Macroscopic Activity-Based Modeling of Urban Active Mobility

TL;DR

This paper presents a scalable, privacy-preserving macroscopic model of urban active mobility using sensor data, employing statistical inference and an EM algorithm to analyze travel patterns.

Contribution

It introduces attendance functions and a Poisson-based inference framework for modeling urban active mobility at a macroscopic level.

Findings

Developed a Poisson-based statistical inference method.

Provided theoretical guarantees for the model.

Implemented an efficient EM algorithm for estimation.

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.