A Distributed Spatial Method for Modeling Maritime Routes

Abstract

In this work we propose a novel spatial knowledge discovery pipeline capable of automatically unravelling the ''roads of the sea'' and maritime traffic patterns by analysing voluminous vessel tracking data, as collected through the Automatic Identification System (AIS). We present a computationally efficient and highly accurate solution, based on a MapReduce approach and unsupervised learning methods, capable of identifying the spatiotemporal dynamics of ship routes and most crucially their characteristics, thus deriving maritime ''patterns of life'' at a global scale, without the reliance on any additional information sources or a priori expert knowledge. Experimental results confirm high accuracy of results and superior performance in comparison to other methods, with the entire processing duration completing in less than 3 hours for more than a terabyte of non-uniform spatial data. Finally, to clearly demonstrate the applicability and impact of our proposed method, we evaluate its ability to detect real world ''anomalies'', such as maritime incidents reported in the European Marine Casualty Information Platform. Numerical results show the advantages of our scheme in terms of accuracy, with an achieved anomaly detection accuracy of higher than 93%, by detecting 313 out of 335 relevant maritime incidents. INDEX TERMS AIS, anomaly detection, data driven maritime traffic, patterns of life, routes.

COVID-19 impact on global maritime mobility

“…AIS has also been used to show how the data association can be significantly improved using the long-term prediction and combining AIS with HF Surface Wave radar (HFSWR) data, or Synthetic Aperture Radar (SAR) data 45 . But in any case, there is a significant literature that considers AIS the sole source of information for maritime surveillance 21 , 45 – 49 , port traffic analysis 50 , 51 , and anomaly detection 52 – 56 ; a common application for historical AIS data is also the training of machine learning, including neural networks, algorithms 48 , 57 , 58 . The interested reader can find in the scientific literature an excellent survey 49 of AIS data exploitation for safety, anomaly detection, route estimation, collision prediction, and path planning.…”

Experimental Comparison of Complex Event Processing Systems in the Maritime Domain

“…The Akka system uses a database of information on common trajectories to detect anomalies on the vessels' movements. This database is created using machine learning algorithms on historical data of vessel trajectories [4]. The results are sets of polygons that define a normal movement trajectory of a vessel, based on the vessel type along with the departure and arrival ports.…”

“…Mining voluminous surveillance datasets to discover frequent patterns and provide accurate insights on vessels' whereabouts is a major step towards digitization of the shipping industry. Even though several data mining techniques that rely on frequent pattern discovery [3], trajectory clustering [4] and vessel classification [3], [5]- [7] have been proposed in the literature, the majority of those follow a batch processing methodology as their focus is on extracting knowledge from historical data, completing the processing workflow within several hours. On the other hand, less attention has been given in handling information coming from high-throughput (and often multiple) sources in true real time and streaming conditions.…”

Toward a Marine Road Network for Ship Passage Planning and Monitoring

“…AIS improves situational awareness of vessels in vicinity and assists mariners in safely navigating their vessels, while historic AIS data provides a means for studying maritime traffic related issues. Aggregated AIS data is illustrative of global ship traffic; many research works have studied methods for deriving usable products from historic AIS data with creating class-specific heat maps of traffic (e.g., Falco et al, 2019), interpolating ship positions where data is missing (e.g., Mao et al, 2018), predicting ship trajectories (e.g., Liu et al, 2019), and extracting predominant routes in grid or vector format (e.g., Guyader et al, 2011;Filipiak et al, 2020), for applications such as identifying anomalous ship behavior (e.g., Zissis et al, 2020), mapping fishing efforts (e.g., Natale et al, 2015), establishing hierarchically related statistical models to simulate traffic and assess navigation risk (e.g., Calder & Schwehr, 2009), assessing shipping energy efficiency (Smith et al, 2013), creating new traffic safety corridors (e.g., ACPARS, 2016), and enhancing cetaceans-ship collision avoidance (e.g., McGillivary et al, 2009). While AIS data establishes the groundwork for creating a network of previously used routes that could aid mariners in safely traversing the seas, we lack a dynamic solution readily available on the bridge and to autonomous vessels to support route planning and monitoring.…”