Dynamic port resilience assessment in the maritime network: modeling flow evolution and cascading failure

Ports are critical to international trade but are highly vulnerable to disruptions, which can trigger cascading congestion across maritime networks. Evaluating port resilience under such dynamic conditions is challenging. This paper proposes a framework to assess dynamic port resilience after network failures, integrating dynamic allocation models with both resilience triangle and moment‑based metrics. Key features include: (1) Queuing theory to capture weekly variations in port operations, estimating efficiency dynamically; (2) Prediction and inertia parameters for shipping routes and ports to reflect adaptive adjustments in the network; (3) Differentiated reallocation strategies to simulate maritime-specific characteristics. A case study on the Asia-Europe maritime network validates the framework, revealing spatial and temporal heterogeneity in congestion patterns. Results show that higher prediction parameters, especially for shipping routes, stabilize resilience faster, while moderate inertia parameters enhance resilience, with optimal effects achieved through balanced route inertia and port inertia. Moment‑based metrics further reveal contrasting pathways. High prediction produces faster but volatile recovery, while moderate inertia supports more stable and coordinated adaptation. Validation against the 2021 Yantian Port disruption confirms that adaptive settings with strong prediction and moderate inertia yield operational patterns similar to the observed data. These findings reveal key trade‑offs between agility and stability and provide operational insight into mitigating congestion and enhancing resilience in maritime networks.