Emergency response management (ERM) is a challenge faced by communities across the globe. First responders must respond to various incidents, such as fires, traffic accidents, and medical emergencies. They must respond quickly to incidents to minimize the risk to human life. Consequently, considerable attention has been devoted to studying emergency incidents and response in the last several decades. In particular, data-driven models help reduce human and financial loss and improve design codes, traffic regulations, and safety measures. This tutorial paper explores four sub-problems within emergency response: incident prediction, incident detection, resource allocation, and resource dispatch. We aim to present mathematical formulations for these problems and broad frameworks for each problem. We also share open-source (synthetic) data from a large metropolitan area in the USA for future work on data-driven emergency response.

翻译：应急响应管理（ERM）是全球各地社区面临的挑战。第一响应者必须应对各类事件，如火灾、交通事故和医疗急救。他们需要快速响应事件，以最大限度降低对人类生命的风险。因此，过去几十年中，关于应急事件及其响应的研究受到了广泛关注。特别是，数据驱动模型有助于减少人员和财务损失，并改进设计规范、交通法规和安全措施。本教程论文探讨了应急响应中的四个子问题：事件预测、事件检测、资源分配和资源调度。我们旨在提出这些问题的数学表述以及每个问题的通用框架。我们还分享了来自美国某大都市区的开源（合成）数据，用于未来数据驱动应急响应研究。