基于需求预测和服务质量指数的城市消防站选址规划
Urban Fire Station Location Planning using Predicted Demand and Service Quality Index
摘要：
在这篇文章中，我们提出了一个系统的方法，消防站的位置规划。我们开发了基于随机森林和极端梯度提升的机器学习模型，用于需求预测，并利用这些模型进一步定义了一个衡量城市消防服务质量的通用指标。我们的模型是建立在从多个不同来源收集的空间数据。适当的设施规划的效力取决于选择的候选人，消防站可以与现有的站，如果有的话。此外，需要注意从这些候选人到需求地点的旅行时间，以保持消防安全标准。在这里，我们提出了一个基于旅行时间的聚类技术，以确定合适的候选人。最后，我们开发了一个优化问题，以选择最佳位置安装新的消防站。我们的优化问题是建立在最大覆盖问题，整数规划的基础上。我们进一步开发了一个两阶段的随机优化模型来描述我们的决策结果的信心。我们提出了一个详细的实验研究，我们提出的方法与城市的维多利亚消防局，MN，美国合作。我们的需求预测模型达到了80%的真阳性率和约20%的假阳性率。我们帮助维多利亚消防局选择一个新的消防站使用我们的方法的位置。我们提出了详细的结果，改善统计数据，通过定位一个新的设施，我们的方法，在维多利亚市。
英文摘要：
In this article, we propose a systematic approach for fire station location planning. We develop machine learning models, based on Random Forest and Extreme Gradient Boosting, for demand prediction and utilize the models further to define a generalized index to measure quality of fire service in urban settings. Our model is built upon spatial data collected from multiple different sources. Efficacy of proper facility planning depends on choice of candidates where fire stations can be located along with existing stations, if any. Also, the travel time from these candidates to demand locations need to be taken care of to maintain fire safety standard. Here, we propose a travel time based clustering technique to identify suitable candidates. Finally, we develop an optimization problem to select best locations to install new fire stations. Our optimization problem is built upon maximum coverage problem, based on integer programming. We further develop a two-stage stochastic optimization model to characterize the confidence in our decision outcome. We present a detailed experimental study of our proposed approach in collaboration with city of Victoria Fire Department, MN, USA. Our demand prediction model achieves true positive rate of 80% and false positive rate of 20% approximately. We aid Victoria Fire Department to select a location for a new fire station using our approach. We present detailed results on improvement statistics by locating a new facility, as suggested by our methodology, in the city of Victoria.
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。
