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, used to monitor the world maritime traffic: the clusters represent significant, atomic motion-patterns, making the model informative for stakeholders.

在海上交通监视的背景下，一个主要挑战是从一组观察到的轨迹中自动识别交通流量，以便得出良好的管理措施或检测异常或非法行为。在本文中，我们提出了一种新的建模框架，用于对以潜在不规则频率记录的大量轨迹的序列进行聚类。该模型在连续时间框架内指定，对记录中的不规则采样具有鲁棒性，并考虑到单个轨迹内可能的异构运动模式。它将轨迹划分为子轨迹或运动模式，允许对个人的运动模式和轨迹进行聚类。聚类使用非参数贝叶斯方法执行，即分层狄利克雷过程，并考虑随机变分推理来估计模型的参数，因此在易于分发的框架中提供了一种可扩展的方法。性能在模拟数据和自动识别系统的大型轨迹数据集上进行评估，用于监控世界海上交通：集群代表重要的原子运动模式，使模型为利益相关者提供信息。