Quantum Annealing Approach for the Optimal Real-time Traffic Control using QUBO

Traffic congestion is one of the major issues in urban areas, particularly when traffic loads exceed the road’s capacity, resulting in higher petrol consumption and carbon emissions as well as delays and stress for road users. In Asia, the traffic situation can be further deteriorated by road sharing of scooters. How to control the traffic flow to mitigate the congestion has been one of the central issues in transportation research. In this study, we employ a quantum annealing approach to optimize the traffic signals control at a real-life intersection with mixed traffic flows of vehicles and scooters. Considering traffic flow is a continuous and emerging phenomenon, we used quadratic unconstrained binary optimization (QUBO) formalism for traffic optimization, which has a natural equivalence to the Ising model and can be solved efficiently on the quantum annealers, quantum computers or digital annealers. In this article, we first applied the QUBO traffic optimization to artificially generated traffic for a simple intersection, and then we used real-time traffic data to simulate a real "Dongda-Keyuan" intersection with dedicated cars and scooter lanes, as well as mixed scooter and car lanes. We introduced two types of traffic light control systems for traffic optimization: C-QUBO and QUBO. Our rigorous QUBO optimizations show that C-QUBO and QUBO outperform the commonly used fixed cycle method, with QUBO outperforming C-QUBO in some instances. It has been found that QUBO optimization significantly relieves traffic congestion for the unbalanced traffic volume. Furthermore, we found that dynamic changes in traffic light signal duration greatly reduce traffic congestion.