Traffic signal optimization in large-scale urban road networks: an adaptive-predictive controller using Ising models

TL;DR

This paper introduces AMPIC, an adaptive-predictive traffic signal control method using Ising models, which improves traffic flow and reduces emissions in large-scale urban networks by leveraging efficient combinatorial optimization.

Contribution

The paper presents a scalable, optimal traffic signal control method that transforms traffic management into an Ising problem solvable by quantum annealing or classical solvers.

Findings

AMPIC achieves faster vehicle speeds and less waiting time.

The method reduces CO2 emissions compared to classical controls.

Quantum annealing provides near-optimal solutions efficiently.

Abstract

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 so-called Ising problem. This transformation allows us to use an Ising solver, which has been widely studied and is expected to have fast and efficient optimization performance. We performed numerical experiments using a microscopic traffic simulator for a realistic city road network. The results show that AMPIC enables faster vehicle cruising speed with less waiting time than that achieved by classical control methods, resulting in lower CO2 emissions. 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.