Activity and Travel Pattern Scheduling with A Perturbed Utility Route Choice Model

Abstract

This paper formulates daily activity scheduling—decisions on when, where, and for what purpose to perform activities and travel—as a Perturbed Utility Route Choice (PURC) problem on a time– space network. Unlike conventional discrete choice models, the proposed framework represents behavior directly as link flows, corresponding to the number of individuals at each location and time. In this framework, optimal flows are sparse, allowing the model to endogenously restrict the effective consideration set without ex ante enumeration while capturing flexible substitution and complementarity patterns across schedules. To address the large size of time–space networks, we propose a column-generation-based estimation algorithm that iteratively expands partial networks. We further introduce a group-based perturbation structure to capture activity-specific correlations beyond those implied by network topology. An empirical application using the Danish National Travel Survey demonstrates that the model can be estimated on realistic networks and reproduces key activity–travel patterns.