A Novel Context-Aware Douglas–Peucker (CADP) Trajectory Compression Method

Most traditional trajectory compression methods, such as the Douglas–Peucker (DP) method, consider only spatial characteristics and disregard contextual factors, including environmental context. This paper proposes a new way of trajectory formulation by considering all spatial, internal, environment...

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Bibliographic Details
Published in:ISPRS international journal of geo-information Vol. 14; no. 2; p. 58
Main Authors: Mehri, Saeed, Hooshangi, Navid, Mahdizadeh Gharakhanlou, Navid
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.02.2025
Subjects:
Algorithms
Collision avoidance
Compression
Context
context awareness
data compression
Datasets
Douglas–Peucker
Efficiency
Methods
Performance evaluation
prediction
Route planning
Semantics
trajectory
Trajectory analysis
Velocity
ISSN:2220-9964, 2220-9964
Online Access:Get full text
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Summary:Most traditional trajectory compression methods, such as the Douglas–Peucker (DP) method, consider only spatial characteristics and disregard contextual factors, including environmental context. This paper proposes a new way of trajectory formulation by considering all spatial, internal, environmental, and semantic contexts to capture all contextual aspects of moving objects. Then, we propose the Context-Aware Douglas–Peucker (CADP) method for trajectory compression. These facts are confirmed by experiments with real AIS data showing that, while CADP preserves the same computational efficiency of DP (i.e., at O(n2)), it outperforms DP and two-stage Context-Aware Piecewise Linear Segmentation (two-stage CPLS) methods in preserving agent movement behavior, obtaining compressed trajectories that are closer to the original ones and that are much more useful in base analyses such as trajectory prediction. Specifically, the LSTM-based models trained on CADP-compressed trajectories have relatively lower RMSEs than others compressed by either DP or two-stage CPLS. Therefore, CADP is more scalable and efficient, thus making it more practical for large-scale engineering applications; with the improvement in trajectory analysis accuracy achieved by the suggested method, a wide range of critical engineering applications can be potentially improved, such as collision avoidance and route planning. Future work will focus on spatial auto-correlation and uncertainty to extend the robustness and applicability of the approach.
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ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi14020058