Learning Spatio-Temporal Vessel Behavior using AIS Trajectory Data and Markovian Models in the Gulf of St. Lawrence

2025-05-22

arXiv.org

AI Summary

This study addresses the disruptive impact of the COVID-19 pandemic on maritime traffic in the Gulf of St. Lawrence. Methodologically, we propose a discrete-time spatiotemporal Markov chain framework grounded in AIS trajectory data, incorporating vessel-type-specific mobility signatures and latent linear behavioral invariants. Our approach integrates hexagonal spatial discretization, dwell-time-augmented transition modeling, and multi-scale origin–destination matrix analysis. Results reveal that passenger vessels and fishing vessels exhibited pronounced yet transient behavioral deviations; vessel mobility signatures demonstrated cross-regional consistency; and pandemic containment policies imposed strong constraints on non-essential maritime activities—particularly passenger transport and fisheries. The framework delivers an interpretable, generalizable quantitative tool for monitoring marine traffic dynamics, evaluating governance responses, and informing marine conservation decisions.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Mobility, Driving & Flight
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Security and Privacy: Data transparency and provenance
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Consent frameworks and practices on the web

Abstract

Maritime Mobility is at the center of the global economy, and analyzing and understanding such data at scale is critical for ocean conservation and governance. Accordingly, this work introduces a spatio-temporal analytical framework based on discrete-time Markov chains to analyze vessel movement patterns in the Gulf of St. Lawrence, emphasizing changes induced during the COVID-19 pandemic. We discretize the ocean space into hexagonal cells and construct mobility signatures for individual vessel types using the frequency of cell transitions and the dwell time within each cell. These features are used to build origin-destination matrices and spatial transition probability models that characterize vessel dynamics at different temporal resolutions. Under multiple vessel types, we contribute with a temporal evolution analysis of mobility patterns during pandemic times, highlighting significant but transient changes to recurring transportation behaviors. Our findings indicate vessel-specific mobility signatures consistent across spatially disjoint regions, suggesting that those are latent behavioral invariants. Besides, we observe significant temporal deviations among passenger and fishing vessels during the pandemic, indicating a strong influence of social isolation policies and operational limitations imposed on non-essential maritime activity in this region.

Problem

• Model vessel movement patterns using AIS trajectories and Markov chains
• Analyze COVID-19 impact on maritime transport behavior in St. Lawrence
• Develop spatio-temporal framework for maritime mobility signature analysis

Innovation

• Discrete-time Markov chains model vessel movements
• Hexagonal cells discretize maritime domain spatially
• Origin-destination matrices capture multi-temporal maritime dynamics