7 research outputs found
Exploring movement – similarity analysis of moving objects
Extracting knowledge about the movement of different types of mobile agents (e.g. human, animals, vehicles) and dynamic
phenomena (e.g. hurricanes) requires new exploratory data
analysis methods for massive movement datasets. Different types of moving objects share similarities but also express differences in terms of their dynamic behavior and the nature of their movement. Extracting such similarities can significantly contribute to the prediction, modeling and simulation dynamic phenomena. Therefore, with the development of a quantitative methodology this research intends to investigate and explore similarities in the
dynamics of moving objects by using methods of GIScience in
knowledge discovery. This paper presents a summary of the
ongoing Ph.D. research project
SMoT+: Extending the SMoT Algorithm for Discovering Stops in Nested Sites
Several methods have been proposed to analyse trajectory data. However, a few of these methods consider trajectory relations with relevant features of the geographic space. One of the best-known methods that take into account the geographical regions crossed by a trajectory is the SMoT algorithm. Nevertheless, SMoT considers only disjoint geographic regions that a trajectory may traverse, while many regions of interest are contained in other regions. In this article, we extend the SMoT algorithm for discovering stops in nested regions. The proposed algorithm, called SMoT+, takes advantage of information about the hierarchy of nested regions to efficiently discover the stops in regions at different levels of this hierarchy. Experiments with real data show that SMoT+ detects stops in nested regions, which are not detected by the original SMoT algorithm, with minor growth of processing time
Análise de comportamento de motoristas através de trajetórias de objetos móveis
Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro TecnolĂłgico, Programa de PĂłs-graduação em CiĂŞncia da Computação, FlorianĂłpolis, 2014Todos os dias o nĂşmero de veĂculos circulando pelas estradas Ă© maior e, se estiverem equipados com dispositivos mĂłveis tais como GPS, Ă© gerado um novo tipo de dado chamado de trajetĂłria. Vários estudos buscam descobrir padrões em trajetĂłrias, mas poucos tĂŞm focado na análise do comportamento de motoristas. AtravĂ©s das trajetĂłrias geradas pelos veĂculos Ă© possĂvel inferir bons e maus condutores e encontrar locais problemáticos ao longo das vias caso existam padrões de comportamento bem definidos. Esta dissertação propõe um mĂ©todo para análise de comportamento de motoristas atravĂ©s das suas trajetĂłrias, com base em caracterĂsticas como acelerações, desacelerações e mudanças bruscas de direção, excesso de velocidade, comportamento de costura, limites de velocidade das vias, bem como locais que possam ser causadores de um movimento anĂ´malo, tal qual um semáforo. Trabalhos na literatura nĂŁo classificam motoristas utilizando dados de GPS e nĂŁo detectam ou analisam movimentos anĂ´malos com justificativas para as anomalias. O mĂ©todo Ă© avaliado atravĂ©s de experimentos com dados reais que mostram ser possĂvel classificar motoristas com base em suas trajetĂłrias.Abstract: Every day the number of vehicles driving on the roads is increasing, and if equipped with mobile devices such as GPS, a new data type called trajectory is generated. Several studies seek to discover patterns in trajectories, but only a few have focused on analyzing the behavior of drivers. Through the trajectories generated by the vehicles it is possible to infer good and bad drivers, as well as problematic places along the roads if there are well-defined behavior patterns. This thesis proposes a method for analyzing the behavior of drivers over their trajectories, based on characteristics such as acceleration, deceleration and abrupt changes of direction, speed, behavior of lane cutting and places that may be causing an anomalous movement, like a traffic light. Existing works in the literature do not classify drivers using GPS data considering repetitive anomalous movement or justifications of anomalies. The proposed method is evaluated through experiments with real data showing the possibility to classify drivers based on their trajectories
Tourenvorschläge für Mountainbiker aus Bewegungsdaten
Der Mountainbike-Sport erfreut sich steigender Beliebtheit und ist in Bergregionen längst ein wichtiger Wirtschaftsfaktor. Mountainbike-spezifische Zeitschriften, Internetseiten und Smartphone-Apps bieten unzählige Informationen über Mountainbike-Touren an. Obwohl sich bei Onlineplattformen Suchfilter einsetzen lassen, ist es oft schwierig und zeitaufwändig eine Mountainbike-Tour nach seinem eigenen Geschmack zu finden.
