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  • 1.
    Osekowska, Ewa
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Design and Implementation of a Maritime Traffic Modeling and Anomaly Detection Method2014Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Nowadays ships are usually equipped with a system of marine instruments, one of which is an Automatic Identification System (AIS) transponder. The availability of the global AIS ship tracking data opened the possibilities to develop maritime security far beyond the simple collision prevention. The research work summarized in this thesis explores this opportunity, with the aim of developing an intuitive and comprehensible method for traffic modeling and anomaly detection in the maritime domain. The novelty of the method lays in employing the technique of artificial potential fields. The general idea is for the potentials to represent typical patterns of vessels' behaviors. A conflict between potentials, which have been observed in the past, and the potential of a vessel currently in motion, indicates an anomaly. The developed potential field based method has been examined using a web-based anomaly detection system STRAND (for Seafaring TRansport ANomaly Detection). Its applicability has been demonstrated in several publications, examining its scalability, modeling capabilities and detection performance. The experimental investigations led to identifying optimal detection resolution for different traffic areas (open sea, harbor and river), and extracting traffic rules, e.g., with regard to speed limits and course, i.e., right-hand sailing rule. The map-based display of modeled traffic patterns and detection cases has been analyzed as well, using several demonstrative cases. The massive AIS database created for this study, together with a dataset of real traffic incidents, provides an abundance of challenges for future studies.

  • 2.
    Osekowska, Ewa
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Axelsson, Stefan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Carlsson, Bengt
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Potential fields in maritime anomaly detection2013Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel approach for pattern extraction and anomaly detection in mari- time vessel traffic, based on the theory of potential fields. Potential fields are used to rep- resent and model normal, i.e. correct, behaviour in maritime transportation, observed in historical vessel tracks. The recorded paths of each maritime vessel generate potentials based on metrics such as geographical location, course, velocity, and type of vessel, resulting in a potential-based model of maritime traffic patterns. A prototype system STRAND, developed for this study, computes and displays distinctive traffic patterns as potential fields on a geographic representation of the sea. The system builds a model of normal behaviour, by collating and smoothing historical vessel tracks. The resulting visual presentation exposes distinct patterns of normal behaviour inherent in the recorded maritime traffic data. Based on the created model of normality, the system can then perform anomaly detection on current real-world maritime traffic data. Anomalies are detected as conflicts between vessel’s potential in live data, and the local history-based potential field. The resulting detection performance is tested on AIS maritime tracking data from the Baltic region, and varies depending on the type of potential. The potential field based approach contributes to maritime situational awareness and enables automatic detection. The results show that anomalous behaviours in maritime traffic can be detected using this method, with varying performance, necessitating further study.

  • 3.
    Osekowska, Ewa
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Axelsson, Stefan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Carlsson, Bengt
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Potential fields in modeling transport over water2015In: Operations Research/Computer Science Interfaces Series, ISSN 1387-666X, Vol. 58, 259-280 p.Article in journal (Refereed)
    Abstract [en]

    Without an explicit road-like regulation, following the proper sailing routes and practices is still a challenge mostly addressed using seamen’s know-how and experience. This chapter focuses on the problem of modeling ship movements over water with the aim to extract and represent this kind of knowledge. The purpose of the developed modeling method, inspired by the theory of potential fields, is to capture the process of navigation and piloting through the observation of ship behaviors in transport over water on narrow waterways. When successfully modeled, that knowledge can be subsequently used for various purposes. Here, the models of typical ship movements and behaviors are used to provide a visual insight into the actual normal traffic properties (maritime situational awareness) and to warn about potentially dangerous traffic behaviors (anomaly detection). A traffic modeling and anomaly detection prototype system STRAND implements the potential field based method for a collected set of AIS data. A quantitative case study is taken out to evaluate the applicability and performance of the implemented modeling method. The case study focuses on quantifying the detections for varying geographical resolution of the detection process. The potential fields extract and visualize the actual behavior patterns, such as right-hand sailing rule and speed limits, without any prior assumptions or information introduced in advance. The display of patterns of correct (normal) behavior aids the choice of an optimal path, in contrast to the anomaly detection which notifies about possible traffic incidents. A tool visualizing the potential fields may aid traffic surveillance and incident response, help recognize traffic regulation and legislative issues, and facilitate the process of waterways development and maintenance. © Springer International Publishing Switzerland 2015.

