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

Pierre Gloaguen (1) , Laetitia Chapel (2) , Chloé Friguet (2) , Romain Tavenard (3, 4)

1 MIA-Paris - Mathématiques et Informatique Appliquées
2 OBELIX - Environment observation with complex imagery
3 LETG - Rennes - Littoral, Environnement, Télédétection, Géomatique
4 OBELIX - Observation de l’environnement par imagerie complexe

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.

Mots clés: Trajectory clustering; Scalability; Stochastic variational inference; Hierarchical Dirichlet process; AIS data; Continuous time

Domaines: Apprentissage [cs.LG]; Statistiques [math.ST]

Pierre Gloaguen, Laetitia Chapel, Chloé Friguet, Romain Tavenard. Scalable clustering of segmented trajectories within a continuous time framework. Application to maritime traffic data.. 2020. ⟨hal-02617575v2⟩