智慧城市网格管理事件模式挖掘与预测

Urban Grid Management Incidents Pattern Mining and Prediction

城市化进程的快速发展给城市管理带来了巨大的挑战,城市问题事件的管理和预警已成为城市可持续发展的一个重要组成部分。多变量时间序列分析预测模型RBTA可以挖掘城市管理事件发展的基本趋势、周期性、异常事件以及不同城市管理事件之间的耦合关系。使用来自于上海市徐汇区网格中心的真实城市管理事件数据及对模型进行评估,该模型的平均拟合均方根误差为0.12,平均预测均方根误差为0.15,预测准确率比现有方法中最好的方法高4.9%。

The rapid development of urbanization has brought great challenges to the management of cities.The management and early warning of urban incidents have become an important part of urban sustainable development.This paper proposes RBTA,a multivariate timeseries model,to find the patterns including basic trend,seasonality,irregular components and relationship among different incidents.We evaluate our model on the real dataset from the downtown area of Shanghai,one of the biggest metropolitan of the world.The average forecasting root mean squared error(RMSE)is0.15,which decreases4.9%comparing to the best one of the existing methods.