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.

已知网络流量拥塞部分原因是车辆在停车场巡航。在本文中，我们通过为停车位有限的停车密集社区开发宏观停车动力学模型来量化和评估停车留存的效果，在该模型中，明确考虑了停车留存及其之间的相互作用。 -和路边停车。该模型主要基于附近不同车辆系列的系统动力学，该动力学由质量守恒方程利用宏观或网络基本图（MFD或NFD）的概念控制。为了减少停车拥堵并改善整体系统性能，开发了两种实时停车定价策略并将其与停车模型集成：（i）由停车位驱动的基于反馈的被动定价策略；（ii）基于模型的预测或主动定价策略，其明确目标是最大程度地减少预期的总巡航延迟。已经进行了广泛的数值实验，以比较应用于路内和路外停车的两种策略的性能。结果为如何更好地管理停车系统提供了新的见解，并总结了政策制定的关键意义。