Multi-agent model-based predictive control for large-scale urban traffic networks using a serial scheme

Reference:
Z. Zhou, B. De Schutter, S. Lin, and Y. Xi, "Multi-agent model-based predictive control for large-scale urban traffic networks using a serial scheme," IET Control Theory & Applications, vol. 9, no. 3, pp. 475-484, 2015.

Abstract:
Urban traffic networks are large-scale systems, consisting of many intersections controlled by traffic lights and interacting connected links. For efficiently regulating the traffic flows and mitigating the traffic congestion in cities, a network-wide control strategy should be implemented. Control of large-scale traffic networks is often infeasible by only using a single controller, i.e. in a centralized way, because of the high dimension, complicated dynamics, and uncertainties of the system. In this paper we propose a multi-agent control approach using a congestion-degree-based serial scheme. Each agent employs a model-based predictive control approach and communicates with its neighbors. The congestion-degree-based serial scheme helps the agents to reach an agreement on their decisions regarding traffic control actions as soon as possible. A simulation study is carried out on a hypothetical large-scale urban traffic network based on the presented control strategy. The results illustrate that this approach has a better performance with regard to computation time compared with the centralized control method and a faster convergence speed compared with the classical parallel scheme.