A Survey of Distance-Based Vessel Trajectory Clustering: Data Pre-processing, Methodologies, Applications, and Experimental Evaluation

TL;DR

This survey addresses the problem of clustering vessel trajectories using distance-based methods by dissecting two core components: trajectory similarity measurement and clustering algorithms. It comprehensively surveys traditional distance metrics (e.g., Hausdorff, Fréchet, DTW, LCSS, EDR, ERP) and learning-based representations (Seq2Seq, CAE), and contrasts their performance within unsupervised clustering pipelines such as spectral and hierarchical methods. A key finding is that trajectory compression substantially improves both accuracy and efficiency of clustering, while interpolation often yields limited gains; an effective practical pairing observed is OWD distance with hierarchical clustering (average linkage). The study highlights a critical need for standardized vessel-trajectory benchmarks and datasets to enable rigorous cross-method comparisons and reproducible progress in maritime trajectory clustering.

Abstract

Vessel trajectory clustering, a crucial component of the maritime intelligent transportation systems, provides valuable insights for applications such as anomaly detection and trajectory prediction. This paper presents a comprehensive survey of the most prevalent distance-based vessel trajectory clustering methods, which encompass two main steps: trajectory similarity measurement and clustering. Initially, we conducted a thorough literature review using relevant keywords to gather and summarize pertinent research papers and datasets. Then, this paper discussed the principal methods of data pre-processing that prepare data for further analysis. The survey progresses to detail the leading algorithms for measuring vessel trajectory similarity and the main clustering techniques used in the field today. Furthermore, the various applications of trajectory clustering within the maritime context are explored. Finally, the paper evaluates the effectiveness of different algorithm combinations and pre-processing methods through experimental analysis, focusing on their impact on the performance of distance-based trajectory clustering algorithms. The experimental results demonstrate the effectiveness of various trajectory clustering algorithms and notably highlight the significant improvements that trajectory compression techniques contribute to the efficiency and accuracy of trajectory clustering. This comprehensive approach ensures a deep understanding of current capabilities and future directions in vessel trajectory clustering.

Figures (20)

• Figure 1: Paradigm of vessel trajectory clustering.
• Figure 2: Bibliometric analysis of vessel trajectory clustering from 2010-2023.
• Figure 3: Illustration of the traditional trajectory similarity measurement hu2023spatio.
• Figure 4: The structure of the sequence-to-sequence model yao2018learning.
• Figure 5: The structure of the Convolutional Auto-encoder model liang2021unsupervised.

Theorems & Definitions (1)

• Definition 1