Web-based Geographical Visualization of Container Itineraries

Around 90% of the world cargo is transported in maritime containers, but only around 2% are physically inspected. This opens the possibility for illicit activities. A viable solution is to control containerized cargo through information-based risk analysis. Container route-based analysis has been considered a key factor in identifying potentially suspicious consignments. Essential part of itinerary analysis is the geographical visualization of the itinerary. In the present paper, we present initial work of a web-based system’s realization for interactive geographical visualization of container itinerary.JRC.G.4-Maritime affair

A Comparison Between Alignment and Integral Based Kernels for Vessel Trajectories

In this paper we present a comparison between two important types of similarity measures for moving object trajectories for machine learning from vessel movement data. These similarities are compared in the tasks of clustering, classication and outlier detection. The rst similarity type are alignment measures, such as dynamic time warping and edit distance. The second type are based on the integral over time between two trajectories. Following earlier work we dene these measures in the context of kernel methods, which provide state-of-the-art, robust algorithms for the tasks studied. Furthermore, we include the in uence of applying piecewise linear segmentation as pre-processing to the vessel trajectories when computing alignment measures, since this has been shown to give a positive eect in computation time and performance. In our experiments the alignment based measures show the best performance. Regular versions of edit distance give the best performance in clustering and classication, whereas the softmax variant of dynamic time warping works best in outlier detection. Moreover, piecewise linear segmentation has a positive eect on alignments, which seems to be due to the fact salient points in a trajectory, especially important in clustering and outlier detection, are highlighted by the segmentation and have a large in uence in the alignments

A ship movement classification based on Automatic Identification System (AIS) data using Convolutional Neural Network

With a wide use of AIS data in maritime transportation, there is an increasing demand to develop algorithms to efficiently classify a ship’s AIS data into different movements (static, normal navigation and manoeuvring). To achieve this, several studies have been proposed to use labelled features but with the drawback of not being able to effectively extract the details of ship movement information. In addition, a ship movement is in a free space, which is different to a road vehicle’s movement in road grids, making it inconvenient to directly migrate the methods for GPS data classification into AIS data. To deal with these problems, a Convolutional Neural Network-Ship Movement Modes Classification (CNN-SMMC) algorithm is proposed in this paper. The underlying concept of this method is to train a neural network to learn from the labelled AIS data, and the unlabelled AIS data can be effectively classified by using this trained network. More specifically, a Ship Movement Image Generation and Labelling (SMIGL) algorithm is first designed to convert a ship’s AIS trajectories into different movement images to make a full use of the CNN’s classification ability. Then, a CNN-SMMC architecture is built with a series of functional layers (convolutional layer, max-pooling layer, dense layer etc.) for ship movement classification with seven experiments been designed to find the optimal parameters for the CNN-SMMC. Considering the imbalanced features of AIS data, three metrics (average accuracy, score and Area Under Curve (AUC)) are selected to evaluate the performance of the CNN-SMMC. Finally, several benchmark classification algorithms (K-Nearest Neighbours (KNN), Support Vector Machine (SVM) and Decision Tree (DT)) are selected to compare with CNN-SMMC. The results demonstrate that the proposed CNN-SMMC has a better performance in the classification of AIS data

From movement tracks through events to places : extracting and characterizing significant places from mobility data

Best VAST 2011 paperInternational audienceWe propose a visual analytics procedure for analyzing movement data, i.e., recorded tracks of moving objects. It is oriented to a class of problems where it is required to determine significant places on the basis of certain types of events occurring repeatedly in movement data. The procedure consists of four major steps: (1) event extraction from trajectories; (2) event clustering and extraction of relevant places; (3) spatio-temporal aggregation of events or trajectories; (4) analysis of the aggregated data. All steps are scalable with respect to the amount of the data under analysis. We demonstrate the use of the procedure by example of two real-world problems requiring analysis at different spatial scales

Review and classification of trajectory summarisation algorithms: From compression to segmentation

