Abstract
Against the background of the epidemic, international air cargo has maintained a high prosperity, and the freight revenue of domestic major airlines has also seen a substantial increase. Accurate forecast of airport cargo flow can provide scientific decision-making basis for airport construction and aviation logistics management. In this paper, based on the GM (1,1) forecast model and markov chain model, the equal-dimensional grey markov model is established. Then, taking the actual value of cargo flow at China’s airports from 2010 to 2019 as the original data, a forecast model is constructed to predict the cargo flow t from 2020 to 2022. The forecast results show that the error between the equal-dimensional new information grey markov forecast model and the original sequence is smaller, its accuracy is better than the traditional grey GM (1,1) model and equal dimensional and new information grey GM (1,1) model, and it can describe the random fluctuation of the forecast results, which is more consistent with the actual situation of airport cargo flow.
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He, H., Ding, H., Liu, S., Li, Y., Zhang, J. (2022). Airport Cargo Volume Forecasting Based on Equal Dimensional and New Information Grey Markov Model. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13339. Springer, Cham. https://doi.org/10.1007/978-3-031-06788-4_23
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DOI: https://doi.org/10.1007/978-3-031-06788-4_23
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