ReeMark: Reeb Graphs for Simulating Patterns of Life in Spatiotemporal Trajectories
This work addresses the need for realistic trajectory simulation in urban environments, offering a scalable solution for applications like epidemiology and traffic management, though it appears incremental by combining existing mobility structures in a novel probabilistic topological model.
The paper tackled the problem of accurately modeling human mobility for urban planning and related fields by introducing Markovian Reeb Graphs, a framework that simulates spatiotemporal trajectories preserving Patterns of Life, achieving strong fidelity as shown by Jensen-Shannon Divergence evaluations on datasets from Atlanta and Berlin.
Accurately modeling human mobility is critical for urban planning, epidemiology, and traffic management. In this work, we introduce Markovian Reeb Graphs, a novel framework for simulating spatiotemporal trajectories that preserve Patterns of Life (PoLs) learned from baseline data. By combining individual- and population-level mobility structures within a probabilistic topological model, our approach generates realistic future trajectories that capture both consistency and variability in daily life. Evaluations on the Urban Anomalies dataset (Atlanta and Berlin subsets) using the Jensen-Shannon Divergence (JSD) across population- and agent-level metrics demonstrate that the proposed method achieves strong fidelity while remaining data- and compute-efficient. These results position Markovian Reeb Graphs as a scalable framework for trajectory simulation with broad applicability across diverse urban environments.