Evaluating Emergency Governance Capacity for Wind–Sand Hazards on Desert Expressways Using a Scenario-Based Dynamic BWM-TOPSIS Weighting Model: A Case Study of the G30 Lianhuo Expressway in Xinjiang, China

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.

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Publication Details

Journal
Infrastructures
Published
2026-09-20
DOI
https://doi.org/10.3390/infrastructures11090336
Primary Topic
Infrastructure Resilience and Vulnerability Analysis
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article
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Evaluating Emergency Governance Capacity for Wind–Sand Hazards on Desert Expressways Using a Scenario-Based Dynamic BWM-TOPSIS Weighting Model: A Case Study of the G30 Lianhuo Expressway in Xinjiang, China

Wuping Ran, Yao Wang, Xiaodong Liu, Long Cheng et al.
Infrastructures
Infrastructure Resilience and Vulnerability Analysis
article

Evaluating Emergency Governance Capacity for Wind–Sand Hazards on Desert Expressways Using a Scenario-Based Dynamic BWM-TOPSIS Weighting Model: A Case Study of the G30 Lianhuo Expressway in Xinjiang, China

Wuping Ran, Yao Wang, Xiaodong Liu, Long Cheng, Jing Zhang, Tao Sun, Maimaitiaili Maturing, Zulyaiga Sabir
article en

Abstract

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.

InfrastructuresVol. 11(9)
Xinjiang Institute of Engineering (CN), China Communications Construction Company (China) (CN), Xinjiang University (CN)
Openalex Percentile: Top 17%
Infrastructure Resilience and Vulnerability Analysis
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