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On the Efficiency of Information-Assisted Search for Parking Space: A Game-Theoretic Approach

机译：关于停车位的信息辅助搜索效率：一种游戏理论方法

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摘要

This paper seeks to systematically explore the efficiency of the uncoordinated information-assisted parking search in urban environments with two types of parking resource facilities: inexpensive but limited facilities (public) and expensive yet unlimited ones (private); an additional cruising cost is incurred when deciding for a public facility but failing to actually utilize one. Drivers decide whether to go for the public or directly for the private facilities, assuming perfect knowledge of prices and costs, total parking capacities and demand; the latter information can be broadcast by an ideal centralized information dissemination mechanism, assisting the otherwise uncoordinated parking search process. Drivers are viewed as strategic decision-makers that aim at minimizing the cost of the acquired parking spot. We formulate the resulting game as an instance of resource selection games and derive its Nash equilibria and their dependence on the environmental parameters such as the parking demand and supply as well as the pricing policy. The cost at the equilibrium states is compared to that under the optimal resource assignment (dictated to the drivers directly by an ideal centralized scheme) and conditions are derived for minimizing the related price of anarchy. Finally, the numerical results and the presented discussion provide hints for the practical management and pricing of public and private parking resources.
机译：本文旨在系统地探讨有关地区环境中未协助的信息辅助停车搜索的效率，拥有两种类型的停车资源设施：廉价但有限的设施（公共）和昂贵但无限的（私人）;在决定公共设施但未能实际利用一个人时，发生了额外的巡航费用。司机决定是否为公众或直接前往私人设施，假设对价格和成本，总停车能力和需求完全了解;后者信息可以通过理想的集中信息传播机制来广播，协助其他不协调的停车搜索过程。司机被视为战略决策者，旨在最大限度地降低所获得的停车位的成本。我们将生成的游戏制定为资源选择游戏的实例，并导出其纳什均衡及其对环境参数的依赖，例如停车需求和供应以及定价政策。将均衡状态的成本与在最佳资源分配下（直接由理想的集中方案直接指定驱动程序），并导出条件以最小化无政府状态的相关价格。最后，数值结果和所讨论的讨论为公共和私人停车场的实际管理和定价提供了提示。

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