Importance sampling for activity path choice

Abstract

We propose a model for the choice of an activity pattern. Models of activity participation patterns allow to assess the impact of demand management strategies on activity and destination choices. In particular, we focus on choice set generation of activity patterns using recent developments in route choice modeling. Spatial choices deal with large choice sets. We develop a framework for choice set generation based on path choice. The activity-episode sequence is modeled as a path in an activity network defining the activity type, duration and time of day. The large dimensionality of the choice set is managed through an importance sampling based on Metropolis-Hastings algorithm. Our model can be used to forecast demand at the urban scale and also in pedestrian facilities, such as transport hubs or mass gathering. Validation of the approach is performed on synthetic data and a case study using WiFi traces on a campus is presented.

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