Online offset optimization for urban traffic network with distributed model predictive control

Abstract

This paper proposes a distributed control framework to optimize the offset for a path in a traffic network with arbitrary topology. Each intersection along the target path applies the model predictive control to optimize their own phase sequence and green splits with the objective of minimizing the sum of queue lengths. The first intersection on this path is regarded as the main intersection and responsible for optimizing the start green time and duration of the first phase on this path with a weighted objective according to the real-time traffic information, while the other intersections take the constraints of offset imposed by intersections ahead into consideration. The signal cycles of these intersections are fixed but allowed to be different. For computation efficiency, the nonlinear optimization problem is approximately reformulated as a mixed-integer linear programming problem. Numerical experiments on a calibrated network of Caohejing District in Shanghai indicate that our proposed method can effectively decrease delay time and waiting time especially at medium and high traffic loads.