Turnover forecast of passenger transport structure in China based on grey correlation theory

Hang Ke; Guangyin Xu; Chuntang Li; Jing Gao; Runkai Zhang

doi:10.1117/12.2658153

22 December 2022 Turnover forecast of passenger transport structure in China based on grey correlation theory

Hang Ke, Guangyin Xu, Chuntang Li, Jing Gao, Runkai Zhang

Proceedings Volume 12460, International Conference on Smart Transportation and City Engineering (STCE 2022); 1246036 (2022) https://doi.org/10.1117/12.2658153
Event: International Conference on Smart Transportation and City Engineering (STCE 2022), 2022, Chongqing, China

Abstract

The accurate prediction of passenger turnover is an important foundation and one of main basis of passenger transportation organization. It is also an important guarantee for the transportation industry to face the market and grasp the future. This paper used the grey correlation prediction theory to construct the GM (1,1) model. Based on the historical data of China's passenger transport before 2020, this paper respectively predicts the turnover of China's railway passenger transport, highway passenger transport and aviation passenger transport in 2030. This study shows that the prediction accuracy of the model is relatively higher. Prediction values are also basically in line with the actual development of China’s passenger transport in the future. Under the background of carbon peak, the results predicted in this paper can provide reference for the adjustment of the current passenger transport structure, and have certain significance for the development of passenger transport.

Citation Download Citation

Hang Ke, Guangyin Xu, Chuntang Li, Jing Gao, and Runkai Zhang "Turnover forecast of passenger transport structure in China based on grey correlation theory", Proc. SPIE 12460, International Conference on Smart Transportation and City Engineering (STCE 2022), 1246036 (22 December 2022); https://doi.org/10.1117/12.2658153

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