Relaxation–discretization algorithm for spatially constrained secondary location assignment

Agent-based transport models demand that the daily activity patterns of artificial agents are described in great detail. While choice models for residential locations or workplaces exist, only few approaches are available to find locations for highly constrained secondary activities such as grocery shopping or recreation at high resolution. The paper describes a data-driven approach of assigning viable locations to such secondary locations while maintaining consistency with homes, workplaces and other fixed points in an artificial traveler's daily plan. Two use cases for Switzerland and Île-de-France are presented, which show that the algorithm is able to assign locations while providing realistic distance distributions that are consistent with mode-specific travel times.