With the continuous development and cost reduction of positioning and tracking technologies, a large amount of trajectories are being exploited in multiple domains for knowledge extraction. A trajectory is formed by a large number of measurements, where many of them are unnecessary to describe the actual trajectory of the vehicle, or even harmful due to sensor noise. This not only consumes large amounts of memory, but also makes the extracting knowledge process more difficult. Trajectory summarisation techniques can solve this problem, generating a smaller and more manageable representation and even semantic segments. In this comprehensive review, we explain and classify techniques for the summarisation of trajectories according to their search strategy and point evaluation criteria, describing connections with the line simplification problem. We also explain several special concepts in trajectory summarisation problem. Finally, we outline the recent trends and best practices to continue the research in next summarisation algorithms.The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This work was funded by public research projects of Spanish Ministry of Economy and Competitivity (MINECO), reference TEC2017-88048-C2-2-

Towards optimization methods for movelets extraction in multiple aspect trajectory classification

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2023.In the last few years there has been a significant increase in the collection of mobility data. By mobility data we refer to the collection of positioning data, called trajectories, of tracked moving objects. These objects could be humans, animals, vehicles or other devices like Internet of Things (IoT). The analysis of such data has been proved to be useful in several application domains from a urban scenario for traffic prediction or transportation means optimization, to maritime domain analysing vessels paths or environmental domain with the study of hurricanes evolution or animal behavior. One of the most typical and used analysis task on mobility data is classification, where trajectory data is automatically assigned a label or class. The explosion of social media data, sensors, IoT, and Internet-enabled sources allowed the semantic enrichment of such mobility data, which evolved from raw spatio-temporal data to high dimensional data. Mobility analysis, and specifically classification task, on such high dimensional data becomes therefore more challenging. In fact, existing trajectory classification methods have mainly considered space, time, and numerical data, ignoring the large number of semantic dimensions. Only recently research community proposed classification methods based on the concept of movelets that are the parts of a trajectory that better discriminate a class and that can therefore improve classification accuracy. State of the art methods in movelets extraction are computationally inefficient, which makes them unfeasible to be used for real large high dimensional datasets. The objective of this thesis is therefore to develop new algorithms for discovering movelets that are faster than state of the art while maintaining or improving classification accuracy. Our main contribution is a new high performance method for extracting movelets and classifying trajectories, called HiPerMovelets (High-performance Movelets). Experimental results show that HiPerMovelets is 10 times faster than the best state of the art method, reduces the high dimensionality problem, is more scalable, and presents a high classification accuracy in all evaluated datasets. A secondary contribution are the algorithms RandomMovelets and UltraMovelets. RandomMovelets reduces the search space by randomly extracting subtrajectories and evaluating their relevance for classification without exploring the entire dataset. UltraMovelets reduces the combinatorial explosion when exploring subtrajectories. Preliminary results suggest that these methods can reduce the search space, use less computational resources, and are at least 6 times faster than baselines.Nos últimos anos, houve um aumento significativo na coleta de dados de mobilidade. Dados de mobilidade referem-se ao conjunto de dados de posicionamento geográfico, chamados de trajetórias de objetos móveis. Esses objetos podem ser pessoas, animais, veículos ou outros dispositivos como a Internet das Coisas (IoT). A análise deste tipo de dados se revela útil em vários domínios de aplicação, desde um cenário urbano para previsão de tráfego ou otimização de meios de transporte, no domínio marítimo analisando trajetos de embarcações, no domínio ambiental com o estudo da evolução de furacões ou comportamento animal. Uma das tarefas de análise mais comuns e usadas em dados de mobilidade é a classificação, onde os dados de trajetória recebem automaticamente um rótulo ou classe. A explosão de dados de mídia social, sensores, IoT e outras fontes da Internet permitiram o enriquecimento semântico desses dados de mobilidade, que evoluíram de dados espaço-temporais brutos para dados de alta dimensionalidade. A análise de mobilidade, e especificamente a tarefa de classificação, em tais dados de alta dimensionalidade tem se tornado mais desafiadora. De fato, os métodos de classificação de trajetória existentes consideram principalmente espaço, tempo e dados numéricos, ignorando o grande número de dimensões semânticas. Apenas recentemente a comunidade de pesquisa propôs métodos de classificação baseados no conceito de movelets que são as partes de uma trajetória que melhor discriminam uma classe e que podem, portanto, melhorar a precisão da classificação. Métodos de última geração na extração de movelets são computacionalmente ineficientes, o que os torna inviáveis para serem usados em grandes conjuntos de dados de alta dimensão. O objetivo desta tese é, portanto, desenvolver novos algoritmos para descobrir movelets que sejam mais rápidos do que o estado da arte, mantendo ou melhorando a precisão da classificação. Nossa principal contribuição é um novo método de alto desempenho para extração de movelets e classificação de trajetórias, denominado HiPerMovelets (Movelets de alto desempenho). Os resultados experimentais mostram que o HiPerMovelets é 10 vezes mais rápido que o melhor método do estado da arte, reduz o problema de alta dimensionalidade, é mais escalável e apresenta uma alta precisão de classificação em todos os conjuntos de dados avaliados. Uma contribuição secundária são os algoritmos RandomMovelets e UltraMovelets. RandomMovelets reduz o espaço de busca extraindo subtrajetórias aleatoriamente e avaliando sua relevância para classificação sem explorar todo o conjunto de dados. UltraMovelets reduz a explosão combinatória ao explorar subtrajetórias. Os resultados preliminares sugerem que esses métodos podem reduzir o espaço de busca, usar menos recursos computacionais e são pelo menos 6 vezes mais rápidos que a linha de base

