Abstract
The effective forecast of container volumes can provide decision support for port scheduling and operating. In this work, by deep learning the historical dataset, the long short-term memory (LSTM) recurrent neural network (RNN) is used to predict daily volumes of containers which will enter the storage yard. The raw dataset of daily container volumes in a certain port is chosen as the training set and preprocessed with box plot. Then the LSTM model is established with Python and Tensorflow framework. The comparison between LSTM and other prediction methods like ARIMA model and BP neural network is also provided in this study, and the prediction gap of LSTM is lower than other methods. It is promising that the proposed LSTM is helpful to predict the daily volumes of containers.
Highlights
In the times of big data, a good forecasting result is helpful to provide decision-makers with strong decision-making basis. e daily container volumes of storage yard refer to the amount of containers which enter the storage yard every day before the container ships enter the port. e prediction of daily container volumes is of great signi cance to the terminal yard operation plan and the ship loading plan
Arti cial neural network (ANN) has been widely used in the planning and prediction of container terminals. ere have been studies shown that articial neural networks can be used to simulate port planning problems associated with container terminals based on historical data, and the predictions can be considered as acceptable [10]. e ANN has been established between the operational parameters and the static heeling angle, and can provide an accurate estimate of the static heeling angle in order to assess the anchor handling vessel stability [11]
Nigri et al [26] applied Long Short-Term Memory (LSTM) architecture into the Lee-Carter model to improve the predictive accuracy of mortality. e comparison with Autoregressive Integrated Moving Average Model (ARIMA) was given and the superiority of LSTM was demonstrated
Summary
In the times of big data, a good forecasting result is helpful to provide decision-makers with strong decision-making basis. e daily container volumes of storage yard refer to the amount of containers which enter the storage yard every day before the container ships enter the port. e prediction of daily container volumes is of great signi cance to the terminal yard operation plan and the ship loading plan. E Long Short-Term Memory (LSTM) is an improved Recurrent Neural Network (RNN) to overcome the vanishing/exploding gradients problem [15]. A container volumes prediction model using deep learning method is proposed and applied to predict the daily volumes of containers which enter the storage yard at the container terminal. (3) e comparison between LSTM, ARIMA, and BP neural network is given to demonstrate the superiority of LSTM when dealing with the prediction of daily container volumes. Rough observing and training the historical data about container volumes which were transported by container ships to the container terminal, the prediction of daily container volumes is given in this study for the purpose of providing the data support when designing the yard storage plan. LSTM can satisfy the dependence of the data source on the time series, and achieve higher prediction accuracy
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