Macroscopic model for on- and off-street parking: Traffic effects

Abstract

On- and off-street parking has been gaining attention in recent research, but its interdependency with searching-for-parking traffic and traffic performance is still unknown. In this paper, we develop a macroscopic formulation to model off-street parking dynamically and integrate it into an urban traffic and on-street parking study to better replicate reality. The parking searchers decide between driving to a parking garage or searching for an on-street parking place in the network. We analyze the influence of on- and off-street parking on the traffic system with regard to different parking pricing schemes. These pricing scenarios include demand-responsive parking pricing methodologies for both on- and off-street parking. Their effects on cruising-for-parking and traffic performance are evaluated. This macroscopic pricing model is only based on aggregated data at the network level over time. Hence, it saves on data collection efforts and reduces the computational costs significantly compared to most literature. This easy to implement methodology can be solved with a simple numerical solver. The model provides a preliminary idea for city councils regarding the interdependency between on- and off-street parking, and their influence on searching-for-parking traffic (cruising), the congestion in the network (traffic performance), the total driven distance (environmental conditions), and the revenue created by on- and off-street parking fees for the city.