A kNN Based Voyage’s Containers’ Entering Time Distribution Prediction System

Compared with the air transportation and land transportation, water transportation has many advantages such as larger loading capacity, lower unit transportation cost, lower construction investment and so on. What’s more, water transportation has played an important role in the economical development of China, especially in the aspect of international trade. Therefore, the improvement in the efficiency of water transportation will be of great significance. In this paper, we designed a system to predict the containers’ entering time distribution of a given voyage at a specific port by using machine learning algorithms and statistical methods. Using Shanghai Yangshan Port phase IV automated terminal’s data, we perform some experiments, and the result shows that our system can provide valid predictions.