Beyond Connectivity: Higher-Order Network Framework for Capturing Memory-Driven Mobility Dynamics

TL;DR

This paper introduces a higher-order network framework that captures memory-dependent mobility dynamics in transportation systems, improving analysis accuracy over traditional models by encoding sequential path dependencies.

Contribution

It extends classical graph models with higher-order Markov chains and de Bruijn graphs, enabling better analysis of memory-driven mobility patterns in transportation networks.

Findings

Higher-order models outperform first-order baselines in predictive tasks.

The third-order model balances accuracy and complexity effectively.

Memory effects are crucial for accurate transportation network analysis.

Abstract

Understanding and predicting mobility dynamics in transportation networks is critical for infrastructure planning, resilience analysis, and traffic management. Traditional graph-based models typically assume memoryless movement, limiting their ability to capture sequential dependencies inherent in real-world mobility patterns. In this study, we introduce a novel higher-order network framework for modeling memory-dependent dynamics in transportation systems. By extending classical graph representations through higher-order Markov chains and de Bruijn graph structures, our framework encodes the spatial and temporal ordering of traversed paths, enabling the analysis of structurally and functionally critical components with improved fidelity. We generalize key network analytics, including betweenness centrality, PageRank, and next-step prediction, to this higher-order setting and validate our approach on the Sioux Falls transportation network using agent-based trajectory data generated with MATSim. Experimental results demonstrate that higher-order models outperform first-order baselines across multiple tasks, with the third-order model achieving an optimal balance between predictive accuracy and model complexity. These findings highlight the importance of incorporating memory effects into network-based transportation analysis and offer a scalable, data-driven methodology for capturing complex mobility behaviors in infrastructure systems.