Application of Smoothing Technique to Model Predictive Traffic Signal Control

This paper introduces a new adaptive traffic signal control algorithm that operates within the model predictive control framework. Its primary objective is to enhance the traffic network's throughput under heavy load. To achieve this goal, a comprehensive traffic flow model and a specially tailored target function were used. The predictive model utilized in this algorithm is second-order macroscopic traffic model, enabling accurate prediction of traffic phenomena such as wave formations and nonlinear effects. Furthermore, the model can be refined using historical data, which enhances the precision of predictions. The paper outlines both the model itself and the numerical scheme utilized for its computations. The proposed target function considers the characteristics of a traffic dynamics and aims to provide a uniform distribution of vehicles in the transport network. Optimal control can be found as a solution to a continuous optimization problem related to a noisy zero-order oracle. The smoothing technique is used to solve the optimization problem. It allows using first-order stochastic optimization methods in the situation when the gradient of the target function is unknown. The developed traffic light control algorithm has been tested in the traffic simulation environment SUMO in the set of RESCO benchmarks.