ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2016, Vol. 53 ›› Issue (12): 2681-2693.doi: 10.7544/issn1000-1239.2016.20160610

• 其他应用技术 • 上一篇    下一篇

基于车辆轨迹大数据的道路网更新方法研究

杨伟,艾廷华   

  1. (武汉大学资源与环境科学学院 武汉 430079) (ywgismap@whu.edu.cn)
  • 出版日期: 2016-12-01
  • 基金资助: 
    国家自然科学基金项目(41531180);国家“八六三”高技术研究发展计划基金项目(2015AA1239012)

A Method for Road Network Updating Based on Vehicle Trajectory Big Data

Yang Wei, Ai Tinghua   

  1. (School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079)
  • Online: 2016-12-01

摘要: 众源轨迹的泛在、实时特性,使其成为道路信息快速获取与更新的重要途径.针对矢量道路数据的变化检测与更新问题,提出了一种基于车辆轨迹大数据的道路网快速变化发现与更新方法.1)以道路弧段为基本单元构建缓冲区,根据道路变化信息类型及表现形式,运用轨迹运动几何信息(方向、转角)与交通语义信息(速度、流量),对道路变化信息进行检测、分类,确定道路变化类型;2)将道路变化类型推断与增量信息提取相结合,分别运用Delaunay三角网、交通流时间序列分析提取增量信息;3)根据变化类型进行增量信息融合.运用深圳市出租车GPS轨迹数据进行实验分析,结果表明:该方法相比常规方法能正确判断道路变化类型、区分真实变化与语义变化,增量信息精度提高约18%,且适于图层级的批处理快速更新.

关键词: 轨迹大数据, 变化检测, 道路网更新, 众源, 交通语义信息

Abstract: Vehicle trajectory data becomes an important approach to access and update of road information. However, conventional methods cannot identify road change type and extract change entities quickly using crowdsourcing trajectory data. To solve the problem, this paper propose a new method to use vehicle trajectory big data to detect and update changes rapidly in the road network. Firstly, road change type is identified by detecting and classifying the road change information using trajectory movement geometry information (direction, turn angle) and traffic semantic information(traffic volume, speed). Through analysis of trajectory data, the real physical change and traffic semantic change of road can be distinguished from each other. And then incremental information is extracted by Delaunay triangulation and traffic flow time series analysis. This method combines the road change type identifying and incremental data extraction through taking road segment buffer as basic unit. Finally, incremental information fusion is conducted according to road change type. An experiment using taxi GPS traces data in Shenzhen is verified the validity of the novel method. The experimental results prove that the method can identity road change type, and the accuracy of incremental data is improved about 18% compared with map matching method. Furthermore, the comparison analysis of the road network update results is also carried out to confirm that the method is suitable for layer-level updates.

Key words: trajectory big data, change detection, road network update, crowdsourcing, traffic semantic information

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