Macroscopic Activity-Based Modeling of Urban Active Mobility

2026-05-13

AI Summary

This study addresses the challenge of accurately modeling spatiotemporal active mobility patterns—such as walking and cycling—in urban environments while preserving individual privacy. The authors propose a macroscopic activity-based modeling framework leveraging non-intrusive sensor data, introducing an innovative “attendance function” to characterize individuals’ spatiotemporal travel behavior between activities. By reformulating aggregate count decomposition as a statistical inference problem, the method employs a Poisson count model, maximum likelihood estimation, and an efficient EM algorithm to enable scalable inference of unknown subpopulation sizes without requiring individual-level trajectories. Theoretical analysis and empirical results demonstrate that the framework effectively balances privacy preservation, computational efficiency, and modeling accuracy, successfully reconstructing fine-grained mobility patterns from aggregated observations.

Abstract

This paper develops a macroscopic, activity-based model of urban active mobility using nonintrusive sensor data. It introduces attendance functions to describe spatio-temporal travel patterns between activities and formulates the disaggregation of aggregated counts as a statistical inference problem. Counts are modeled as Poisson variables, and unknown subpopulation sizes are estimated via maximum likelihood, with theoretical guarantees and an efficient EM algorithm for computation. Grounded in a microscopic stochastic model, the framework offers a scalable and privacy-preserving approach to analyzing urban soft mobility dynamics.

Problem

• active mobility
• macroscopic modeling
• activity-based modeling
• urban mobility
• nonintrusive sensor data

Innovation

• activity-based modeling
• attendance functions
• statistical inference
• Poisson disaggregation
• privacy-preserving mobility analysis