Multiple-Aspect Analysis of Semantic Trajectories

This open access book constitutes the refereed post-conference proceedings of the First International Workshop on Multiple-Aspect Analysis of Semantic Trajectories, MASTER 2019, held in conjunction with the 19th European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, in Würzburg, Germany, in September 2019. The 8 full papers presented were carefully reviewed and selected from 12 submissions. They represent an interesting mix of techniques to solve recurrent as well as new problems in the semantic trajectory domain, such as data representation models, data management systems, machine learning approaches for anomaly detection, and common pathways identification

GeoTrackNet-A Maritime Anomaly Detector using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection

Representing maritime traffic patterns and detecting anomalies from them are key to vessel monitoring and maritime situational awareness. We propose a novel approach-referred to as GeoTrackNet-for maritime anomaly detection from AIS data streams. Our model exploits state-of-the-art neural network schemes to learn a probabilistic representation of AIS tracks, then uses a contrario detection to detect abnormal events. The neural network helps us capture complex and heterogeneous patterns in vessels' behaviors, while the a contrario detection takes into account the fact that the learned distribution may be location-dependent. Experiments on a real AIS dataset comprising more than 4.2 million AIS messages demonstrate the relevance of the proposed method

Deep Learning Methods for Vessel Trajectory Prediction based on Recurrent Neural Networks

Data-driven methods open up unprecedented possibilities for maritime surveillance using Automatic Identification System (AIS) data. In this work, we explore deep learning strategies using historical AIS observations to address the problem of predicting future vessel trajectories with a prediction horizon of several hours. We propose novel sequence-to-sequence vessel trajectory prediction models based on encoder-decoder recurrent neural networks (RNNs) that are trained on historical trajectory data to predict future trajectory samples given previous observations. The proposed architecture combines Long Short-Term Memory (LSTM) RNNs for sequence modeling to encode the observed data and generate future predictions with different intermediate aggregation layers to capture space-time dependencies in sequential data. Experimental results on vessel trajectories from an AIS dataset made freely available by the Danish Maritime Authority show the effectiveness of deep-learning methods for trajectory prediction based on sequence-to-sequence neural networks, which achieve better performance than baseline approaches based on linear regression or on the Multi-Layer Perceptron (MLP) architecture. The comparative evaluation of results shows: i) the superiority of attention pooling over static pooling for the specific application, and ii) the remarkable performance improvement that can be obtained with labeled trajectories, i.e., when predictions are conditioned on a low-level context representation encoded from the sequence of past observations, as well as on additional inputs (e.g., port of departure or arrival) about the vessel's high-level intention, which may be available from AIS.Comment: Accepted for publications in IEEE Transactions on Aerospace and Electronic Systems, 17 pages, 9 figure

Increasing maritime situation awareness via trajectory detection, enrichment and recognition of events

The research presented in this paper aims to show the deployment and use of advanced technologies towards processing surveillance data for the detection of events, contributing to maritime situation awareness via trajectories’ detection, synopses generation and semantic enrichment of trajectories. We first introduce the context of the maritime domain and then the main principles of the big data architecture developed so far within the European funded H2020 datAcron project. From the integration of large maritime trajectory datasets, to the generation of synopses and the detection of events, the main functions of the datAcron architecture are developed and discussed. The potential for detection and forecasting of complex events at sea is illustrated by preliminary experimental results

Paths forward for sustainable maritime transport : A techno-economic optimization framework for next generation vessels

