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An ontology-based approach for managing spatio-temporal linearly referenced road event data

机译:基于本体的时空线性参考道路事件数据管理方法

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摘要

Transportation organisations, agencies, professionals, and officials have embraced GIS-T (Geographic Information System for Transportation) as an essential technology in planning, decision making, and maintaining transportation systems. GIS-T linear referencing (LR) is a popular tool that transforms linearly referenced road event data on the road network. Diverse transportation applications need to store and share spatio-temporal linearly referenced data. For this purpose, this paper presents an OWL (Web Ontology Language) ontology based spatio-temporal LR model to manage spatio-temporal linearly referenced data. The data stored in ontology is machine-processable and can be reasoned, shared, and reused by intelligent agents, databases, and applications. Also, this research presents an appropriate method for efficiently preserving and retrieving spatio-temporal linear road segments (linear events). The method does not need to store the entire segments for each of the old time stamps but to contain only the changed portions of segments in the OWL ontology. A recursive procedure is developed to retrieve all segments for every old time stamp.
机译:运输组织,机构,专业人员和官员已经将GIS-T(运输地理信息系统)作为规划,决策和维护运输系统的基本技术。 GIS-T线性参考(LR)是一种流行的工具,可转换道路网络上线性参考的道路事件数据。多样化的运输应用程序需要存储和共享时空线性参考数据。为此,本文提出了一种基于OWL(Web本体语言)本体的时空LR模型,以管理时空线性参考数据。本体中存储的数据是机器可处理的,并且可以由智能代理,数据库和应用程序进行推理,共享和重用。此外,这项研究提出了一种有效地保存和检索时空线性路段(线性事件)的适当方法。该方法不需要为每个旧时间戳存储整个片段,而只包含OWL本体中片段的已更改部分。开发了一个递归过程来为每个旧时间戳检索所有段。

著录项

  • 来源
    《Road & Transport Research》 |2012年第4期|38-49|共12页
  • 作者单位

    Department of Geoinformatics Engineering, Inha University, Incheon, South Korea;

    Department of Geoinformatics Engineering, Inha University, Incheon, South Korea Faculty of Geodesy and Geomatics Eng, K.N.Toosi University of Technology, Tehran,Iran;

    Department of Geoinformatics Engineering, Inha University, Incheon, South Korea;

  • 收录信息 美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-18 00:18:42

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