理解城市移动模式对设计高效且可持续的交通系统至关重要。受帕多瓦市及其周边地区实际应用的启发，我们提出了一种新颖的统计框架，用于分析和聚类基于电话数据生成的移动轨迹。我们引入一种成分表示法来刻画个体移动行为，该方法将设备定位的不确定性与周围道路网络信息相结合，在每个时间点编码与观测位置相容的不同道路类型所占比例。这一表述自然地纳入了测量不确定性，并使轨迹演化于单纯形空间中。为建模此类数据，我们构建了一个面向成分时间序列的状态空间框架，同时刻画电话定位误差与潜在移动过程的时间动态特性。在此表示基础上，我们提出一种基于状态空间模型混合分布的模型驱动聚类方法，以识别具有相似演化特征的轨迹群组。该方法可将个体移动行为在群体层面聚合为可解释的移动模式。案例研究结果表明，该方法能够有效揭示有意义的移动行为，其发现对政策制定者具有潜在参考价值。

Understanding urban mobility patterns is crucial for designing efficient and sustainable transportation systems. Motivated by an application to the municipality of Padova and its surroundings, we propose a novel statistical framework for the analysis and clustering of mobility trajectories derived from telephonic data. We introduce a compositional representation of individual movements that integrates the uncertain device location with information on the surrounding road network, encoding at each time point the proportions of different road types compatible with the observed position. This formulation naturally accounts for measurement uncertainty and yields trajectories evolving in the simplex. To model these data, we develop a state-space framework for compositional time series that captures both the telephonic measurement error and the temporal dynamics of the latent mobility process. Building on this representation, we propose a model-based clustering approach based on mixtures of state-space models to identify groups of trajectories with similar evolution. This allows us to aggregate individual movements into interpretable mobility patterns at the population level. The results of the case study demonstrate the ability of the approach to uncover meaningful mobility behaviors, providing insights that are potentially relevant to policy makers.