Maritime transportation is central to the global economy, and analyzing its large-scale behavioral data is critical for operational planning, environmental stewardship, and governance. This work presents a spatio-temporal analytical framework based on discrete-time Markov chains to model vessel movement patterns in the Gulf of St. Lawrence, with particular emphasis on disruptions induced by the COVID-19 pandemic. We discretize the maritime domain into hexagonal cells and construct mobility signatures for distinct vessel types using cell transition frequencies and dwell times. These features are used to build origin-destination matrices and spatial transition probability models that characterize maritime dynamics across multiple temporal resolutions. Focusing on commercial, fishing, and passenger vessels, we analyze the temporal evolution of mobility behaviors during the pandemic, highlighting significant yet transient disruptions to recurring transport patterns. The methodology we contribute to this paper allows for an extensive behavioral analytics key for transportation planning. Accordingly, our findings reveal vessel-specific mobility signatures that persist across spatially disjoint regions, suggesting behaviors invariant to time. In contrast, we observe temporal deviations among passenger and fishing vessels during the pandemic, reflecting the influence of social isolation measures and operational constraints on non-essential maritime transport in this region.

Routing vessels through narrow and dynamic waterways is challenging due to changing environmental conditions and operational constraints. Existing vessel-routing studies typically fail to generalize across multiple origin-destination pairs and do not exploit large-scale, data-driven traffic graphs. In this paper, we propose a reinforcement learning solution for big maritime data that can learn to find a route across multiple origin-destination pairs while adapting to different hexagonal grid resolutions. Agents learn to select direction and speed under continuous observations in a multi-discrete action space. A reward function balances fuel efficiency, travel time, wind resistance, and route diversity, using an Automatic Identification System (AIS)-derived traffic graph with ERA5 wind fields. The approach is demonstrated in the Gulf of St. Lawrence, one of the largest estuaries in the world. We evaluate configurations that combine Proximal Policy Optimization with recurrent networks, invalid-action masking, and exploration strategies. Our experiments demonstrate that action masking yields a clear improvement in policy performance and that supplementing penalty-only feedback with positive shaping rewards produces additional gains.

Accurate vessel trajectory prediction is crucial for navigational safety, route optimization, traffic management, search and rescue operations, and autonomous navigation.Traditional data-driven models lack real-world physical constraints, leading to forecasts that violate vessel motion dynamics, such as in scenarios with limited or noisy data where sudden course changes or speed variations occur due to external factors.To address this limitation, we propose a Physics-Informed Neural Network (PINN) approach for trajectory prediction that integrates a streamlined kinematic model for vessel motion into the neural network training process via firstand second-order, finite-difference physics-based loss functions.These loss functions, discretized using the first-order forward Euler method, Heun's second-order approximation, and refined with a midpoint approximation based on Taylor series expansion, enforce fidelity to fundamental physical principles by penalizing deviations from expected kinematic behavior.We evaluated PINN using real-world AIS datasets that cover diverse maritime conditions and compared it with state-of-the-art models.Our results demonstrate that the proposed method reduces average displacement errors by up to 32% across models and datasets while maintaining physical consistency.These results enhance model reliability and adherence to mission-critical maritime activities, where precision translates into better situational awareness in the oceans.

Seaports play a crucial role in the global economy, and researchers have sought to understand their significance through various studies.In this paper, we aim to explore the common characteristics shared by important ports by analyzing the network of connections formed by vessel movement among them.To accomplish this task, we adopt a bottom-up network construction approach that combines three years' worth of AIS (Automatic Identification System) data from around the world, constructing a Ports Network that represents the connections between different ports.Through this representation, we utilize machine learning to assess the relative significance of various port features.Our model examined such features and revealed that geographical characteristics and the port's depth are indicators of a port's importance to the Ports Network.Accordingly, this study employs a data-driven approach and utilizes machine learning to provide a comprehensive understanding of the factors contributing to the extent of ports.Our work aims to inform decision-making processes related to port development, resource allocation, and infrastructure planning within the industry.