Intelligent Urban Emergency Response: Integrating Large Language Models, Multi-Objective Optimization, and Reinforcement Learning

Urban emergency response plays a critical role in protecting lives and property during crises. However, traditional emergency response systems often struggle with inefficient resource allocation, uncoordinated relief efforts, and delayed decision-making. To address these challenges, we propose a novel intelligent decision-making framework that integrates large language models for victim interaction, multi-objective optimization for resource allocation, and reinforcement learning for personalized dispatch. Our approach consists of three main components: an intelligent voice assistant that engages in human-like conversations with victims, a multi-objective optimization model (ERMOO) for efficient resource allocation, and a Deep Q-Networks (DQN) based reinforcement learning approach for personalized dispatch policies. Extensive experiments on both synthetic and real-world datasets demonstrate the superior performance of our approach compared to existing baselines. The results highlight the immense potential of integrating cutting-edge AI techniques to revolutionize urban emergency response practices. By bridging the gap between artificial intelligence and human intelligence, our framework provides a solid foundation for creating more resilient, equitable, and human-centered emergency management systems. This work aims to inspire further research and innovation in this critical domain, ultimately benefiting urban societies worldwide.