Climate change is omnipresent in our society. It is known that climate change is occurring, and that additional warming is unavoidable. Therefore, the decarbonization of industrial sectors has gained increased importance in the last years. The maritime transport sector is one of the most targeted industries as it contributes to approximately 3% of global GHG emissions. Nevertheless, maritime transport accounts for up to 80% of the global trade volume, underlying its importance for the world economy. A technical feasible and reliable solution is, thus, essential for the shipping industry to reach the ambitious climate goals established by the Paris Agreement. In the past, the maritim sector has been highly reliant on fossil fuels, using heavy fuel oil as the major energy input. Heavy fuel oil has been the most dominant fuel in the industry due to its cost advantage and high energy density. Recent developments in the maritime industry promote the emergence of dual fuel engines (e.g. LNG and HFO). Even though increased efficiencies and low carbon fuels can reduce maritime pollution, they cannot achieve carbon neutrality. In the long-term, it will be necessary to implement zero emission fuels including green hydrogen, ammonia, methanol, and LNG. The implementation of new sustainable technologies and fuels in the maritime sector will however depend on their economic competitiveness compared to alternative solutions. Therefore, the following research question arises: When can sustainable maritime transport achieve cost parity compared to conventional technologies? The master thesis investigates the break-even point of sustainable shipping technologies in order to achieve climate targets. Thereby, the focus is set on the life cycle costs of different maritime technologies. A techno-economic framework is necessary to decide on the most suitable options for the industry in prospective years. The framework should be able to analyze current as well as prospective technologies, and guide during the technological decision-making process. Therefore, the definition of key performance indicators (KPI) is essential to set a standard for further assessments. The KPIs will be the main value to compare technologies from an economic perspective. In order to answer the research question a case study is developed. The case study is formed by an extensive literature review on current and next-generation sustainable energy systems for vessels. A priority lies on potential carbon neutral technologies and engines such as fuel cells and battery systems based on a predetermined shipping route and shipping class. In a first step, a simulation model for the developed case is established. The output of the simulation model will then be used in the techno-economic framework, connecting components of the system through thermodynamic and physical properties. In a last step, cost functions translate the systems behavior into economic behavior. Once the case study is analyzed, a statistical model is applied on the results in order to evaluate the system under varying boundary conditions. This sensitivity approach is further necessary to underline the impact of the aforementioned KPIs. By that, the robustness of the framework is tested and secured. Finally, the results of the analysis are explained and interpreted with regard to the research question. A conclusion is drawn regarding the potential economic benefits of sustainable maritime transport technologies within the light of potential market access.The results of the thesis are to be documented in a scientifically appropriate manner and discussed within the context of existing literature and regulatory targets for the industry

Big data analytics for time critical maritime and aerial mobility forecasting

The correlated exploitation of heterogeneous data sources offering very large archival and streaming data is important to increase the accuracy of computations when analysing and predicting future states of moving entities. Aiming to significantly advance the capacities of systems to improve safety and effectiveness of critical operations involving a large number of moving entities in large geographical areas, this paper describes progress achieved towards time critical big data analytics solutions to user-defined challenges in the air-traffic management and maritime domains. Besides, this paper presents further research challenges concerning data integration and management, predictive analytics for trajectory and events forecasting, and visual analytics

Maritime modular anomaly detection framework

Detecting maritime anomalies is an extremely important task for maritime agencies around the globe. With the number of vessels at seas growing exponentially, the need for novel automated methods to support them with their routines and upgrade existing technologies is undeniable. MARISA, the Maritime Integrated Surveillance Awareness project, aims at fostering collaboration between 22 governmental organisations and enhance the reaction and decision-making capabilities of the maritime authorities. This work describes our contributions to the development of MARISA’s common toolkit for the detection of maritime anomalies. These efforts, as part of a Masters’ dissertation, lead to the development of the Modular Anomaly Detection Framework, MAD-F, a full data pipe-line which applies efficient and reliable routines to raw vessel navigational data in order to output potential maritime vessel anomalies. The anomalies considered for this work were defined by the experts from various maritime institutions, through MARISA, and allowed us to implement solutions given the real needs in the industry. The MADF functionalities will be validated through actual real maritime exercises. In its current state, we believe that the MAD-F is able to support maritime agencies and be integrated into their legacy systems.Detetar anomalias marítimas é uma tarefa extremamente importante para agências marítimas á escala mundial. Com o número de embarcações em mar crescendo exponencial, a necessidade de desenvolver novas rotinas de suporte ás suas atividades e de atualizar as tecnologias existentes é inegável. MARISA, o projeto de Conscientização da Vigilância Integrada Marítima, visa fomentar a colaboração entre 22 organizações governamentais e melhorar as capacidades de reação e tomada de decisões das autoridades marítimas. Este trabalho descreve as nossas contribuições para o desenvolvimento do toolkit global MARISA, que tem como âmbito a deteção de anomalias marítimas. Estas contribuições servem como parte do desenvolvimento da Modular Anomaly Detection Framework (MAD-F), que serve como um data-pipeline completo que transforma dados de embarcações não estruturados em potenciais anomalias, através do uso de métodos eficientes para tal. As anomalias consideradas para este trabalho foram definidas através do projeto MARISA por especialistas marítimos, e permitiram-nos trabalhar em necessidades reais e atuais do sector. As funcionalidades desenvolvidas serão validadas através de exercícios marítimos reias. No estado atual do MAD-F acreditamos que este será capaz de apoiar agências marítimas, e de posteriormente ser integrado nos sistemas dos mesmos

