A dynamic macroscopic parking pricing model

Abstract

Demand-responsive pricing has been gaining attention in recent research, but its interdependency on searching-for-parking traffic is still unknown and a model is needed. In this paper, a dynamic macroscopic parking pricing model with its influence on cruising vehicles is developed. The methodology builds on a previous dynamic macroscopic urban traffic and parking study, whose model is extended by parking pricing to better replicate reality. This study describes a demand-responsive pricing scheme which takes parking search phenomena into consideration. The parking fee also changes in response to the demand, compared to previous studies that only focus on the current occupancy rate of parking spaces. In addition, the model investigates how parking pricing can affect the decision of searchers between paying the current parking cost or keep on searching to obtain a lower cost. In other words, it describes the trade-off of a user between a longer cruising time and a lower parking price. To do so, multiple user classes are considered with respect to their origin and valuation of time. Several cost variables are taken into consideration such as the predicted parking cost at future time and the penalty cost for the past cruising distance in the network. Compared to most literature, this macroscopic pricing model has rather low data requirements, mostly related to average values and probability distributions across time at the network level. Hence, this macroscopic pricing approach saves on data collection efforts and reduces the computation costs significantly. Moreover, the model can be easily solved with a simple numerical solver without the use of complex simulation software. The model also provides a preliminary idea for city councils regarding the impacts of parking fee policies on the searching-for-parking traffic (cruising), the congestion in the network (traffic performance), the total driven distance (environmental conditions) and the revenue created by parking fees for the city. Based on the model, the influence of demand-responsive parking pricing on traffic can be better understood. It is then hoped that the knowledge and methodology obtained here can be transferred and used in urban areas.