期刊文献+

基于大规模浮动车数据的城市道路网复杂度分析 被引量:6

Analysis of Urban Road Network Complexity Based on Large-scale Floating Car Data
原文传递
导出
摘要 针对现有浮动车技术应用研究中缺乏道路网络复杂度的定量分析,引入置信点的概念,提出了一种基于大规模浮动车数据的城市路网复杂度分析方法。利用该方法统计了路网中各路段的置信点分布情况和置信点的平均匹配距离,建立了城市路网复杂度模型。结合浮动车数据的预处理,提出了城市路网复杂度的分析流程。以广州市为例,分析了广州市全局路网和4类典型城市道路代表路段的复杂度。对于地图匹配算法实证研究中的指定路径,具体分析了其各组成路段的复杂度及其主要影响因素。 In the light of the fact that the quantitative analysis in the application of floating car technology, the concept of of urban road network complexity is unexplored confidence point (CP) is introduced, and an analytical method of urban road network complexity based on large-scale floating car data is presented. On this basis, the CP distribution of each road section and the average matching distance of CPs are analyzed, and the urban road network complexity model is established. Combining with the floating car data pre- processing, the analysis complexity of the whole process of road network complexity is proposed. Taking Guangzhou for example, Guangzhou urban road network and 4 typical types urban road section are analyzed, and the specific path for map matching empirical study is specifically analyzed to indicate the complexity of each road section on the path and the main influencin~ factors.
出处 《公路交通科技》 CAS CSCD 北大核心 2013年第6期120-126,共7页 Journal of Highway and Transportation Research and Development
基金 广东省粤港关键领域重点突破项目(2011A011305002) 广东省科技计划项目(2012B010900012)
关键词 交通工程 路网复杂度分析 置信点 浮动车数据 地图匹配 traffic engineering road network complexity analysis confidence point floating car data mapmatching
  • 相关文献

参考文献17

  • 1BRAKATSOULAS S, PFOSER D, TRYFONA N. Practical Data Management Techniques for Vehicle Tracking Data [ C] // Proceedings of 21st International Conference on Data Engineering. Tokyo: IEEE, 2005: 324 - 235.
  • 2TURKSMA S. The Various Uses of Floating Car Data [ C ]// Proceedings of 10th International Conference on Road Transport Information and Control. London: IEEE, 2000: 51 -55.
  • 3SCHAEFER R P, THIESSENHUSEN K U, WAGNER P. A Traffic Information System by Means of Real-time Floating-car Data [C]//Proceedings of 9th World Congress on Intelligent Transport Systems. Chicago: ERTICO, 2002.
  • 4SRINIVASAN K K, JOVANIS P P. Determination of Number of Probe Vehicles Required for Reliable Travel Time Measurement in Urban Network [ J ]. Transportation Research Record, 1996, 1537:15-22.
  • 5刘丽娜,吴建平,左兴权,王春露.浮动车最小样本数量确定方法综述[J].公路交通科技,2009(S1):74-78. 被引量:3
  • 6VELAGA N R, QUDDUS M A, BRISTOW A L. Developing an Enhanced Weight-based Topological Map- matching Algorithm for Intelligent Transport Systems [ J ]. Transportation Research Part C: Emerging Technologies, 2009, 17 (6) : 672 -683.
  • 7QUDDUS M A, OCHIENG W Y, NOLAND R B. Integrity of Map-Matching Algorithms [ J ]. Transportation Research Part C: Emerging Technologies, 2006, 14 (4) : 283 - 302.
  • 8WANG Wei, JIN Jing, RAN Bin, et al. Large-scale Freeway Network Traffic Monitoring: A Map-matching Algorithm Based on Low-logging Frequency GPS Probe Data [ J ]. Journal of Intelligent Transportation Systems, 2011, 15 (2) : 63 -74.
  • 9张昊,刘晓鸿,富立.确定性地图匹配算法在车辆导航的应用和推广[J].计算机应用研究,2004,21(6):117-119. 被引量:3
  • 10WATTS D J, STROGATZ S H. Collective Dynamics of " Small-World " Networks [ J ]. Nature, 1998, 393 (6684) : 440 - 442.

二级参考文献36

共引文献17

同被引文献92

引证文献6

二级引证文献15

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部