Adaptive genetic algorithm-optimized temporal convolutional networks for high-precision ship traffic flow prediction

Timely prediction of Ship Traffic Flow (STF) is essential for managing maritime traffic and preventing congestion. However, existing deep neural network-based STF models often face challenges with hyperparameter selection and limited accuracy improvements. This study introduces a Temporal Convolutional Network (TCN) model optimized by an Adaptive Genetic Algorithm (AGA) to address these issues. The methodology begins with comprehensive data preprocessing, using gate-line-based rules to analyze ship traffic entering and leaving ports, leveraging Automatic Identification System (AIS) data. The AGA-TCN model then employs causally dilated convolutions to capture long-term dependencies and extract frequency domain features, with the AGA dynamically optimizing TCN hyperparameters for specific prediction tasks, resulting in an end-to-end STF prediction framework. AIS data from San Francisco waters, covering the period from June 1, 2022, to December 14, 2022, was used to evaluate the model. The performance of the AGA-TCN model was compared against Particle Swarm Optimization (PSO)-TCN, standard TCN, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models. These models were chosen for comparison due to their widespread use in time-series prediction tasks, representing a variety of approaches in deep learning and optimization. The experiments demonstrate that the AGA-TCN model outperformed all these models, with improvements in RMSE, MSE, and MAPE of 54.37%, 79.18%, and 27.43%, respectively, over the standard TCN. These results underscore the robustness and high accuracy of the AGA-TCN model in STF prediction, establishing it as a superior approach for this application.