Semantic trajectory compression: Representing urban movement in a nutshell
Semantic trajectory compression: Representing urban movement in a nutshell
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DOI：
10.5311/josis.2012.4.62
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发表时间：
2012-06
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影响因子：
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通讯作者：
Kai-Florian Richter;Falko Schmid;P. Laube
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文献类型：
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作者：
Kai-Florian Richter;Falko Schmid;P. Laube
有越来越多的快速增长的存储库捕捉时空中的人的运动。运动轨迹压缩成为应对这种不断增长的数据量的明显必要条件。本文介绍了语义轨迹压缩的概念。STC允许以可接受的信息损失基本上压缩轨迹数据。它利用了人类城市流动通常发生在交通网络中，交通网络定义了移动的地理环境。在STC中，由定位在运输网络中的参考点组成的轨迹的语义表示代替原始的、高度冗余的位置信息（例如，GPS接收器）。用真实的和合成轨迹进行的实验评估证明了STC在将轨迹减少到基本信息方面的能力，并说明了如何从压缩数据恢复轨迹。本文讨论了STC轨迹的可能应用领域。
There is an increasing number of rapidly growing repositories capturing the movement of people in space-time. Movement trajectory compression becomes an ob- vious necessity for coping with such growing data volumes. This paper introduces the concept of semantic trajectory compression (STC). STC allows for substantially compressing trajectory data with acceptable information loss. It exploits that human urban mobility typically occurs in transportation networks that define a geographic context for the move- ment. In STC, a semantic representation of the trajectory that consists of reference points localized in a transportation network replaces raw, highly redundant position information (e.g., from GPS receivers). An experimental evaluation with real and synthetic trajectories demonstrates the power of STC in reducing trajectories to essential information and illus- trates how trajectories can be restored from compressed data. The paper discusses possible application areas of STC trajectories.