突发事件零延迟：基于机器学习的城市环境中建设项目对应急响应影响的量化方法
Zero Latency for Emergencies: A Machine Learning based Approach to Quantify Impact of Construction Projects on Emergency Response in Urban Settings
摘要：
城市环境中的持续建设和恢复不可避免地影响到第一反应者到达紧急地点的时间。目前关于应急反应评估的研究工作侧重于个案研究，在个案研究中，超级风暴桑迪）的应急响应时间进行了分析。也存在旨在评估与各种约束/车队规模相关的响应时间的基于模拟的研究。然而，他们没有分析具体的变化（例如，新的和正在进行的建设项目）影响第一反应者的紧急响应时间。本文旨在填补这一空白，并提出了一种新的方法来预测预期的紧急响应时间为一个给定的位置使用织物的区域建设活动。这种方法依赖于应急反应的历史记录和城市机构颁发的建筑许可证。该方法首先定义了一个区域的签名（邮政编码）的基础上，在该区域允许随着时间的推移，历史建筑工程类型的分布的建设活动。然后，将共享相似特征的区域聚类，以确定施工特征和紧急响应时间之间是否存在关系。接下来，部署监督学习算法来预测每个聚类的平均紧急响应时间。该方法使用纽约市的建筑许可证和应急响应记录进行了测试，并且可以很容易地复制到具有类似公共数据集的其他城市。本研究作为定量了解建设项目对城市环境中生活质量（QoL）指标（特别是紧急响应时间）影响的第一步。
英文摘要：
Continuous construction and rehabilitation in urban settings have unavoidable impacts on arrival times of first responders to emergency locations. Current research efforts on emergency response assessments focus on case studies, where specific periods (e.g., super storm Sandy) of emergency response times are analyzed. Simulation based studies that aim to evaluate response times in relation to various constraints/fleet sizes also exist. However, they do not analyze how specific changes (e.g., new and ongoing construction projects) in urban settings impact emergency response times of first responders. This paper aims to fill the gap and proposes a novel approach to predict the expected emergency response time for a given location using the fabric of zones regarding construction activities. This approach relies on historical records of emergency response and construction permits issued by city agencies. The approach first defines the signature of a zone (by zip codes) for construction activities based on the distribution of historical construction work types permitted in that zone over time. Then, zones that share similar signatures are clustered to find if there exists a relationship between construction signatures and emergency response times. Next, supervised learning algorithms are deployed to predict the average emergency response times for each cluster. The approach was tested using New York City's construction permit and emergency response records, and can be easily replicated for other cities with similar public datasets. This study serves as the first step towards quantitatively understanding construction projects' impact on a quality of life (QoL) indicator (specifically emergency response times) in urban settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
