Modeling Maritime Transportation Behavior Using AIS Trajectories and Markovian Processes in the Gulf of St. Lawrence

TL;DR

This paper develops a Markov chain-based framework to analyze vessel movement patterns in the Gulf of St. Lawrence, revealing persistent behaviors and pandemic-induced disruptions across vessel types.

Contribution

It introduces a novel spatio-temporal modeling approach using AIS data and Markov processes to capture maritime behavior and its temporal variations during COVID-19.

Findings

Vessel-specific mobility signatures are consistent across regions.

Pandemic caused temporary disruptions in passenger and fishing vessel patterns.

Method enables detailed behavioral analytics for maritime transportation planning.

Abstract

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.