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Semantic-Aware Trajectory Compression with Urban Road Network

  • Na TaEmail author
  • Guoliang Li
  • Bole Chen
  • Jianhua Feng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9658)

Abstract

Vehicles are generating more and more trajectories, which are expensive to store and mange. Thus it calls for vehicular trajectory compression techniques. We propose a semantic-aware compression framework that includes two steps: Map Matching(MM) and Semantic Trajectory Compression(STC). On the one hand, due to measurement errors, trajectories cannot be precisely mapped to real roads. In the MM step, we utilize multidimensional information(including distance and direction) to find the most matching roads and generate aligned trajectories. On the other hand, some unnecessary points in trajectories can be reduced based on the roads. In the STC step, we extract a set of crucial points from the aligned trajectory, which capture the major driving semantics of the trajectory. Meanwhile, our decompression method is fairly lightweight and can efficiently support various applications. Empirical study shows that MM achieves high matching quality, STC achieves more than 8 times compression ratio, and decompression is efficient on real datasets.

Keywords

Road Network Compression Ratio Road Segment High Compression Ratio Road Edge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This research is supported in part by the 973 Program of China (2015CB358700), the NSF of China (61272090, 61373024), Tencent, Huawei, Shenzhou, FDCT/116/2013/A3, MYRG105(Y1-L3)-FST13-GZ, and the Chinese Special Project of Science and Technology (2013zx01039-002-002).

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceTsinghua UniversityBeijingChina

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