Method and system for simulating users in the context of a parking lot based on the automatic learning of a user choice decision function from historical data considering multiple user behavior profiles

Abstract

Methods and systems for modeling user arrival and choice in the context of off-street parking solutions. A first component models the arrival and duration of stay of users as a function of time, taking into account different user profiles (or 'clusters'), captured by a latent variable. A second component provides a ranking function (for each user cluster), wherein the input features describing the 'choice' constitute status variables associated different car park(s), and the output constitutes a preferred car park and a pricing scheme. The system simulates different user behaviors by assuming some standard groups of users will behave similarly. Groups of users or user profiles are learned automatically. The profiles are then employed as a key element for automatically learning a decision function of parking users, and automatically learning one decision function per profile.