89,350 research outputs found

    SMSM: a similarity measure for trajectory stops and moves

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    Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2019.Medidas de similaridade são a base para a maioria dos métodos de mineração de dados e extração de conhecimento. Na área de trajetórias de objetos móveis, por muitos anos a pesquisa em similaridade de trajetórias focou nas trajetórias brutas, considerando somente a informação de espaço e tempo. Com o enriquecimento das trajetórias com informações semânticas, como o nome e a categoria dos locais visitados, meio de transporte utilizado durante o movimento, o nome das ruas percorridas, etc, emergiu a necessidade por medidas de similaridade que suportem espaço, tempo e semântica. Apesar de algumas medidas de similaridade para trajetórias lidarem com todas estas dimensões, elas consideram somente os locais onde o objeto móvel faz paradas, denominados stops, ignorando o movimento que ocorre entre as paradas, denominado move. Acredita-se que, para algumas aplicações, o movimento entre os stops é tão importante quanto o stop em si, e ele deve ser levado em consideração na análise da similaridade, como em sistemas de transporte público, turismo, planejamento urbano, entre outros. Nesta dissertação é proposta a medida Similarity Measure for trajectory Stops and Moves (SMSM), um nova medida de similaridade para trajetórias semânticas que considera tanto os stops quanto os moves. O SMSM é avaliado em três conjuntos de dados: (i) um conjunto de dados de trajetórias sintéticas criadas com o gerador de trajetórias semânticas Hermoupolis, (ii) um conjunto de trajetórias reais de táxis do projeto CRAWDAD, e (iii) o conjunto de dados de trajetórias reais chamado Geolife, com trajetórias de pessoas na cidade de Pequim. Os resultados mostram que o SMSM supera as medidas de similaridade do estado da arte desenvolvidas tanto para trajetórias brutas quanto semânticas.Abstract : For many years trajectory similarity research has focused on raw trajectories, considering only space and time information. With the trajectory semantic enrichment, using information as the name and type of the visited places, the transportation mean, the name of the streets, etc, emerged the need for similarity measures that support space, time, and semantics. Although some trajectory similarity measures deal with all these dimensions, they consider only the places where the moving object stays for a certain time, called stop, ignoring the movement between stops. We claim that, for some applications, as traffic management systems, urban planning, public transportation, etc, the movement between stops is as important as the stops, and it must be considered in the similarity analysis. In this thesis we propose the similarity measure called Similarity Measure for trajectory Stops and Moves(SMSM), a novel similarity measure for semantic trajectories that considers both stops and moves. We evaluate SMSM with three trajectory datasets: (i) a synthetic trajectory dataset generated with the Hermoupolis semantic trajectory generator, (ii) a real trajectory dataset of taxis from the CRAWDAD project, and (iii) the Geolife trajectory dataset, with raw trajectories of persons around Beijing. The results show that SMSM overcomes state-of-the-art measures developed for both raw and semantic trajectories

    Human Mobility Prediction Through Twitter.

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    Abstract Social media, in recent years, have become an invaluable source of information concerning human dynamics within urban context, allowing to enhance the comprehension of people behaviour, including human mobility regularities. The paper presents an approach to predict human mobility by exploiting Twitter data. The prediction approach is based on a novel trajectory pattern similarity measure that allows to identify the more suitable historic patterns to exploit for the prediction of the user next location. The pattern with the highest similarity to the user current trajectory will be used to predict the user next position. The experimental results obtained by using a real-world dataset show that the proposed method is effective in predicting the users next places achieving a remarkable precision

    LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protection

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    The prevalence of location-based services contributes to the explosive growth of individual-level trajectory data and raises public concerns about privacy issues. In this research, we propose a novel LSTM-TrajGAN approach, which is an end-to-end deep learning model to generate privacy-preserving synthetic trajectory data for data sharing and publication. We design a loss metric function TrajLoss to measure the trajectory similarity losses for model training and optimization. The model is evaluated on the trajectory-user-linking task on a real-world semantic trajectory dataset. Compared with other common geomasking methods, our model can better prevent users from being re-identified, and it also preserves essential spatial, temporal, and thematic characteristics of the real trajectory data. The model better balances the effectiveness of trajectory privacy protection and the utility for spatial and temporal analyses, which offers new insights into the GeoAI-powered privacy protection

    Exploring dance movement data using sequence alignment methods

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    Despite the abundance of research on knowledge discovery from moving object databases, only a limited number of studies have examined the interaction between moving point objects in space over time. This paper describes a novel approach for measuring similarity in the interaction between moving objects. The proposed approach consists of three steps. First, we transform movement data into sequences of successive qualitative relations based on the Qualitative Trajectory Calculus (QTC). Second, sequence alignment methods are applied to measure the similarity between movement sequences. Finally, movement sequences are grouped based on similarity by means of an agglomerative hierarchical clustering method. The applicability of this approach is tested using movement data from samba and tango dancers

    Comparing and Combining Time Series Trajectories Using Dynamic Time Warping

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    This research proposes the application of dynamic time warping (DTW) algorithm to analyse multivariate data from virtual reality training simulators, to assess the skill level of trainees. We present results of DTW algorithm applied to trajectory data from a virtual reality haptic training simulator for epidural needle insertion. The proposed application of DTW algorithm serves two purposes, to enable (i) two trajectories to be compared as a similarity measure and also enables (ii) two or more trajectories to be combined together to produce a typical or representative average trajectory using a novel hierarchical DTW process. Our experiments included 100 expert and 100 novice simulator recordings. The data consists of multivariate time series data-streams including multi-dimensional trajectories combined with force and pressure measurements. Our results show that our proposed application of DTW provides a useful time-independent method for (i) comparing two trajectories by providing a similarity measure and (ii) combining two or more trajectories into one, showing higher performance compared to conventional methods such as linear mean. These results demonstrate that DTW can be useful within virtual reality training simulators to provide a component in an automated scoring and assessment feedback system
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