史潇, 徐家鹏, 杜毅贤, 沈婕, 臧垲岳. 面向道路积水动态可视化的多源数据集成方法[J]. 武汉大学学报 ( 信息科学版), 2022, 47(5): 693-699. DOI: 10.13203/j.whugis20190350
引用本文: 史潇, 徐家鹏, 杜毅贤, 沈婕, 臧垲岳. 面向道路积水动态可视化的多源数据集成方法[J]. 武汉大学学报 ( 信息科学版), 2022, 47(5): 693-699. DOI: 10.13203/j.whugis20190350
SHI Xiao, XU Jiapeng, DU Yixian, SHEN Jie, ZANG Kaiyue. Multi-source Data Integration Method for Dynamic Road Waterlogging Visualization[J]. Geomatics and Information Science of Wuhan University, 2022, 47(5): 693-699. DOI: 10.13203/j.whugis20190350
Citation: SHI Xiao, XU Jiapeng, DU Yixian, SHEN Jie, ZANG Kaiyue. Multi-source Data Integration Method for Dynamic Road Waterlogging Visualization[J]. Geomatics and Information Science of Wuhan University, 2022, 47(5): 693-699. DOI: 10.13203/j.whugis20190350

面向道路积水动态可视化的多源数据集成方法

Multi-source Data Integration Method for Dynamic Road Waterlogging Visualization

  • 摘要: 随着城市化进程的不断加快及暴雨等极端天气的时有发生,道路积水问题愈发严重,影响了市民的出行和城市正常运行,因此有必要对道路积水信息进行动态可视化,而数据的集成与管理是从多源数据到道路积水信息的关键一环。为了更加直接有效地表达道路积水信息,提出了面向城市内涝动态预警可视化的多源时空数据集成与管理方法,构建了道路积水时空数据模型,探讨了积水数据与道路数据的匹配方法,并设计了面向道路积水动态预警的原型系统,实现了道路积水深度的提取与发布。以南京市主城区某区域对该方法进行了实验案例分析,实验结果表明,提出的数据集成与管理方法在进行道路积水可视化时具有一定的可行性。

     

    Abstract:
      Objectives  With the acceleration of urbanization, problems caused by extreme weather such as heavy rain have become increasingly obvious. As one of the most common meteorological disasters, urban floods have become one of the urban diseases in today's big cities. Therefore, it is necessary to explore the methods and technologies of multi-source spatiotemporal data integration to achieve dynamic road waterlogging visualization.
      Methods  Firstly, we designed a spatiotemporal data model of road waterlogging and stored the data in the form of database tables to describe the evolution process of road waterlogging in both time and space. Secondly, a matching method for waterlogging data and road data was put forward to obtain road waterlogging depth. Thirdly, we designed a prototype system for dynamic road waterlogging warnings and achieved the extraction and release of road waterlogging depth. Finally, an experiment was carried out in downtown Nanjing City to verify the availability of the proposed method.
      Results  The results show that the proposed data integration method can meet the multi-scale needs of dynamic road waterlogging visualization. We integrate multi-source spatiotemporal data from the perspective of dynamic road waterlogging visualization to solve the problem of transferring multi-source data into road waterlogging depth.
      Conclusions  In further studies, real-time traffic will be considered to comprehensively express the road condition and waterlogging condition.

     

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