The growing adoption of electric vehicles (EVs) and fast charging stations (FCSs) has intensified the coupling between urban transportation network (TN) and power distribution network (PDN), forming a closely coupled transportation and power network (CTPN). This interdependence, however, can exacerbate urban vulnerability during disasters: disruptions in one subnetwork can trigger internal cascading failures, which propagate to the interdependent subsystem via coupling mechanisms. Existing research often overlooks these dynamic cross-system interactions during disruptions, leading to suboptimal emergency resource allocation and potentially misleading recovery strategies. To address this limitation, this study proposes an optimized decision-making framework for coordinated emergency resource allocation in CTPN, aiming to minimize total travel time and active/reactive power curtailments under resource constraints. As a result, a mixed-integer linear programming (MILP) model is developed that integrates, for the first time, TN lane reversal, FCS charging pile management, and PDN line switching within a unified framework. Route choice behavior, EV charging dynamics, and PDN operational constraints are concurrently considered in this model, meticulously characterizing the multi-scale interdependencies among traffic flow dynamics, FCS operations, and power dispatch. Numerical simulations demonstrated that compared to link reversing or isolated optimization strategies for TN and PDN, the proposed method exhibited significant advantages in mitigating traffic congestion, fulfilling travel demand, and reducing power curtailment. This study provides urban planners with a scalable and high-precision modeling methodology and decision tool, offering theoretical support for CTPN resilience enhancement and coordinated post-disaster recovery.
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