Actual Truck Arrival Prediction at a Container Terminal with the Truck Appointment System Based on the Long Short-Term Memory and Transformer Model

The implementation of the truck appointment system (TAS) in various ports shows that it can effectively reduce congestion and enhance resource utilization. However, uncertain factors such as traffic and weather conditions usually prevent the external trucks from arriving at the port on time according to the appointed period for container pickup and delivery operations. Comprehensively considering the significant factors associated with truck appointment no-shows, this paper proposes a deep learning model that integrates the long short-term memory (LSTM) network with the transformer architecture based on the cascade structure, namely the LSTM-Transformer model, for actual truck arrival predictions at the container terminal using TAS. The LSTM-Transformer model combines the advantages of LSTM in processing time dependencies and the high efficiency of the transformer in parsing complex data contexts, innovatively addressing the limitations of traditional models when faced with complex data. The experiments executed on two datasets from a container terminal in Tianjin Port, China, demonstrate superior performance for the LSTM-Transformer model over various popular machine learning models such as random forest, XGBoost, LSTM, transformer, and GRU-Transformer. The root mean square error (RMSE) values for the LSTM-Transformer model on two datasets are 0.0352 and 0.0379, and the average improvements are 23.40% and 18.43%, respectively. The results of sensitivity analysis show that possessing advanced knowledge of truck appointments, weather, traffic, and truck no-shows will improve the accuracy of model predictions. Accurate forecasting of actual truck arrivals with the LSTM-Transformer model can significantly enhance the efficiency of container terminal operational planning.