Traffic signal optimization in large-scale urban road networks: an adaptive-predictive controller using Ising models
<|reference_start|>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 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.<|reference_end|>
@article{inoue2024traffic,
    title={Traffic signal optimization in large-scale urban road networks: an
  adaptive-predictive controller using Ising models},
    author={Daisuke Inoue, Hiroshi Yamashita, Kazuyuki Aihara, Hiroaki Yoshida},
    journal={arXiv preprint arXiv:2406.03690},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03690},
    primaryClass={math.OC cs.ET cs.SY eess.SY quant-ph}
}