JAMC
Analysis of the Relationship Between Railway Passenger Traffic and Turnover in China Based on XGBoost Algorithm
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Abstract
As an important part of China's modern transportation system, high-speed rail is of great significance to economic and social development. Accurate prediction of the relationship between passenger traffic volume and turnover of high-speed rail is valuable for optimizing resource allocation, improving service quality, and decision support. Based on the XGBoost algorithm, this study explores the relationship between high-speed rail passenger traffic and turnover in China and constructs a reliable prediction model. The study first collects historical data including high-speed rail passenger transportation volume, turnover volume, and related influencing factors, and preprocesses and characterizes the data for feature engineering. Subsequently, the XGBoost algorithm is used for modeling and prediction, and it is compared with traditional prediction methods. The results show that the XGBoost algorithm performs well in the prediction of high-speed rail passen-ger transportation and turnover, and has higher prediction accuracy and stability compared with traditional methods. Meanwhile, the complex nonlinear relationship between passenger traffic and turnover and the importance ranking of influencing factors are revealed through model interpretive analysis. Future research can further optimize feature engineering, consider spatio-temporal data and multi-source data fusion, enhance model interpretability, cross-region forecasting, and decision support, and extend the XGBoost algorithm to other transportation fields. This study provides effective decision support for high-speed rail operating companies and government departments, as well as new ideas and methods for related research in the field of transportation. It is hoped that this study can promote the continuous development and innovation of China's high-speed rail business and help optimize and enhance the transportation system.
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How to cite this paper
Analysis of the Relationship Between Railway Passenger Traffic and Turnover in China Based on XGBoost Algorithm
How to cite this paper: Yunkai He, Xin Sui, Liyuan Guo, Yangcheng Wu, Hao Wei. (2023) Analysis of the Relationship Between Railway Passenger Traffic and Turnover in China Based on XGBoost Algorithm. Journal of Applied Mathematics and Computation, 7(3), 381-386.
DOI: http://dx.doi.org/10.26855/jamc.2023.09.008
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