We propose an innovative and generic methodology to analyse individual and collective behaviour through individual trajectory data. The work is motivated by the analysis of GPS trajectories of fishing vessels collected from regulatory tracking data in the context of marine biodiversity conservation and ecosystem-based fisheries management. We build a low-dimensional latent representation of trajectories using convolutional neural networks as non-linear mapping. This is done by training a conditional variational auto-encoder taking into account covariates. The posterior distributions of the latent representations can be linked to the characteristics of the actual trajectories. The latent distributions of the trajectories are compared with the Bhattacharyya coefficient, which is well-suited for comparing distributions. Using this coefficient, we analyse the variation of the individual behaviour of each vessel during time. For collective behaviour analysis, we build proximity graphs and use an extension of the stochastic block model for multiple networks. This model results in a clustering of the individuals based on their set of trajectories. The application to French fishing vessels enables us to obtain groups of vessels whose individual and collective behaviours exhibit spatio-temporal patterns over the period 2014-2018.

翻译：本文提出一种创新且通用的方法论，通过个体轨迹数据分析个体与群体行为。本研究源于海洋生物多样性保护及基于生态系统的渔业管理背景下，利用监管跟踪数据中采集的渔船GPS轨迹数据。我们通过卷积神经网络作为非线性映射构建轨迹的低维潜在表示，具体通过训练考虑协变量的条件变分自编码器实现。潜在表示的后验分布可与实际轨迹特征相关联，并利用适用于分布比较的巴氏系数对轨迹潜在分布进行比较。基于该系数，我们分析每艘渔船个体行为随时间的变化规律。针对群体行为分析，构建邻近图并采用多网络随机块模型的扩展方法，该模型基于个体轨迹集实现聚类。对法国渔船的实证应用表明，在2014-2018年期间，个体与群体行为呈现出时空模式的渔船群体得以识别。