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Application Analysis of Multi-Agent Collaborative AI-Driven Models in Urban Emergency Management

Mengxue Lyu et al.

Journal of Organizational and End User Computing2026https://doi.org/10.4018/joeuc.405159article
AJG 1ABDC B
Weight
0.50

Abstract

With global urbanization on the rise, cities face complex cascading risk impacts. Traditional emergency management has bottlenecks like fragmented multi-agent collaboration and single-point AI application, making it hard to respond to dynamic emergencies. This study integrates Complex Adaptive System (CAS) theory, AI empowerment theory, and AnyLogic simulation validation to develop a multi-agent collaborative AI-driven model. It builds an “agent-data-technology-process” four-layer coupled architecture, clarifies multi-agent interaction rules and AI integration paths, and covers the full “prevention-preparedness-response-recovery” cycle. Empirical validation shows that in three typical scenarios—urban waterlogging, large-scale crowd gatherings, cross-regional public health incidents—vs. traditional models, the proposed model shortens response time, improves resource efficiency, and reduces casualties. It resolves emergency collaboration dilemmas, enriches AI-empowered emergency management's theoretical system, and guides building a resilient smart city emergency system.

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https://doi.org/https://doi.org/10.4018/joeuc.405159

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@article{mengxue2026,
  title        = {{Application Analysis of Multi-Agent Collaborative AI-Driven Models in Urban Emergency Management}},
  author       = {Mengxue Lyu et al.},
  journal      = {Journal of Organizational and End User Computing},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.4018/joeuc.405159},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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