Data-driven identification of critical links in transport networks using quantum annealing

Abstract

In urban transport systems, time-varying demand and network conditions cause the importance of infrastructure elements to evolve, requiring the identification of period-specific critical links to support systemlevel risk and resilience analysis. However, static or time-averaged network analyses struggle to capture the temporal variation of infrastructure importance at the city scale. To address this gap, this study proposes a time-dependent critical link identification framework for large-scale urban transport networks. The problem is formulated as a Quadratic Unconstrained Binary Optimisation (QUBO) model and solved using quantum annealing on D-Wave hardware. Empirical analysis using real-world traffic data reveals a strong temporal concentration of critical links. Rather than persistently influencing system performance, critical links emerge mainly within a small number of key time windows, during which even limited disruptions can lead to substantial network delay amplification. These findings demonstrate the value of time-dependent analysis for risk screening, stress testing, and resilience-oriented transport management.