Model-based clustering of compositional trajectories for the analysis of mobility data

2026-06-16

AI Summary

This study addresses the challenge of clustering individual mobility trajectories derived from mobile phone signaling data by proposing a novel framework that integrates compositional data analysis with state-space modeling. The approach represents trajectories as temporal sequences of compositional vectors in the simplex space, explicitly incorporating both localization uncertainty and road network structure. By leveraging a mixture state-space model, the method enables interpretable trajectory clustering. As the first work to combine compositional data analysis with temporal state-space models for human mobility analysis, this research successfully identifies semantically meaningful group travel patterns in an empirical study of Padua, offering urban planners highly interpretable, data-driven insights for transportation decision-making.

Abstract

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.

Problem

• mobility trajectories
• compositional data
• model-based clustering
• urban mobility
• trajectory analysis

Innovation

• compositional trajectories
• state-space model
• model-based clustering
• mobility data
• road network integration