Forecast of port freight volume based on grey RBF neural network combination model

Yang Jiao; Lianbo Li; Zhenyu Zhu; Wenhao Wu

doi:10.1117/12.2624229

6 February 2022 Forecast of port freight volume based on grey RBF neural network combination model

Yang Jiao, Lianbo Li, Zhenyu Zhu, Wenhao Wu

Proceedings Volume 12081, Sixth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2021); 1208121 (2022) https://doi.org/10.1117/12.2624229
Event: Sixth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2021), 2021, Chongqing, China

Abstract

The prediction of port freight volume is of great significance to transportation and port planning. Based on the characteristics of grey GM (1,1) model and RBF neural network model, a combined forecasting model based on grey GM (1,1) model and RBF neural network model is constructed, and the port freight volume is predicted by field survey data. Experiments show that grey RBF neural network can improve the forecasting accuracy, which is effective and feasible for freight volume forecasting.

Citation Download Citation

Yang Jiao, Lianbo Li, Zhenyu Zhu, and Wenhao Wu "Forecast of port freight volume based on grey RBF neural network combination model", Proc. SPIE 12081, Sixth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2021), 1208121 (6 February 2022); https://doi.org/10.1117/12.2624229

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