Learning Activity Behavior Choice Models without Personal Data to Generate Behavioral Data for Social Simulations

This research proposes a method to generate arbitrary activity data for social simulation by utilizing a behavior choice model, learned from synthetic population data and activity data derived from public statistics without relying on hard-to-obtain personal data (actual behavior data). The effectiveness of the proposed method is validated using the activity simulator ActivitySim.