) is proposed for multi classification of bus bunching rate. Firstly, using variance filtering and recursive feature elimination to screen the factors affecting the bus bunching rate. Secondly, SMOTE algorithm is used to deal with the data imbalance. Finally, XGBoost model is used to identify the multi classification of the bus bunching rate, and this paper compares the proposed model with other models. The research shows that the XGBoost model proposed in this paper has the best results in measurement indicators of identification performance, which verifies the applicability of the model to accurately identify the categories of bus bunching rate.