Road networks are susceptible to disruptions. Since disruptions on certain roads have graver impacts, critical link identification is vital in disruption preparedness. To identify critical links, most approaches systematically inject disruptions and evaluate the ensuing after-effects. Since degradation patterns are uncertain, such iterative approaches either presume knowledge of disruption patterns or try numerous patterns but soon escalate to computational impracticality on real-life road networks. This study obviates both, using a novel approach which measures the contribution of each link to a predefined operational limit on the network. It generates a criticality hierarchy on real-life networks, while on hypothetical symmetric networks it rightly identifies all links as critical. © Indian Academy of Sciences.