Hybrid Quantum-Classical Optimization for Emergency-Priority Traffic Signal Control

Abstract:Emergency-priority traffic signal control requires reducing emergency-vehicle delay while limiting disruption to ordinary traffic. This work investigates a finite-horizon QUBO formulation and compares Exact QUBO optimization, QAOA, CVaR-QAOA, a quantum machine-learning policy, fixed-time control, and max-pressure control in a three-intersection corridor. The study evaluates three traffic-demand regimes, examines the effect of the emergency-priority weight, and compares quantum solutions with the exact QUBO optimum. The optimization-based controllers achieved reliable emergency progression across all tested demand regimes, while max-pressure produced lower ordinary-traffic delay. Increasing emergency priority reduced emergency travel time but increased congestion, with diminishing improvements at larger weights. CVaR-QAOA reproduced the exact QUBO solution more accurately than standard QAOA, although the resulting differences in closed-loop traffic performance were comparatively small.

arXiv:2610.21883 [quant-ph]

(or arXiv:2610.21883v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2610.21883 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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