MATNEC: AIS data-driven environment-adaptive maritime traffic network construction for realistic route generation

In the context of the global maritime industry, which plays a vital role in international trade, navigating vessels safely and efficiently remains a complex challenge, especially due to the absence of structured road-like networks on the open seas. This paper proposes MATNEC , a framework for constructing a data-driven Maritime Traffic Network (MTN), represented as a graph that facilitates realistic route generation. Our approach, leveraging Automatic Identification System (AIS) data along with portcall and global coastline datasets, aims to address key challenges in MTN construction from AIS data observed in the literature, particularly the imprecise placement of network nodes and sub-optimal definition of network edges. At the core of MATNEC is a novel incremental clustering algorithm that is capable of intelligently determining the placement and distribution of the graph nodes in a diverse set of environments, based on several environmental factors. To ensure that the resulting MTN generates maritime routes as realistic as possible, we design a novel edge mapping algorithm that defines the edges of the network by treating the mapping of AIS trajectories to network nodes as an optimisation problem. Finally, due to the absence of a unified approach in the literature for measuring the efficacy of an MTN’s ability to generate realistic routes, we propose a novel methodology to address this gap. Utilising our proposed evaluation methodology, we compare MATNEC with existing methods from literature. The outcome of these experiments affirm the enhanced performance of MATNEC compared to previous approaches. • End-to-end MTN framework using AIS, portcall, coastline data for route optimisation. • Environment adaptive clustering algorithm for identifying nodes. • Optimised MTN connectivity using a sliding window edge mapping algorithm. • Qualitative and quantitative evaluation metrics for MTN validation.