Advanced techniques in trajectory data analysis for anomaly detection and map construction
With a large amount of trajectory data generated every day, there is a high demand for developing advanced techniques to discover the underlying information instead of dull and heavy manual work. This thesis focuses on the anomaly detection and map construction from GPS data. Anomaly detection aims...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/673055 |
| Acceso en línea: | http://hdl.handle.net/10803/673055 |
| Access Level: | acceso abierto |
| Palabra clave: | Dades GPS Datos GPS GPS data Mapes Mapas Maps Cartografia Cartography Detecció d'anomalies Detección de anomalías Anomaly detection Anàlisi de dades de trajectòries Análisis de datos de trayectoria Trajectories data analysis 68 91 |
| Sumario: | With a large amount of trajectory data generated every day, there is a high demand for developing advanced techniques to discover the underlying information instead of dull and heavy manual work. This thesis focuses on the anomaly detection and map construction from GPS data. Anomaly detection aims to identify trajectories that do not follow common behaviors, and map construction deals with a set of trajectory data to generate a route graph that represents the main movement paths hidden in data. For online anomaly detection, we study the well-known Sequential Hausdorff Nearest-Neighbor Conformal Anomaly Detector (SHNN-CAD) approach, and propose an enhanced version called SHNN-CAD•. For map construction, we present a new, fast and robust three-step framework. Considering the storage limitation and computational cost dealing with large-scale data, we propose a split-andmerge strategy. Besides, we utilize the edge weight to visualize the map and remove the wrong edges. |
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