上海城市规划Issue(1)：51-56,6.
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
吴俊 1王杰艺 2金耀辉3
作者信息
- 1. 上海信投建设有限公司
- 2. 上海交通大学电子信息与电气工程学院
- 3. 上海交通大学人工智能研究院
- 折叠
摘要
Abstract
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 time-series 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) is 0.15, which decreases 4.9% comparing to the best one of the existing methods.关键词
智慧城市/时间序列/预测Key words
Smart city/Time-series/Forecast分类
建筑与水利引用本文复制引用
吴俊,王杰艺,金耀辉..智慧城市网格管理事件模式挖掘与预测[J].上海城市规划,2018,(1):51-56,6.
