论文标题
交通拥堵的交易控制中高性能的最佳寻求奖励
High-Performance Optimal Incentive-Seeking in Transactive Control for Traffic Congestion
论文作者
论文摘要
交通拥堵在现代大都市地区产生了可怕的经济和社会影响。为了解决这个问题，在本文中，我们介绍了一种新型的无模型交易控制器，以管理无法确切的数学模型的高速公路网络中的车辆流量。具体来说，我们考虑了一个具有托管车道的高速公路系统，可以使用道路的测量值实时实施动态收费机制。我们提出了三个寻求激励的反馈控制者，能够实时找到最佳的经济激励措施（例如通行费），这些反馈控制者说服高速公路用户遵循适当的驾驶行为，以最大程度地减少预定义的性能指数。控制器对高速公路的确切模型是不可知的，并且还可以通过利用结合连续时间动力学和离散时间动态的非平滑和混合动态机制来确保快速收敛到最佳通行费。我们提供数值示例，以说明不同提出的技术的优势。
Traffic congestion has dire economic and social impacts in modern metropolitan areas. To address this problem, in this paper we introduce a novel type of model-free transactive controllers to manage vehicle traffic in highway networks for which precise mathematical models are not available. Specifically, we consider a highway system with managed lanes on which dynamic tolling mechanisms can be implemented in real-time using measurements from the roads. We present three incentive-seeking feedback controllers able to find in real-time the optimal economic incentives (e.g., tolls) that persuade highway users to follow a suitable driving behavior that minimizes a predefined performance index. The controllers are agnostic with respect to the exact model of the highway, and they are also able to guarantee fast convergence to the optimal tolls by leveraging non-smooth and hybrid dynamic mechanisms that combine continuous-time dynamics and discrete-time dynamics. We provide numerical examples to illustrate the advantages of the different presented techniques.