  • 4.
    Osekowska, Ewa
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. Blekinge Tekniska Hogskola, S-37179 Karlskrona, Sweden..
    Carlsson, Bengt
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering. Blekinge Tekniska Hogskola, S-37179 Karlskrona, Sweden..
    Learning Maritime Traffic Rules Using Potential Fields2015In: COMPUTATIONAL LOGISTICS (ICCL 2015), 2015, 298-312 p.Conference paper (Refereed)
    Abstract [en]

    The Automatic Identification System (AIS) is used to identify and locate active maritime vessels. Datasets of AIS messages recorded over time make it possible to model ship movements and analyze traffic events. Here, the maritime traffic is modeled using a potential fields method, enabling the extraction of traffic patterns and anomaly detection. A software tool named STRAND, implementing the modeling method, displays real-world ship behavior patterns, and is shown to generate traffic rules spontaneously. STRAND aids maritime situational awareness by displaying patterns of common behaviors and highlighting suspicious events, i.e., abstracting informative content from the raw AIS data and presenting it to the user. In this it can support decisions regarding, e.g., itinerary planning, routing, rescue operations, or even legislative traffic regulation. This study in particular focuses on identification and analysis of traffic rules discovered based on the computed traffic models. The case study demonstrates and compares results from three different areas, and corresponding traffic rules identified in course of the result analysis. The ability to capture distinctive, repetitive traffic behaviors in a quantitative, automatized manner may enhance detection and provide additional information about sailing practices.

  • 5.
    Osekowska, Ewa
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Johnson, Henric
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Carlsson, Bengt
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Grid size optimization for potential field based maritime anomaly detection2014In: 17TH MEETING OF THE EURO WORKING GROUP ON TRANSPORTATION, EWGT2014 / [ed] Benitez, FG Rossi, R, ELSEVIER SCIENCE BV , 2014, 720-729 p.Conference paper (Refereed)
    Abstract [en]

    This study focuses on improving the potential field based maritime data modeling method, developed to extract traffic patterns and detect anomalies, in a clear, understandable and informative way. The method's novelty lies in employing the concept of a potential field for AIS vessel tracking data abstraction and maritime traffic representation. Unlike the traditional maritime surveillance equipment, such as radar or GPS, the AIS system comprehensively represents the identity and properties of a vessel, as well as its behavior, thus preserving the effects of navigational decisions, based on the skills of experienced seamen. In the developed data modeling process, every vessel generates potential charges, which value represent the vessel's behavior, and drops the charges at locations it passes. Each AIS report is used to assign a potential charge at the reported vessel positions. The method derives three construction elements, which define, firstly, how charges are accumulated, secondly, how a charge decays over time, and thirdly, in what way the potential is distributed around the source charge. The collection of potential fields represents a model of normal behavior, and vessels not conforming to it are marked as anomalous. In the anomaly detection prototype system STRAND, the sensitivity of anomaly detection can be modified by setting a geographical coordinate grid precision to more dense or coarse. The objective of this study is to identify the optimal grid size for two different conditions an open sea and a port area case. A noticeable shift can be observed between the results for the open sea and the port area. The plotted detection rates converge towards an optimal ratio for smaller grid sizes in the port area (60-200 meters), than in the open sea case (300-1000 meters). The effective outcome of the potential filed based anomaly detection is filtering out all vessels behaving normally and presenting a set of anomalies, for a subsequent incident analysis using STRAND as an information visualization tool.

  • 6.
    Osekowska, Ewa
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Johnson, Henric
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Carlsson, Bengt
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Maritime vessel traffic modeling in the context of concept drift2017In: Transportation Research Procedia, Elsevier, 2017, Vol. 25, 1457-1476 p.Conference paper (Refereed)
    Abstract [en]

    Maritime traffic modeling serves the purpose of extracting human-readable information and discovering knowledge in the otherwise illegible mass of traffic data. The goal of this study is to examine the presence and character of fluctuations in maritime traffic patterns. The main objective is to identify such fluctuations and capture them in terms of a concept drift, i.e., unforeseen shifts in statistical properties of the modeled target occurring over time. The empirical study is based on a collection of AIS vessel tracking data, spanning over a year. The scope of the study limits the AIS data area to the Baltic region (9-31°E, 53-66°N), which experiences some of the most dense maritime traffic in the world. The investigations employ a novel maritime traffic modeling method based on the potential fields concept, adapted for this study to facilitate the examination of concept drift. The concept drift is made apparent in course of the statistical and visual analysis of the experimental results. This study shows a number of particular cases, in which the maritime traffic is affected by concept drifts of varying extent and character. The visual representations of the traffic models make shifts in the traffic patterns apparent and comprehensible to human eye. Based on the experimental outcomes, the robustness of the modeling method against concept drift in traffic is discussed and improvements are proposed. The outcomes provide insights into regularly reoccurring drifts and irregularities within the traffic data itself that may serve to further optimize the modeling method, and - in turn - the performance of detection based on it. © 2017 The Authors. Published by Elsevier B. V.

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