Urban network throughput optimization via model predictive control using the link transmission model

Developing practicable and efficient real-time signal control strategies for urban road networks is a major challenge with significant scientific and practical relevance. For the sake of practical feasibility, many urban traffic control strategies are based on simplified models. In this way, a trade-off can be made between controller accuracy, and (computational) complexity of the controller. However, designing a controller which operates efficiently with low computational time under different traffic conditions (i.e. under-saturated, saturated and over-saturated) remains a challenge. Several linear and quadratic model predictive control approaches are described in the literature to tackle this problem without considering the shock-wave dynamics of spill-back under over-saturated conditions. The principal contribution of this paper is the formulation of a linear model predictive controller that takes the shock-wave dynamics into account. This is realized by modeling the traffic dynamics in a link using the link transmission model. The performance of the proposed controller is compared with two other existing strategies. The total time spent by all the vehicles in the network and the computation time have been applied as performance indexes for the appraisal of the control strategies. Simulation results show that the control strategy proposed in this paper achieves better throughput under over-saturated conditions within comparable, low computation time.Transport & PlanningCivil Engineering and Geoscience