Optimal storage and loading zones within surface parking facilities for privately owned automated vehicles

摘要

There is much speculation about the prospective impacts of automated vehicles (AVs) on parking supply and behaviour, however the literature contains little quantitative evidence. In this study, we develop a mixed-integer non-linear optimisation (MINLP) model of revenue maximisation to design parking facility layouts for privately-owned automated cars that include separate vehicle-storage and drop-off/pick-up zones (DOPU, or alternatively termed "PUDO zones"). The control variable is the allocation of space between these two competing uses. The model balances between revenue derived from parking (including revenue during activity time) and costs associated with the range of AVs' parking and loading/unloading activities. Via numerical analysis of an archetypal shopping centre's parking facility, the authors demonstrate that the model responds intuitively to the stimulus of systematically varying the input parameters. This study is intended to provide an incremental advance to support researchers and practitioners tasked with quantifying the impacts of AVs on the parking system.