Quadratic Unconstrained Binary Formulation for Traffic Signal Optimization on Real-World Maps

TL;DR

This paper presents a novel QUBO formulation for traffic signal optimization on complex road networks, validated with real-world data, but finds current quantum annealing hardware underperforms classical solvers.

Contribution

It introduces a new QUBO model capable of handling complex intersections and tests its effectiveness with realistic urban data.

Findings

QUBO model reduces vehicle waiting times

D-Wave quantum annealer cannot find optimal solutions efficiently

Classical optimizer outperforms D-Wave in speed and accuracy

Abstract

The D-Wave quantum annealing machine can quickly find the optimal solution for quadratic unconstrained binary optimization (QUBO). One of the applications where the use of quantum annealing is desired is in problems requiring rapid calculations. One such application is the traffic signal optimization. Several studies have used quantum annealing; however, they are formulated in relatively unrealistic settings, such as only crossroads on a map. We propose a different formulation of QUBO that can also deal with T-junctions and multi-forked roads. The simulation of urban mobility (SUMO) was used to validate the efficiency of our approach and verify the feasibility of real-time control using geographical information data that were very similar to the real world. Our model could reduce the waiting time at red lights for vehicles. In addition, we compared our results with those of the Gurobi Optimizer to confirm whether the D-Wave machine could find the ground state. Unfortunately, our results show that the D-Wave machine could not find the optimal solution and was slower than the Gurobi Optimizer in computation time.