Nonlinear optimization for freeway control using variable-speed signaling

Abstract

A traffic control problem with a dynamic macroscopic model is considered by means of simulation. An optimal control problem is stated for variable-speed signaling in order to improve traffic behavior near congestion. A traffic state estimator based on the extended Kalman filter is designed to generate real-time estimates of the traffic density and, by means of these, to activate speed signaling. The variable-speed signaling control law is closed loop and is set by minimizing (or maximizing) a performance criterion. The optimization procedure is based on Powell's method, and its off-line execution is computationally tractable on low-cost computers too. Simulation results demonstrate the efficacy of the proposed approach for preventing and reducing congestion.