High-performance optimal incentive-seeking in transactive control for traffic congestion

Traffic congestion has dire economic and social impacts in modern metropolitan areas. To address this problem, paper we introduce a novel type of model-free transactive controllers to manage vehicle traffic highway networks for which precise mathematical models are not available. Specifically, consider system with managed lanes on dynamic tolling mechanisms can be implemented real-time using measurements from the roads. We present three incentive-seeking feedback able find optimal incentives (e.g., tolls) that persuade users follow suitable driving behavior minimizes predefined performance index. The agnostic respect exact model highway, they also guarantee fast convergence tolls by leveraging non-smooth hybrid combine continuous-time dynamics discrete-time dynamics. provide numerical examples illustrate advantages different presented techniques.