Forecast of Guangzhou Port Logistics Demand Based on Back Propagation Neural Network

In recent years, the port economy of our country has developed rapidly. Guangzhou Port is an important node of the maritime transportation of the Belt and Road, connecting the hinterland economy of our country with the countries along the Belt and Road, which is of great significance in promoting the economic development of the hinterland of our country. It is of great significance to predict the freight development demand of Guangzhou port scientifically and reasonably, which is beneficial to optimize the infrastructure construction and logistics system planning of Guangzhou port. This study selects the port cargo throughput, foreign trade cargo throughput, and container cargo throughput as three index values to measure the freight development of Guangzhou port. Firstly, the GM(1,1) model and the BP neural network model are constructed to predict the freight demand of Guangzhou port. Then, the GM(1,1) model and the BP neural network model are combined to predict again. By comparing the three models, the results show that the accuracy of the combined model is better than that of the single model. The combined model of BP neural network and GM(1,1) can be effectively applied in the prediction of Guangzhou port logistics demand. Finally, the combined model of BP neural network and GM(1,1) is used to forecast the freight development demand of Guangzhou Port in 2022-2024, which provides a reference for the development planning of Guangzhou Port. The results further indicate that the BP–GM(1,1) combination model significantly outperforms single forecasting models in terms of prediction accuracy, highlighting its effectiveness and robustness in port logistics demand forecasting.

@article{Chen2026,
  title     = {Forecast of Guangzhou Port Logistics Demand Based on Back Propagation Neural Network},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {1},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Xiu Chen and Lianhua Liu and Lifen Zheng},
  doi       = {10.14569/IJACSA.2026.0170130},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170130}
}