Traffic Signal Optimization in Large-Scale Urban Road Networks: An Adaptive-Predictive Controller Using Ising Models

Realizing smooth traffic flow is important for achieving carbon neutrality. Adaptive traffic signal control, which considers traffic conditions, has thus attracted attention. However, it is difficult to ensure optimal vehicle flow throughout a large city using existing control methods because of their heavy computational load. Here, we propose a control method called AMPIC (Adaptive Model Predictive Ising Controller) that guarantees both scalability and optimality. The proposed method employs model predictive control to solve an optimal control problem at each control interval with explicit consideration of a predictive model of vehicle flow. This optimal control problem is transformed into a combinatorial optimization problem with binary variables that is equivalent to the Ising problem. This transformation allows us to use Ising solvers, which have been widely studied and are expected to offer fast and efficient optimization performance. The method works adaptively according to traffic conditions such as the structure of the road network and feedback from observation of the traffic system. We performed numerical experiments using a microscopic traffic simulator for a realistic city road network. Compared to the classical pattern control method, the results show that AMPIC increases the vehicle cruising speed by 13%, reduces the waiting vehicle ratio to 60%, and lowers the CO 2emissions to only 25% of the original level. The model predictive approach with a long prediction horizon thus effectively improves control performance. Systematic parametric studies on model cities indicate that the proposed method realizes smoother traffic flows for large city road networks. Among Ising solvers, D-Wave’s quantum annealing is shown to find near-optimal solutions at a reasonable computational cost.