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.

翻译：本文利用非侵入式传感器数据，构建了一种基于宏观活动的城市主动出行模型。该模型引入出席函数来描述活动间的时空出行模式，并将聚合计数分解问题转化为统计推断问题。将计数建模为泊松变量，通过最大似然估计未知子群体规模，并提供了理论保证及高效EM算法用于计算。该框架基于微观随机模型构建，为分析城市慢行交通动态提供了一种可扩展且保护隐私的方法。