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

arXiv: https://arxiv.org/abs/2407.11084 · HTML: https://mlsanity.com/paper/2407.11084/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 146 references that are in the map.
- First posted on arXiv 13 Jul 2024.

## Builds on (most cited first, 12 of 12)

- [A Survey on Trajectory Data Management, Analytics, and Learning](https://mlsanity.com/paper/2003.11547/) (2020)
- [Deep Learning Methods for Vessel Trajectory Prediction based on Recurrent Neural Networks](https://mlsanity.com/paper/2101.02486/) (2021)
- [Hierarchical Clustering better than Average-Linkage](https://mlsanity.com/paper/1808.02227/) (2018)
- [Trajectory Similarity Measurement: An Efficiency Perspective](https://mlsanity.com/paper/2311.00960/) (2023)
- [Asynchronous Trajectory Matching-Based Multimodal Maritime Data Fusion for Vessel Traffic Surveillance in Inland Waterways](https://mlsanity.com/paper/2302.11283/) (2023)
- [Clustering of Driving Encounter Scenarios Using Connected Vehicle Trajectories](https://mlsanity.com/paper/1807.08415/) (2018)
- [Spatio-Temporal Trajectory Similarity Measures: A Comprehensive Survey and Quantitative Study](https://mlsanity.com/paper/2303.05012/) (2023)
- [An Unsupervised Learning Method with Convolutional Auto-Encoder for Vessel Trajectory Similarity Computation](https://mlsanity.com/paper/2101.03169/) (2021)
- [GPU-Accelerated Compression and Visualization of Large-Scale Vessel Trajectories in Maritime IoT Industries](https://mlsanity.com/paper/2004.13653/) (2020)
- [A Spatio-temporal Track Association Algorithm Based on Marine Vessel Automatic Identification System Data](https://mlsanity.com/paper/2010.15921/) (2020)
- [One-Pass Trajectory Simplification Using the Synchronous Euclidean Distance](https://mlsanity.com/paper/1801.05360/) (2018)
- [Predictive Clustering of Vessel Behavior Based on Hierarchical Trajectory Representation](https://mlsanity.com/paper/2403.08838/) (2024)

## Built on by (most cited first, 3 of 3)

- [Contrast & Compress: Learning Lightweight Embeddings for Short Trajectories](https://mlsanity.com/paper/2506.02571/) (2025)
- [Human-Centric Traffic Signal Control for Equity: A Multi-Agent Action Branching Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2602.02959/) (2026)
- [MoCo-AIS: A Contrastive Learning Framework for Similarity Computation of Vessel Trajectories](https://mlsanity.com/paper/2606.17978/) (2026)

## Abstract (arXiv)

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.