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On Velocity-Preserving Trajectory Simplification

机译：关于保持速度的轨迹简化

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Trajectory data plays crucial role in many real-world applications with moving objects. The size of trajectory dataset is always very huge because of high sampling rate. Therefore, it is desired to simplify each trajectory before it is stored and processed. As the result, many trajectory simplification notions have been proposed. However, existing studies on trajectory simplification more or less rely on geometric-preserving manner (e.g., minimizing position-based or direction-based errors). These manners directly avoid effectiveness of velocity in many real-world applications. Actually, the velocity of a moving object is very important in many real-world applications, such as map-matching, mobility prediction, moving pattern mining, etc. In this paper, we propose a novel trajectory simplification, velocity-preserving trajectory simplification (VPTS), which minimize both geometric error and velocity error. We present an efficient algorithm for optimal velocity-preserving trajectory simplification. Through a series of experimental evaluation with real trajectory data, we examine the benefit of our proposed velocity-preserving trajectory simplification.
机译：轨迹数据在许多带有移动物体的现实应用中起着至关重要的作用。由于高采样率，轨迹数据集的大小始终非常大。因此，期望在存储和处理每个轨迹之前简化每个轨迹。结果，提出了许多轨迹简化概念。然而，关于轨迹简化的现有研究或多或少地依赖于几何保持方式（例如，最小化基于位置或基于方向的误差）。这些方式直接避免了在许多实际应用中速度的有效性。实际上，运动物体的速度在许多实际应用中非常重要，例如地图匹配，移动性预测，运动模式挖掘等。在本文中，我们提出了一种新颖的轨迹简化，速度保持轨迹简化（ VPTS），可将几何误差和速度误差减至最小。我们提出了一种最佳的速度保持轨迹简化的有效算法。通过对真实轨迹数据进行一系列实验评估，我们检验了所提出的速度保持轨迹简化的好处。

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