Higher-order Network Analysis of Human Mobility Data

2026-05-30

AI Summary

This study addresses the challenge of rigorously evaluating how well synthetic human mobility data reproduces real individual behaviors while balancing statistical utility and privacy preservation. It proposes the first path-level higher-order network analysis framework to systematically assess the realism of synthetic trajectories by comparing path patterns between real and simulated mobility within infrastructure networks. Integrating higher-order network modeling, path sequence analysis, and synthetic population mobility simulation, the approach is applied to the Île-de-France region, revealing critical limitations in existing models’ ability to capture dynamic path structures. The findings highlight significant deficiencies in the topological fidelity of synthetic paths and offer a novel direction for advancing mobility simulation methodologies and enabling more nuanced analyses of higher-order human movement behaviors.

Abstract

The detailed study of individual human mobility requires large-scale high-resolution datasets, but collecting such datasets in a way that is both statistically powerful and privacy preserving is a challenging and expensive task. In response, researchers have built tools to generate complex synthetic populations of agents that can be used to simulate synthetic individual mobility data, potentially obviating the difficulties of data collection. While these simulation-based approaches offer a promising avenue for expanding individual mobility research, it is difficult to asses whether such tools are effective at generating realistic mobility traces. In this work, we develop a framework for comparing observed and simulated mobility data using a higher-order network framework that focuses on analyzing patterns of movement in the paths individuals take through the underlying infrastructure network. We apply our framework to a case study comparing the NetMob 2025 Data Challenge Dataset, which includes individual mobility data for thousands of residents of the Île-de-France region, with a sophisticated open-source synthetic population and mobility simulation model of the same region. We show that while simulated mobility data is indeed promising as a surrogate for observed mobility, there are some key limitations to the simulation paradigm from a path-based perspective, which we discuss along with potential future remediations and open challenges for higher-order mobility network analysis.

Problem

• human mobility
• synthetic data
• higher-order networks
• mobility simulation
• data validation

Innovation

• higher-order network
• human mobility
• synthetic population
• mobility simulation
• path-based analysis