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Cited by 53Maritime Navigation and SafetyData Management and AlgorithmsAnomaly Detection Techniques and Applications

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A Distributed Spatial Method for Modeling Maritime Routes

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Zissis, Dimitris, Chatzikokolakis, Konstantinos, Spiliopoulos, Giannis, Vodas, Marios·2020·IEEE Access
DOI 10.1109/access.2020.2979612
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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.

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Figures

FIGURE 1 ROTA a spatial knowledge discovery pipeline. The above figure depicts the several steps the methodology proposes and are analysed in detail in the following sections.
FIGURE 1 ROTA a spatial knowledge discovery pipeline. The above figure depicts the several steps the methodology proposes and are analysed in detail in the following sections.CC-BY · From the PDF
FIGURE 2 A depiction of convex hulls or polygons that consist of the positions that belong to each cluster. These are then used to model sea routes (belonging to the same departure and destination port) across the globe.
FIGURE 2 A depiction of convex hulls or polygons that consist of the positions that belong to each cluster. These are then used to model sea routes (belonging to the same departure and destination port) across the globe.CC-BY · From the PDF
FIGURE 3 A random selection of roads of sea. Road of sea as represented as set of convex hulls. Colours depict quantiles of global distribution of the calculated density (yellow is low density and red is high density).
FIGURE 3 A random selection of roads of sea. Road of sea as represented as set of convex hulls. Colours depict quantiles of global distribution of the calculated density (yellow is low density and red is high density).CC-BY · From the PDF
FIGURE 4 Cumulative distribution function of accuracy per number of trips per route.
FIGURE 4 Cumulative distribution function of accuracy per number of trips per route.CC-BY · From the PDF
FIGURE 5 AIS track history for the ''Costa Concordia'' during the time period of the incident while travelling from Civitavecchia to Savona. The green polygons depict the commonly travelled route between these ports, while the red star marks the position of the incident.
FIGURE 5 AIS track history for the ''Costa Concordia'' during the time period of the incident while travelling from Civitavecchia to Savona. The green polygons depict the commonly travelled route between these ports, while the red star marks the position of the incident.CC-BY · From the PDF
FIGURE 6 AIS track history for the ''YM Pluto'' during the time period of the incident while travelling from Ceuta to Rotterdam. The green polygons depict the commonly travelled route between these ports, while the red star marks the position of the incident.
FIGURE 6 AIS track history for the ''YM Pluto'' during the time period of the incident while travelling from Ceuta to Rotterdam. The green polygons depict the commonly travelled route between these ports, while the red star marks the position of the incident.CC-BY · From the PDF
FIGURE 7 AIS track history for the ''Norman Atlantic'' during the time period of the incident while travelling from Patra to Ancona. The green polygons depict the commonly travelled route between these ports, while the red star marks the position of the incident.
FIGURE 7 AIS track history for the ''Norman Atlantic'' during the time period of the incident while travelling from Patra to Ancona. The green polygons depict the commonly travelled route between these ports, while the red star marks the position of the incident.CC-BY · From the PDF
FIGURE 8 AIS track history for the ''KEIT'' during the time period of the incident while travelling from Kiel to Sodertajle. The green polygons depict the commonly travelled route between these ports, while the red star marks the position of the incident.
FIGURE 8 AIS track history for the ''KEIT'' during the time period of the incident while travelling from Kiel to Sodertajle. The green polygons depict the commonly travelled route between these ports, while the red star marks the position of the incident.CC-BY · From the PDF
Image 9
CC-BY · From the PDF
FIGURE 9 AIS track history for ''INDRA II'' during the time period of the incident while travelling from Odessa to Burgas. The green polygons depict the commonly travelled route between these ports.
FIGURE 9 AIS track history for ''INDRA II'' during the time period of the incident while travelling from Odessa to Burgas. The green polygons depict the commonly travelled route between these ports.CC-BY · From the PDF
Image 11
CC-BY · From the PDF
Image 12
CC-BY · From the PDF
Image 13
CC-BY · From the PDF
Image 14
CC-BY · From the PDF

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Metadata

Authors
Zissis, D., Chatzikokolakis, K., Spiliopoulos, G., & Vodas, M.
Affiliations
University of the Aegean (GR)
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2020
Volume
8
Pages
47556-47568
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2020.2979612
OpenAlex
W3011915942
Cited by
53
References
0
Citations by year
2025: 10 · 2024: 10 · 2023: 11 · 2022: 7 · 2021: 10 · 2020: 5
FWCI
4.80
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Access (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
European Metrology Programme for Innovation and Research (825070, 732310)
Topics
  • Physical Sciences › Engineering › Ocean Engineering › Maritime Navigation and Safety
  • Physical Sciences › Computer Science › Information Systems › Data Management and Algorithms
  • Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
Keywords
shipping routes, vessel tracking data, Automatic Identification System, spatiotemporal dynamics, MapReduce, unsupervised learning, maritime safety

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CoChat. (2026). Citation check: A Distributed Spatial Method for Modeling Maritime Routes. https://app.cochat.ai/papers/10.1109/access.2020.2979612
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Zissis, D., Chatzikokolakis, K., Spiliopoulos, G., & Vodas, M. (2020). A Distributed Spatial Method for Modeling Maritime Routes. IEEE Access. https://doi.org/10.1109/access.2020.2979612