An extension of the linear regression model for improved vessel trajectory prediction utilising a priori AIS Information

Thesis (MSc)--Stellenbosch University, 2022.ENGLISH ABSTRACT: As maritime activities increase globally, there is a greater dependency on technology in monitoring, control and surveillance of vessel activity. One of the most prominent systems for monitoring vessel activity is the Automatic Identification System (AIS). An increase in both vessels fitted with AIS transponders, and satellite- and terrestrial receivers has resulted in a significant increase in AIS messages received globally. This resultant rich spatial and temporal data source related to vessel activity provides analysts with the ability to perform enhanced vessel movement analytics, of which a pertinent example is the improvement of vessel location predictions. In this thesis, we propose a novel method for predicting future locations of vessels by making use of historic AIS data. The proposed method extends a Linear Regression Model (LRM), utilising historic AIS movement data in the form of a priori generated spatial maps of the course over ground (LRMAC). The LRMAC has low complexity and is programmatically easy to implement, and attains accurate prediction results. We first compare the LRM with a Discrete Kalman Filter (DKF) on linear trajectories. We then extend the LRM to form the LRMAC. The LRMAC is compared to another method in literature called the Single Point Neighbour Search (SPNS). For the use case of predicting Cargo and Tanker vessel trajectories, with a prediction horizon of up to six hours, the LRMAC has an improved execution time and performance compared to the SPNS.AFRIKAANSE OPSOMMING: As gevolg van die toename in maritieme aktiwiteite wˆereldwyd, het die afhanklikheid van tegnologie in die monitering, beheer en toesig van vaartuigaktiwiteite ook toegeneem. Een van die mees prominente stelsels vir die monitering van vaartuigaktiwiteit is die Outomatiese Identifikasiestelsel (OIS). ’n Toename in vaartuie wat toegerus is met OIS-transponders, en die toename in satelliet- en terrestri¨ele ontvangers, het gelei tot ’n aansienlike groei in OIS-boodskappe wat wˆereldwyd ontvang is. Dit het weer gelei tot die toename in dataryke ruimte-temporele bronne, wat verband hou met vaartuigaktiwiteite. Dit gee ontleders die vermo¨e om gevorderde vaartuig-bewegingsanalise uit te voer, waarvan ’n toepaslike voorbeeld, die verbetering van vaartuig-liggingvoorspelling is. In hierdie tesis stel ons ’n nuwe strategie voor om toekomstige liggings van vaartuie te voorspel, wat gebruik maak van historiese OIS-data. Die voorgestelde metode brei ’n Lineˆere Regressie Model (LRM) uit, deur gebruik te maak van historiese bewegingsdata en ruimte kaarte van a priori koers oor grond inligting (LRMAK). Die LRMAK het ’n lae kompleksiteit en is programmaties eenvoudig om te implementeer, met relatiewe akkurate voorspelling resultate. Ons vergelyk eers die LRM met ’n Diskrete Kalman Filter (DKF) op lineˆere trajekte. Dan brei ons die LRM uit om die LRMAK te vorm. Die LRMAK word vergelyk met ’n ander metode in literatuur wat die Enkel-punt Buur soektog (EPBS) genoem word. In die geval van trajek-voorspelling vir vrag- en tenkwa-vaartuie, het die LRMAK ’n verbeterde uitvoeringstyd en is vergelykbaar met ’n ander algoritme in literatuur, die EPBS, tot en met ’n voorspellingstydperk van ses-ure.Master