Dynamic route redundancy-oriented strategic planning towards resilient transportation networks

Abstract

Sufficient route redundancy ensures the availability of alternative routes during disruptions for critical trips (e.g., evacuations, relief transportation), which is essential for sustaining transportation network resilience. Existing studies on route redundancy focus mainly on assessment rather than optimization, and most adopt a static perspective that ignores the inherently dynamic nature of resilience. This study introduces the definition, evaluation, and optimization methods for dynamic network redundancy. We propose period-based metrics that account for travelers’ adaptive behavior and network congestion and develop a link-based day-to-day model under uncertainty coupled with Dial counting to consistently measure redundancy. A two-stage stochastic bi-level programming model is then formulated to identify investment strategies that maximize expected dynamic redundancy under uncertain disruptions. In the first stage, planners allocate budgets between link retrofitting and new link construction, while in the second stage, travelers dynamically reroute on a daily basis following disruptions. To solve the resulting non-convex optimization problem, we implement a Bayesian optimization framework with parallel scenario evaluation. Experiments on both test and real-world networks demonstrate the properties, features, and applicability of the proposed methods. Results indicate that accounting for dynamic traffic evolution and congestion can reduce redundancy estimates by up to 40% compared to static assessments in the 16-node test network, particularly under severe disruptions and high congestion levels. The spatial–temporal evolution of congestion patterns, which influences travelers’ perception of alternative routes, is naturally captured by dynamic redundancy but overlooked in static assessments. In the Anaheim network, increasing the budget from $300 million to $1200 million raises dynamic redundancy from 4.5 % to 10.1 %, illustrating diminishing marginal returns. The framework developed in this study provides a decision-support tool for more informed, resilience-oriented network planning.