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Journal Articles Machine Learning Year : 2021

Scalable clustering of segmented trajectories within a continuous time framework. Application to maritime traffic data

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Abstract

In the context of the surveillance of the maritime traffic, a major challenge is the automatic identification of traffic flows from a set of observed trajectories, in order to derive good management measures or to detect abnormal or illegal behaviours for example. In this paper, we propose a new modelling framework to cluster sequences of a large amount of trajectories recorded at potentially irregular frequencies. The model is specified within a continuous time framework, being robust to irregular sampling in records and accounting for possible heterogeneous movement patterns within a single trajectory. It partitions a trajectory into sub-trajectories, or movement modes, allowing a clustering of both individuals' movement patterns and trajectories. The clustering is performed using non parametric Bayesian methods, namely the hierarchical Dirichlet process, and considers a stochastic variational inference to estimate the model's parameters, hence providing a scalable method in an easy-to-distribute framework. Performance is assessed on both simulated data and on our motivational large trajectory dataset from the Automatic Identification System (AIS), used to monitor the world maritime traffic: the clusters represent significant, atomic motion-patterns, making the model informative for stakeholders.
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Dates and versions

hal-02617575 , version 1 (25-05-2020)
hal-02617575 , version 2 (25-05-2020)
hal-02617575 , version 3 (01-04-2021)

Identifiers

  • HAL Id : hal-02617575 , version 3

Cite

Pierre Gloaguen, Laetitia Chapel, Chloé Friguet, Romain Tavenard. Scalable clustering of segmented trajectories within a continuous time framework. Application to maritime traffic data. Machine Learning, 2021. ⟨hal-02617575v3⟩
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