Length-Preserving Vessel Trajectory Simplification With Controllable Compression-Precision Bound
Abstract
Vessel trajectory data from the Automatic Identification System (AIS) is important for a wide range of maritime analysis and management applications. As AIS datasets continue to grow in scale, trajectory simplification (or compression) algorithms have become indispensable tools for managing their computational demands. Existing approaches, however, largely overlook the preservation of trajectory length, a critical metric of direct relevance to maritime environmental auditing and voyage management. We address this gap by framing trajectory simplification as a pair of dual optimization problems subject to error constraints: Compression-Driven Simplification (CDS), which minimizes data volume for a given length-error tolerance, and Precision-Driven Simplification (PDS), which minimizes length approximation error for a given compression target. Both problems are solved within a unified dynamic programming framework, and we provide mathematical proofs establishing that our formulation yields globally optimal solutions. This guarantees flexible, direct control over both compression ratio and length-approximation fidelity. { We benchmark CDS and PDS on real-world AIS datasets spanning diverse navigation scenarios and representative water area, comparing against state-of-the-art baselines. Experimental results on public benchmark datasets demonstrate that even under a small length error tolerance (ε=0.1 nmi), our methods achieve about 91\% data compression rate, approximately 14\% higher than the best-performing baseline, which implies that our methods are capable of maximally preserving the trajectory length while reducing data redundancy significant.
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BibTeX
@misc{tu2026lengthpreserving,
title = {{Length-Preserving Vessel Trajectory Simplification With Controllable Compression-Precision Bound}},
author = {Enmei Tu and Zhiye Xie and Xixia Xu and Shuhang Chen},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.38295},
eprint = {2610.38295},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.38295},
}
RIS
TY - UNPB
TI - Length-Preserving Vessel Trajectory Simplification With Controllable Compression-Precision Bound
AU - Enmei Tu
AU - Zhiye Xie
AU - Xixia Xu
AU - Shuhang Chen
PY - 2026
DA - 2026/10/28
PB - arXiv
DO - 10.48550/arxiv.2610.38295
UR - https://arxiv.org/abs/2610.38295
AB - Vessel trajectory data from the Automatic Identification System (AIS) is important for a wide range of maritime analysis and management applications. As AIS datasets continue to grow in scale, trajectory simplification (or compression) algorithms have become indispensable tools for managing their computational demands. Existing approaches, however, largely overlook the preservation of trajectory length, a critical metric of direct relevance to maritime environmental auditing and voyage management. We address this gap by framing trajectory simplification as a pair of dual optimization problems subject to error constraints: Compression-Driven Simplification (CDS), which minimizes data volume for a given length-error tolerance, and Precision-Driven Simplification (PDS), which minimizes length approximation error for a given compression target. Both problems are solved within a unified dynamic programming framework, and we provide mathematical proofs establishing that our formulation yields globally optimal solutions. This guarantees flexible, direct control over both compression ratio and length-approximation fidelity. { We benchmark CDS and PDS on real-world AIS datasets spanning diverse navigation scenarios and representative water area, comparing against state-of-the-art baselines. Experimental results on public benchmark datasets demonstrate that even under a small length error tolerance (ε=0.1 nmi), our methods achieve about 91\% data compression rate, approximately 14\% higher than the best-performing baseline, which implies that our methods are capable of maximally preserving the trajectory length while reducing data redundancy significant.
ER -