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

TL;DR

This paper introduces a quantum annealing-based framework for identifying time-dependent critical links in urban transport networks, revealing their temporal concentration and impact on system resilience.

Contribution

It formulates a QUBO model for dynamic critical link detection and demonstrates its effectiveness using real traffic data and quantum annealing hardware.

Findings

Critical links are concentrated in specific time windows.

Disruptions during key periods cause significant delays.

Time-dependent analysis enhances resilience planning.

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.