A CVaR‐Based Optimal Perimeter Control Framework With Consideration of Boundary Queue Length

Perimeter control is a method used to manage clusters of intersections at regional boundaries, based on the macroscopic fundamental diagram (MFD). However, the MFD‐based model of urban traffic networks often contains parameter uncertainty and system noise, leading to excessive queues at regional boundaries and reducing the effectiveness of perimeter control. To address these challenges, this paper proposes an optimized control framework for urban traffic networks based on the Conditional Value at Risk (CVaR). The framework includes a Markov jump linear system model to simulate the traffic network and uses CVaR to measure congestion risk caused by uncertainty. The CVaR‐based optimization objectives and the regional boundary queue model are established to enhance control strategies. The proposed CVaR‐based optimal perimeter control framework is compared with no control, bang–bang control, and stochastic model predictive control through simulation. Results show that the CVaR‐based framework significantly reduces the number of vehicles and travel time, outperforming the other three control strategies in alleviating congestion. This study demonstrates the potential of CVaR‐based control in improving urban traffic management.