Research on Forecast of Passenger Flow of High Speed Railway in Competitive Market Based on XGBoost Model

Abstract

Forecasting the passenger flow of high speed railway is a hot but difficult research topic in the field of railway transportation for a long time. It is the basis of the revenue management and the rapid response mechanism for the railway sector. With the increasingly fierce competition of aviation and railway, it brings forward new challenges to the forecasting. This paper proposes a new method of passenger flow forecasting of the high speed railway in competitive market based on XGBoost model. The ticket price of the train is also taken into account in the method. First, the flight booking info data is preprocessed and statistics features are calculated to complement the missing data. Then, the railway booking info and flight booking info merge into a large training data and prediction model is constructed by using XGBoost algorithm. Applying this model for prediction, the passenger flow in competitive market can be obtained. Experimental results show that the proposed model has higher prediction accuracy than traditional models.