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Updated: Aug 24, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
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A trajectory data compression algorithm based on spatio-temporal characteristics.

Yanling Zhong1, Jinling Kong1, Juqing Zhang1

  • 1School of Geological Engineering and Geomatics, Chang'an University, Xi'an, Shaanxi, China.

Peerj. Computer Science
|October 20, 2022
PubMed
Summary
This summary is machine-generated.

A new trajectory data compression algorithm, CASC, effectively preserves spatial-temporal characteristics. CASC outperforms traditional methods like DP and TD-TR in retaining trajectory direction and velocity information.

Keywords:
Data compressionOnline algorithmSpatial-temporal characteristicsTrajectories data

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Area of Science:

  • Data Science
  • Computer Science
  • Geospatial Analysis

Background:

  • The exponential growth of trajectory data presents significant storage and analysis challenges.
  • Traditional trajectory compression methods often neglect crucial temporal information, focusing primarily on spatial characteristics.

Purpose of the Study:

  • To introduce a novel data compression algorithm, CASC (Compression algorithm based on the spatio-temporal characteristics), designed for trajectory data.
  • To address the limitations of existing methods by incorporating spatio-temporal features for enhanced compression.

Main Methods:

  • Developed the CASC algorithm, utilizing azimuth difference, velocity difference, and time interval as key compression parameters.
  • Validated CASC using the Microsoft Geolife1.3 dataset.
  • Compared CASC against Douglas-Peucker (DP), Top-Down Time Ratio (TD-TR), and Opening Window (OPW) algorithms based on compression rate, directional accuracy, distance error, and online/offline capabilities.

Main Results:

  • CASC demonstrated superior retention of trajectory direction compared to DP, TD-TR, and OPW at similar compression rates.
  • Velocity characteristics were stably preserved with CASC for speed thresholds up to 100%.
  • CASC, an online algorithm, exhibited better compression quality and more stable error distribution than the online algorithm OPW and the offline algorithms DP and TD-TR.

Conclusions:

  • The CASC algorithm offers a significant advancement in trajectory data compression, outperforming established methods.
  • CASC effectively balances compression efficiency with the preservation of critical spatial-temporal trajectory information.
  • Its online processing capability makes CASC suitable for real-time trajectory data applications.