Generative AI-Powered Digital Twins for Crowd Management in Large-Scale Events

Abstract

Managing safety and operational efficiency in large-scale events requires decision-support tools capable of representing complex crowd dynamics while enabling rapid and evidence-based operational assessment. This paper presents a Generative AI-driven simulation-enabled digital twin prototype that integrates an agent-based crowd simulation framework, an API-based execution pipeline, and a Large Language Model (LLM)-driven conversational interface within a unified architecture. The proposed framework enables the dynamic configuration, execution, and analysis of crowd scenarios under different operational conditions, including high-demand situations and emergency evacuation contexts. Experimental results show that the system can reproduce nonlinear crowd dynamics, identify congestion patterns, and assess evacuation performance. While evaluated under TRL-4 conditions, these results demonstrate the framework’s architectural potential to provide actionable insights for planning and safety evaluation once empirically calibrated with real-world data. A central contribution of this work is the introduction of an API-based execution paradigm that exposes the complete simulation lifecycle, including configuration, validation, execution, and output retrieval, through programmatic interfaces. This design supports reproducible, modular, and scalable what-if analysis. In addition, the integration of an LLM-based conversational interface allows non-technical users to interact with complex simulation models through natural language, improving accessibility without compromising execution control. The framework is validated through a TRL-4 prototype, demonstrating stable performance and reliable interaction behavior. Scalability is strictly confirmed within the evaluated hardware configuration, model abstraction level, and tested agent scale (up to 60,000 agents), providing a foundation for localized event management. Overall, the proposed system serves as a simulation-enabled digital twin prototype, demonstrating how models can transition from static analytical representations toward executable, interactive, and user-centered platforms, laying the necessary architectural groundwork for future operational decision support in complex urban environments.