In den verschiedensten Bereichen nutzen Menschen GPS-Sensoren, um ihre Bewegungsdaten mittels GPS aufzuzeichnen. Aus den aufgezeichneten raumzeitlichen Daten können mittels moderner Methoden der Bewegungsanalyse nützliche Informationen gewonnen werden. Im Fall des Mountainbikings können relevante Toureninformationen aus den GPS-Daten abgeleitet und daraus ein personalisiertes Tourenprofil erstellt werden. Dieses kann wiederum verwendet werden, um personalisierte Tourenvorschläge zu unterbreiten.
In dieser Arbeit wurde ein Algorithmus entwickelt, welcher aus GPS-Tracks relevante Tourenparameter ableitet und daraus personalisierte Tourenprofile erstellt. Mittels Tourenprofil kann schliesslich aus einer beliebigen Auswahl an Mountainbike-Touren ein personalisierter Tourenvorschlag unterbreitet werden.In the last decade mountain biking has become a very popular sport. For mountain regions it has turned into an important economic factor. Targeted mountain biking magazines, websites and smartphone apps provide a wide range of information about mountain bike trails and tours. Although there is the possibility to apply search filters, it remains difficult to find a mountain bike tour according to one’s own preferences.
People use GPS-devices in many different areas to trace their movements while jogging or cycling. This spatio-temporal data can be processed by modern methods of movement analysis. Through the movement analysis, mountain biking specific information can be gained and used to draw up a mountain biker’s specific tour profile. A personalised mountain bike tour profile can then be used to develop personalised tour recommendations.
For this thesis a special algorithm was developed which uses GPS tracks to deduce tour parameters, draw up personalised mountain bike tour profiles and make individual tour recommendations
Técnicas de agrupamento de trajetórias com aplicação à recomendação de percursos
Mestrado em Engenharia de Computadores e TelemáticaO uso generalizado de dispositivos capazes de obter e transmitir dados sobre a localização de objetos ao longo do tempo tem permitido recolher grandes volumes de dados espácio-temporais. Por isso, tem-se assistido a uma procura crescente de técnicas e ferramentas para a análise de grandes volumes de dados espácio-temporais com o intuito de disponibilizar uma gama variada de serviços baseados na localização.
Esta dissertação centra-se no desenvolvimento de um sistema para recomendaSr trajetos com base em dados histĂłricos sobre a localização de objetos mĂłveis ao longo do tempo. O principal problema estudado neste trabalho consiste no agrupamento de trajetĂłrias e na extração de informação a partir dos grupos de trajetĂłrias. Este estudo, nĂŁo se restringe a dados provenientes apenas de veĂculos, podendo ser aplicado a outros tipos de trajetĂłrias, por exemplo, percursos realizados por pessoas a pĂ© ou de bicicleta.
O agrupamento baseia-se numa medida de similaridade. A extração de informação consiste em criar uma trajetĂłria representativa para cada grupo de trajetĂłrias. As trajetĂłrias representativas podem ser visualizadas usando uma aplicação web, sendo tambĂ©m possĂvel configurar cada mĂłdulo do sistema com parâmetros desejáveis, na sua maioria distâncias limiares. Por fim, sĂŁo apresentados casos de teste para avaliar o desempenho global do sistema desenvolvido.The widespread use of devices to capture and transmit data about the location of objects over time allows collecting large volumes of spatio-temporal data. Consequently, there has been in recent years a growing demand for tools and techniques to analyze large volumes of spatio-temporal data aiming at providing a wide range of location-based services.
This dissertation focuses on the development of a system for recommendation of trajectories based on historical data about the location of moving objects over time. The main issues covered in this work are trajectory clustering and extracting information from trajectory clusters. This study is not restricted to data from vehicles and can also be applied to other kinds of trajectories, for example, the movement of runners or bikes.
The clustering is based on a similarity measure. The information extraction consists in creating a representative trajectory for the trajectories clusters. Finally, representative trajectories are displayed using a web application and it is also possible to configure each system module with desired parameters, mostly distance thresholds. Finally, case studies are presented to evaluate the developed system