Desert expressway corridors exposed to strong wind hazards often rely on single high-grade routes, with limited alternatives, high detour costs, and low network redundancy. These constraints make it difficult to maintain traffic operation resilience through route substitution alone. Taking the Hami–Tuyugou section of the G30 Lianhuo Expressway in Xinjiang, China, as a case study, this study investigates the formation and evaluation of traffic operation resilience in a wind-hazard-affected, low-redundancy desert expressway corridor. A hierarchical indicator system was constructed with four first-level, fourteen second-level, and thirty-one third-level indicators. Fuzzy DEMATEL(Decision Making Trial and Evaluation Laboratory)–ISM(Interpretive Structural Modeling) was used to identify causal relationships and hierarchical transmission paths; fuzzy DANP(DEMATEL-based Analytic Network Process)–AHP(Analytic Hierarchy Process) was applied to determine indicator weights; and a cloud model was employed to evaluate the overall resilience level. The results show that institutional adaptability, organizational learning, monitoring and information support, and multi-actor collaboration are the main upstream drivers. The corridor was evaluated as Grade IV, indicating a relatively high resilience level approaching Grade V. Sensitivity analyses confirm the robustness of the substantive conclusion. The findings suggest that, under low-redundancy conditions, resilience depends less on structural redundancy and more on adaptive governance, information support, and coordinated response.

Fixed indicator weights cannot fully capture changes in governance priorities as wind–sand hazards intensify. Using the Xinjiang section of the G30 Lianhuo Expressway as a case study, this study classified wind–sand events into three scenarios: warning and restricted traffic, high-risk traffic control and diversion, and extreme conditions with comprehensive rescue. A five-dimensional evaluation system containing 15 indicators was established. Scenario-specific weights were determined using the Best–Worst Method, while TOPSIS was applied to evaluate governance performance based on objective response records and independent text coding. The dominant dimensions across the three scenarios were risk monitoring and early warning, decision activation and traffic control, and rescue resources and coordination, with weights of 0.356, 0.392, and 0.421, respectively. The corresponding closeness coefficients were 0.577, 0.667, and 0.733. As hazard severity increased, governance priorities shifted from risk communication to traffic diversion, interdepartmental coordination, evacuation, medical assistance, and support for vulnerable groups. Compared with the fixed-weight model, the dynamic model improved the closeness coefficients by 0.029, 0.064, and 0.092. The results show that scenario-specific weighting can support staged response, resource allocation, and human safety protection on desert expressways.