Original scientific paper

https://doi.org/10.17559/TV-20250220002405

Gauging Urban Security Advances: A Fuzzy Hierarchical TOPSIS Model with MOPSO Optimization

Mingxu Yu ; 1) Key Laboratory of Urban Safety Risk Monitoring and Early Warning, Ministry of Emergency Management, Shenzhen Technology Institute of Urban Public Safety, Shenzhen 518023, China 2) China Academy of Industrial Internet, Beijing 100102, China
Jietan Geng ; China Academy of Industrial Internet, Beijing 100102, China
Duo Shang ; 1) Key Laboratory of Urban Safety Risk Monitoring and Early Warning, Ministry of Emergency Management, Shenzhen Technology Institute of Urban Public Safety, Shenzhen 518023, China 2) China Academy of Industrial Internet, Beijing 100102, China *
Jiyao Yin ; 1) Key Laboratory of Urban Safety Risk Monitoring and Early Warning, Ministry of Emergency Management, Shenzhen Technology Institute of Urban Public Safety, Shenzhen 518023, China 2) Shenzhen Key Laboratory of Urban Disasters Digital Twin, Shenzhen 518023, China
Zhangyu Chang ; 1) Key Laboratory of Urban Safety Risk Monitoring and Early Warning, Ministry of Emergency Management, Shenzhen Technology Institute of Urban Public Safety, Shenzhen 518023, China 2) Shenzhen Key Laboratory of Urban Disasters Digital Twin Shenzhen 518023, China
Rui Yan ; School of Economics and Management, University of Science and Technology Beijing, Beijing 100102, China

* Corresponding author.

Abstract

In the era of rapid urban digitalization, intelligent analysis systems are pivotal for urban safety management, yet their applicability lacks a robust evaluation framework. This study develops a comprehensive model integrating the Delphi method, Fuzzy Hierarchical TOPSIS, and MOPSO. Through two-round expert consultations, 36 indicators across four dimensions, technical performance, functionality, interaction modes, and cost-effectiveness, are identified. The MOPSO algorithm dynamically optimizes weights in the fuzzy TOPSIS framework, addressing static evaluation limitations and balancing conflicting objectives like reliability and cost-efficiency. Empirical results show a 12% average improvement in the closeness index and enhanced scenario adaptability, such as an 18% reduction in warning latency during emergencies. This hybrid approach bridges qualitative expert insights with quantitative optimization, offering a systematic tool for evaluating system applicability. The research enriches multi-criteria decision-making methodologies and supports evidence-based urban safety governance, facilitating resource allocation and sustainable planning.

Keywords

applicability evaluation; influencing factors; intelligent analysis system; MOPSO; urban safety

Hrčak ID:

342642

URI

https://hrcak.srce.hr/342642

Publication date:

31.12.2025.

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