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New algorithms for the simplification of multiple trajectories under bandwidth constraints

Gilles Dejaegere, Mahmoud Sakr

TL;DR

This paper tackles the problem of compressing multiple moving trajectories under explicit bandwidth constraints by introducing time-windowed extensions of Squish, STTrace, and Dead Reckoning. It proposes four BandWidth-Constrained variants that share a time-windowed framework, including an improved priority computation (BWC-STTrace-Imp) and a cross-trajectory priority sharing approach (BWC-Squish). Through empirical evaluations on AIS and bird GPS datasets, the study demonstrates that BWC-STTrace-Imp excels with larger time windows while BWC-DR remains robust for small windows, and that BWC-Squish offers strong cost-performance in many settings. The work provides practical strategies for deploying trajectory compression in bandwidth-limited monitoring scenarios such as maritime AIS coverage and IoT-based object tracking, with potential for further optimization of window transitions and real-time parameter adaptation.

Abstract

This study introduces time-windowed variations of three established trajectory simplification algorithms. These new algorithms are specifically designed to be used in contexts with bandwidth limitations. We present the details of these algorithms and highlight the differences compared to their classical counterparts. To evaluate their performance, we conduct accuracy assessments for varying sizes of time windows, utilizing two different datasets and exploring different compression ratios. The accuracies of the proposed algorithms are compared with those of existing methods. Our findings demonstrate that, for larger time windows, the enhanced version of the bandwidth-constrained STTrace outperforms other algorithms, with the bandwidth-constrained improved version of \squish also yielding satisfactory results at a lower computational cost. Conversely, for short time windows, only the bandwidth-constrained version of Dead Reckoning remains satisfactory.

New algorithms for the simplification of multiple trajectories under bandwidth constraints

TL;DR

This paper tackles the problem of compressing multiple moving trajectories under explicit bandwidth constraints by introducing time-windowed extensions of Squish, STTrace, and Dead Reckoning. It proposes four BandWidth-Constrained variants that share a time-windowed framework, including an improved priority computation (BWC-STTrace-Imp) and a cross-trajectory priority sharing approach (BWC-Squish). Through empirical evaluations on AIS and bird GPS datasets, the study demonstrates that BWC-STTrace-Imp excels with larger time windows while BWC-DR remains robust for small windows, and that BWC-Squish offers strong cost-performance in many settings. The work provides practical strategies for deploying trajectory compression in bandwidth-limited monitoring scenarios such as maritime AIS coverage and IoT-based object tracking, with potential for further optimization of window transitions and real-time parameter adaptation.

Abstract

This study introduces time-windowed variations of three established trajectory simplification algorithms. These new algorithms are specifically designed to be used in contexts with bandwidth limitations. We present the details of these algorithms and highlight the differences compared to their classical counterparts. To evaluate their performance, we conduct accuracy assessments for varying sizes of time windows, utilizing two different datasets and exploring different compression ratios. The accuracies of the proposed algorithms are compared with those of existing methods. Our findings demonstrate that, for larger time windows, the enhanced version of the bandwidth-constrained STTrace outperforms other algorithms, with the bandwidth-constrained improved version of \squish also yielding satisfactory results at a lower computational cost. Conversely, for short time windows, only the bandwidth-constrained version of Dead Reckoning remains satisfactory.
Paper Structure (19 sections, 13 equations, 6 figures, 5 tables, 5 algorithms)

This paper contains 19 sections, 13 equations, 6 figures, 5 tables, 5 algorithms.

Table of Contents

  1. Introduction
  2. Compression under bandwidth constraints motivation
  3. Extension of AIS signal coverage
  4. Objects tracking over the Internet of Things
  5. Existing algorithms
  6. Squish
  7. STTrace
  8. DR
  9. BWC variants
  10. BWC-Squish and BWC-STTrace
  11. BWC-STTrace-Imp
  12. BWC-DR
  13. Empirical results
  14. Datasets
  15. AIS
  16. ...and 4 more sections

Figures (6)

  • Figure 1: AIS trips around Copenhagen and Malmo.
  • Figure 2: Birds trips.
  • Figure 3: Histogram of the quantity of points in different time-windows in samples obtained with TD-TR.
  • Figure 4: Histogram of the quantity of points in different time-windows in samples obtained with Squish.
  • Figure 5: Histogram of the quantity of points in different time-windows in samples obtained with STTrace.
  • ...and 1 more figures