Scalable analysis of movement data for extracting and exploring significant places

Place-oriented analysis of movement data, i.e., recorded tracks of moving objects, includes finding places of interest in which certain types of movement events occur repeatedly and investigating the temporal distribution of event occurrences in these places and, possibly, other characteristics of the places and links between them. For this class of problems, we propose a visual analytics procedure consisting of four major steps: 1) event extraction from trajectories; 2) extraction of relevant places based on event clustering; 3) spatiotemporal aggregation of events or trajectories; 4) analysis of the aggregated data. All steps can be fulfilled in a scalable way with respect to the amount of the data under analysis; therefore, the procedure is not limited by the size of the computer's RAM and can be applied to very large data sets. We demonstrate the use of the procedure by example of two real-world problems requiring analysis at different spatial scales

Vehicle Category Classification Based on GPS Trajectory Data

Understanding the category of a vehicle is an essential study for transportation safety and operation. With the explosive number of GPS devices, there are massive vehicle GPS trajectory data sets whose sizes are beyond the traditional trajectory analysis method's capability. This study utilizes Apache Spark™ to build up a framework whose output data can be compatible with machine learning algorithms for vehicle category classification. Five types of features were extracted from the GPS trajectory data, namely driving habits statistics, trajectory sample quality statistics, geographical information statistics, origin and destination cluster statistics, and temporal statistics. The spatial clustering algorithm and spatial join are incorporated in the workflow, significantly broadening the number of features for the training data set. The results show that the five types of statistics extracted from the trajectory are adequate for distinguishing different vehicle categories by machine learning algorithms. The same accuracy rank sequence for the vehicle classes was observed across different types of features and algorithms, and the decision tree ensemble algorithms have better performance over the logistic regression and support vector machine algorithms

Renewing the house

This study is a contribution to the household archaeology of the Caribbean. The aim of the research was to come to a material definition of the precolonial house, rather than rely on the few, short, Spanish colonial descriptions. Archaeological research from the indigenous Taíno site of El Cabo in the Dominican Republic is presented and seven centuries of community history from development and growth, to eventual demise after European contact is narrated through the dominant structure, the house. The interpretation of over 2000 domestic features, associated artefact assemblages and the spatial organization of the settlement between ca. AD 800 and 1504 is described in detail. No archaeological house plans have previously been published for precolonial Hispaniola. The data from El Cabo tips the scales the other way, contributing to a history of indigenous life through the study of the native house and its diachronic materialization - the House Trajectory