Neural network-based prediction system for port throughput: A case study of Ningbo-Zhoushan Port

Abstract

Port throughput is a crucial indicator for assessing the efficiency of port enterprises. Accurately predicting long-term port throughput can help mitigate the risk of investing in major infrastructure during the development process. This study introduces a neural network-based system for predicting port throughput at the Ningbo-Zhoushan Port. By utilizing the grey relational analysis method, we identified 12 highly correlated factors that affect port throughput variation in Ningbo-Zhoushan Port. These factors were then used as input indicators for the BiLSTM model to predict port throughput. Our research indicates that the proposed model achieved a high level of accuracy, with a MAPE value of 2.95%, outperforming commonly used machine learning and neural network models. Additionally, the model's predictions suggest a rapid growth trend in cargo throughput at Ningbo-Zhoushan Port by 2026. Consequently, we provide relevant suggestions to address this trend. Our research can assist port enterprises in making informed decisions, contributing to the field of port logistics.