The VLDB Journal

, Volume 28, Issue 6, pp 897–921 | Cite as

One-pass trajectory simplification using the synchronous Euclidean distance

  • Xuelian Lin
  • Jiahao Jiang
  • Shuai MaEmail author
  • Yimeng Zuo
  • Chunming HuEmail author
Regular Paper


Various mobile devices have been used to collect, store and transmit tremendous trajectory data, and it is known that raw trajectory data seriously wastes the storage, network bandwidth and computing resource. To attack this issue, one-pass line simplification (\(\textsf {LS} \)) algorithms have been developed, by compressing data points in a trajectory to a set of continuous line segments. However, these algorithms adopt the perpendicular Euclidean distance, and none of them uses the synchronous Euclidean distance (\(\textsf {SED} \)), and cannot support spatiotemporal queries. To do this, we develop two one-pass error bounded trajectory simplification algorithms (\(\textsf {CISED} \)-\(\textsf {S} \) and \(\textsf {CISED} \)-\(\textsf {W} \)) using \(\textsf {SED} \), based on a novel spatiotemporal cone intersection technique. Using four real-life trajectory datasets, we experimentally show that our approaches are both efficient and effective. In terms of running time, algorithms \(\textsf {CISED} \)-\(\textsf {S} \) and \(\textsf {CISED} \)-\(\textsf {W} \) are on average 3 times faster than \(\textsf {SQUISH} \)-\(\textsf {E} \) (the fastest existing \(\textsf {LS} \) algorithm using \(\textsf {SED} \)). In terms of compression ratios, \(\textsf {CISED} \)-\(\textsf {S} \) is close to and \(\textsf {CISED} \)-\(\textsf {W} \) is on average \(19.6\%\) better than \(\textsf {DPSED} \) (the existing sub-optimal \(\textsf {LS} \) algorithm using \(\textsf {SED} \) and having the best compression ratios), and they are \(21.1\%\) and \(42.4\%\) better than \(\textsf {SQUISH} \)-\(\textsf {E} \) on average, respectively.


Trajectory simplification Synchronous Euclidean distance One-pass line simplification 



This work is supported in part by National Key R&D Program of China 2018YFB1700403, NSFC U1636210&61421003, Shenzhen Institute of Computing Sciences, and the Fundamental Research Funds for the Central Universities. Funding was provided by National Natural Science Foundation of China (Grant No. U1636210), 973 program (Grant No. 2014CB340300), Beijing Advanced Innovation Center for Big Data and Brain Computing.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC)Beihang UniversityBeijingChina

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