Macroscopic parking dynamics modeling and optimal real-time pricing considering cruising-for-parking

Abstract

Network traffic congestion is known to be partially caused by vehicles cruising for parking. In this paper, we quantify and assess the effect of cruising-for-parking by developing a macroscopic parking dynamics model for a parking-dense neighborhood with limited parking supply, where cruising-for-parking is explicitly considered in conjunction with the interactions between on- and off-street parking. The model is mainly built upon the system dynamics of different families of vehicles in the neighborhood, which is governed by mass conservation equations utilizing the concept of macroscopic or network fundamental diagram (MFD or NFD). To reduce parking congestion and improve the overall system performance, two real-time parking pricing strategies are developed and integrated with the parking model: (i) a feedback-based reactive pricing strategy driven by the parking occupancy; and (ii) a model-based predictive or proactive pricing strategy that explicitly aims to minimize the expected aggregate cruising delay. Extensive numerical experiments have been conducted to compare the performance of the two strategies applied to both on- and off-street parking. The results provide new insights into how a parking system shall be better managed, with key implications for policy making summarized.