# Predictive Clustering of Vessel Behavior Based on Hierarchical Trajectory Representation

arXiv: https://arxiv.org/abs/2403.08838 · HTML: https://mlsanity.com/paper/2403.08838/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 44 references that are in the map.
- First posted on arXiv 13 Mar 2024.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering](https://mlsanity.com/paper/1610.04794/) (2016)
- [Deep Learning for Spatio-Temporal Data Mining: A Survey](https://mlsanity.com/paper/1906.04928/) (2019)
- [SOM-VAE: Interpretable Discrete Representation Learning on Time Series](https://mlsanity.com/paper/1806.02199/) (2018)
- [Temporal Phenotyping using Deep Predictive Clustering of Disease Progression](https://mlsanity.com/paper/2006.08600/) (2020)
- [Incremental Few-Shot Semantic Segmentation via Embedding Adaptive-Update and Hyper-class Representation](https://mlsanity.com/paper/2207.12964/) (2022)
- [Challenges in Vessel Behavior and Anomaly Detection: From Classical Machine Learning to Deep Learning](https://mlsanity.com/paper/2004.03722/) (2020)
- [An Unsupervised Learning Method with Convolutional Auto-Encoder for Vessel Trajectory Similarity Computation](https://mlsanity.com/paper/2101.03169/) (2021)
- [COBRAS: Fast, Iterative, Active Clustering with Pairwise Constraints](https://mlsanity.com/paper/1803.11060/) (2018)
- [Predictive K-means with local models](https://mlsanity.com/paper/2012.09630/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Survey of Distance-Based Vessel Trajectory Clustering: Data Pre-processing, Methodologies, Applications, and Experimental Evaluation](https://mlsanity.com/paper/2407.11084/) (2024)

## Abstract (arXiv)

Vessel trajectory clustering, which aims to find similar trajectory patterns, has been widely leveraged in overwater applications. Most traditional methods use predefined rules and thresholds to identify discrete vessel behaviors. They aim for high-quality clustering and conduct clustering on entire sequences, whether the original trajectory or its sub-trajectories, failing to represent their evolution. To resolve this problem, we propose a Predictive Clustering of Hierarchical Vessel Behavior (PC-HiV). PC-HiV first uses hierarchical representations to transform every trajectory into a behavioral sequence. Then, it predicts evolution at each timestamp of the sequence based on the representations. By applying predictive clustering and latent encoding, PC-HiV improves clustering and predictions simultaneously. Experiments on real AIS datasets demonstrate PC-HiV's superiority over existing methods, showcasing its effectiveness in capturing behavioral evolution discrepancies between vessel types (tramp vs. liner) and within emission control areas. Results show that our method outperforms NN-Kmeans and Robust DAA by 3.9% and 6.4% of the purity score.