DOLab

Maritime transport handles over 80% of global trade but rising traffic density and congestion have increased collision risk. Traditional assessment methods, including the ship domain and closest point-of-approach approaches, are limited to pairwise interactions and cannot capture regional traffic dynamics. To overcome these shortcomings, we propose a regional collision risk assessment framework integrating AIS-derived relative traffic representations, convolutional neural networks, and explainable artificial intelligence. A gradient-weighted class activation mapping framework generated risk influence distribution maps and a centroid radial distribution, while a novel indicator, the risk influence radius (RIR), quantified radial influence patterns. Experiments with 3,660 low-risk and 366 high-risk cases from South Korean coastal waters revealed that the Visual Geometry Group (VGG) network backbone achieved the highest performance, with an average F1-score of 0.9324, outperforming ResNeXt, EfficientNet, ResNet, and MobileNetV3 models. Statistical tests revealed a significant difference in RIR (p < 0.05) between high- and low-risk collision areas at 10 and 20 km, but not at 30 km. These results confirm RIR as an interpretable indicator of collision risk at regional scales, while highlighting its limitations at broader ranges. The framework combines accuracy with interpretability and supports practical applications such as maritime monitoring, route planning, and early warning systems.applications such as maritime monitoring, route planning, and early warning systems.

Forecasting the maritime economics index, including container volume and Baltic Panamax Index, is essential for long-term planning and decision-making in the shipping industry. However, studies on container volume prediction are not sufficient, and the bulk freight index has highly fluctuating characteristics, which pose a challenge in long-term prediction. This study proposes a new hybrid framework for the long-term prediction of the maritime economics index. The framework consists of time-series decomposition to break down a time-series into several components (trend, seasonality, and residual), a two-stage attention mechanism that prioritizes important variables to increase long-term prediction accuracy and a long short-term memory network that predicts and combines all components to derive the final predictive outcome. Extensive experiments are conducted using the container volume data, bulk freight index data, and various external variables. The proposed framework achieved a better predictive performance than existing time-series methods, including conventional machine learning and deep learning-based models, in the long-term prediction of container volume and the Baltic Panamax Index. Hence, the proposed method can help in decision-making through accurate long-term predictions of the maritime economics index.

Time-series forecasting (TSF) is a traditional problem in the field of artificial intelligence, and models such as recurrent neural network, long short-term memory, and gate recurrent units have contributed to improving its predictive accuracy. Furthermore, model structures have been proposed to combine time-series decomposition methods such as seasonal-trend decomposition using LOESS. However, this approach is learned in an independent model for each component, and therefore, it cannot learn the relationships between the time-series components. In this study, we propose a new neural architecture called a correlation recurrent unit (CRU) that can perform time-series decomposition within a neural cell and learn correlations (autocorrelation and correlation) between each decomposition component. The proposed neural architecture was evaluated through comparative experiments with previous studies using four univariate and four multivariate time-series datasets. The results showed that long- and short-term predictive performance was improved by more than 10%. The experimental results indicate that the proposed CRU is an excellent method for TSF problems compared to other neural architectures.