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.
@article{arxiv.2605.13742,
title = {Macroscopic Activity-Based Modeling of Urban Active Mobility},
author = {Romain Azaïs and Adrien Marion and Florian Patout},
journal= {arXiv preprint arXiv:2605.13742},
year = {2026}
}