Enormous amounts of GPS trajectories, which record users' spatial and temporal information, are c... more Enormous amounts of GPS trajectories, which record users' spatial and temporal information, are collected by geo-positioning mobile phones in recent years. The massive volumes of trajectory data bring about heavy burdens for both network transmission and data storage. To overcome these difficulties, a number of compression algorithms have been proposed by reducing the number of points in the trajectory data. But these algorithms lack a rigorous investigation on how to encode the reduced trajectories. In this paper, we propose an algorithm that optimizes both the trajectory simplification and the coding procedure using the quantized data. The underlying algorithm is also compared with the existing methods across 640 trajectories from Microsoft Geolife dataset using synchronous Euclidean distance (SED) as the error metrics. Experimental results show that the proposed method saves 60% of compression cost against the current state of the art compression algorithms.
Enormous amounts of GPS trajectories, which record users' spatial and temporal information, are c... more Enormous amounts of GPS trajectories, which record users' spatial and temporal information, are collected by geo-positioning mobile phones in recent years. The massive volumes of trajectory data bring about heavy burdens for both network transmission and data storage. To overcome these difficulties, a number of compression algorithms have been proposed by reducing the number of points in the trajectory data. But these algorithms lack a rigorous investigation on how to encode the reduced trajectories. In this paper, we propose an algorithm that optimizes both the trajectory simplification and the coding procedure using the quantized data. The underlying algorithm is also compared with the existing methods across 640 trajectories from Microsoft Geolife dataset using synchronous Euclidean distance (SED) as the error metrics. Experimental results show that the proposed method saves 60% of compression cost against the current state of the art compression algorithms.
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