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.

中文翻译：

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动态宏观停车定价与决策模型

开发动态宏观停车定价模型以最大化城市的收入，同时最小化网络上的总巡航时间。提议的响应式定价方案考虑了停车搜索现象。这意味着停车费除了根据停车位占用率而变化外，还根据搜索车辆的数量而变化。与大多数文献相比，这种宏观定价模型嵌入到动态的宏观城市交通和停车模型中，对数据的要求较低，主要与网络层面的平均值和概率分布有关。苏黎世市内一个地区的案例研究，