A dynamic macroscopic parking pricing and decision model

Abstract

A dynamic macroscopic parking pricing model is developed to maximize the revenue for a city, while simultaneously minimizing the total cruising time on the network. The proposed responsive pricing scheme takes the parking search phenomenon into consideration. This means that the parking fee also changes in response to the number of searching vehicles, in addition to changes in response to the parking occupancy. Compared to most literature, this macroscopic pricing model is embedded into a dynamic macroscopic urban traffic and parking model and has rather low data requirements, mostly related to average values and probability distributions at the network level. The case study of an area within the city of Zurich, Switzerland shows that the model provides a preliminary idea for city councils regarding an optimal parking pricing policy resulting in financial revenues that can be obtained without having a significant negative effect on short-term traffic performance and environmental conditions.