Container terminal daily gate in and gate out forecasting using machine learning methods

Abstract

Container throughput is an essential indicator for measuring the container terminal’s efficiency. Gate in and gate out containers are the containers that are transported to and transported out of the terminal respectively. Containers are stacked in the container yard before they leave the terminal. Handling of these containers accounts for a major workload at the terminal. Therefore, accurate short-term forecasting of the daily gate in and gate out containers at a container terminal is critical for operational planning. While most forecasts are made for the strategical level of the overall container throughput, this paper focuses on the daily gate in and gate out container quantities with a case study of Ningbo Zhoushan Port Beilun Second Container Terminal. Traditional approaches are mainly time series prediction algorithms that purely rely on the previously observed values. This paper proposes a novel decomposition-ensemble methodology framework which first divides the daily forecasting into a group of forecasting on a set of vessels which arrive in a certain time range around the predicted day. A novel machine learning method, “Extreme Gradient Boost (XGBoost)”, is used for the vessel level of forecasting, where some temporal-related features of vessels are considered in the model. We then ensemble the predicated value of all vessels in the set as the total gate in/out amount for a day. Experimental results demonstrate that our proposed method achieves notable performance gains compared to the time series-based method ARIMA. In addition, the results of this new technology can be fed into the terminal operating system (ToS) in the Ningbo Beilun container terminal for better management. Specifically, the prediction results facilitate effective real-time decision-making for operational management and policy drafting, such as workload planning, equipment scheduling, yard planning, etc.