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A Rapid Surrogate Framework for Automated Screening of Shelter Candidate Sites and Evacuation Policies in NPP Using Link-Based Path Metric
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- Authors
- Yewon Kim, Hyewon Song, Chang Soo Kim, Moonyoung Chung
- Issue Date
- 2026-08
- Citation
- International Conference on Automation Science and Engineering (CASE) 2026, pp.1-6
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Agent-based evacuation simulations are powerful tools for precise policy evaluation, yet they suffer from high computational costs when exploring the vast design space of shelter locations and evacuation policies during the planning phase of Nuclear Power Plants (NPPs). This paper proposes a rapid AI-based surrogate framework for the automated screening of shelter candidate sites and allocation strategies by approximating high-fidelity simulation outputs at a significantly lower cost. A case study was conducted on the traffic environment around the Precautionary Action Zone (PAZ) of the Kori Nuclear Power Plant, demonstrating that the proposed model achieves up to a Spearman’s ρ of 0.70 and top 50% recall 80%, suggesting that it successfully captures the non-linear complexity of the road network. The surrogate generates predictions in under one second, achieving a 500-fold speedup over conventional simulation runs. These results demonstrate that the proposed framework establishes a critical technical foundation for the preliminary and automated screening of hundreds of thousands of diverse policy candidates, a scale of evaluation that is computationally prohibitive for traditional agent-based simulators.
- Keyword
- Intelligent Transportation Systems, Agent-Based Systems, Machine learning
- KSP Keywords
- Agent-based system, Case studies, Design space, High-fidelity simulation, Intelligent Transport System(ITS), Intelligent Transportation, Link-based, Lower cost, Non-linear, Nuclear Power Plant, Planning Phase