Conference Paper

Deep Representation Learning-Based Vessel Trajectory Clustering for Situation Awareness in Ship Navigation

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Abstract

Vessel trajectory clustering using historical AIS data has been a popular research topic in recent years. However, few studies have investigated applying deep learning techniques. In this study, deep representation learning is investigated for use in clustering historical AIS trajectories to provide insight into navigation patterns to support maritime situation awareness. A recurrent autoencoder and beta-variational recurrent autoencoder are investigated to generate fixed size vector representations of the AIS trajectories. Subsequently, clustering is facilitated by applying the Hierarchical Density-Based Spatial Clustering of Applications with Noise algorithm to the representations. The method was tested on historical AIS data for a region surrounding Tromsø, Norway, with successful results. The results also indicate that the beta-variational recurrent autoencoder was able to generate representations of the AIS trajectories that resulted in more compact clusters. ( Video Presentation: https://www.youtube.com/watch?v=KLQp1zwrvk8&feature=emb_logo )

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... Yao et al. [38] investigated clustering AIS trajectories using deep representation learning, where the results indicated that the deep learning approach outperformed non-deep learning based approaches. Murray and Perera [39] expanded this work, where it was found that a variational recurrent autoencoder architecture provided better representations for trajectory clustering. These methods, however, do not provide a method to predict the future trajectory of a selected vessel. ...
... To create these local models, it is suggested to cluster historical ship behavior using a variational recurrent autoencoder, as outlined in [39]. This approach is expanded to add more complexity to the model, resulting in improved clustering performance. ...
... It is, therefore, of interest to develop a framework to generate fixed size representations of the trajectories, such that standard clustering techniques can then be applied to the representations. Murray and Perera [39] suggested to utilize a deep representation learning-based approach to facilitate trajectory representation generation for subsequent clustering. The study argues that RNNs are ideal for such a task, as they are designed to generate representations of multivariate sequences via their hidden states. ...
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... In the case of this study, it is desirable to cluster historical AIS trajectories as illustrated in Fig. 5. Fig. 5 illustrates a subset of trajectory clusters from the data in Fig. 4. These clusters were discovered by applying the approach in Murray and Perera (2021b). This technique leverages a Variational Recurrent Autoencoder (Fabius and van Amersfoort, 2015) to generate fixed size vector representations of historical AIS trajectories, and subsequently clusters the representations using the Hierarchical Density-Based Clustering of Applications with Noise (HDBSCAN) algorithm (Campello et al., 2013). ...
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Variational Recurrent Auto-Encoders
  • O J R Fabius
  • Van Amersfoort
Fabius, O. & J. R. van Amersfoort (2015). Variational Recurrent Auto-Encoders. In Proceedings of the International Conference on Learning Representations (ICLR).