Short-term Prediction of Outbound Container Arrival Quantity Based on KNN with Deep Learning in Container Terminals

Short-term prediction of outbound container arrival quantity is of great significance to container storage management, equipment planning and other managerial decisions in container terminals. Based on the actual terminal export operation, the problem is decomposed in the dimension of voyages. A KNN with deep learning based framework is proposed to predict the voyage’s container arrival distribution. Then an accumulative calculation module is designed to transform the prediction results of sub-problems into the final predicted arrival quantity. Through multiple experiments carried out to compare the prediction performance with other algorithms, the effectiveness of the proposed method is verified. Therefore, this study has a strong practical application prospect in container terminals.