A methodological foundation for proactive disruption mitigation in transport networks: Integrating route similarity and elastic demand in stochastic user equilibrium-based link criticality analysis

Assessing link criticality in transport networks requires accounting for route similarity (due to shared links) and elastic demand (ED) (due to travelers’ responses to congestion). Route similarity induces correlated route choices, diverting traffic from overlapping routes, while ED adjusts travel volumes as travelers may switch modes, change their departure times, or forego trips altogether in response to congestion. Current methods often ignore both factors, oversimplifying their joint impact on traffic flows and link criticality rankings. Addressing this gap can enhance assessment reliability, supporting strategies for mitigating medium- to long-term network disruptions such as infrastructure deterioration or collapse. This study suggests an approach to advance the link criticality index (LCI), a state-of-the-art method for link criticality analysis, via the integration of route similarity. Specifically, we present an advanced LCI method based on the cross-nested logit (CNL) stochastic user equilibrium (SUE) assignment model, which can flexibly and realistically capture the effect of route similarity on both individuals’ route choices and network flows. Numerical experiments on toy and real-size networks show and quantify how route similarity can affect link criticality values under the assumption of fixed demand (FD) and elastic demand (ED). The results demonstrate that the criticality ranking of links may be significantly altered if route similarity and demand elasticity are overlooked.