交通运输系统工程与信息 ›› 2014, Vol. 14 ›› Issue (2): 74-79.

• 智能交通系统与信息技术 • 上一篇    下一篇

基于浮动车数据的隧道路段在线地图匹配方法

何兆成*,褚俊飞,庄立坚,叶伟佳   

  1. 中山大学工学院智能交通研究中心,广州510006
  • 收稿日期:2013-06-07 修回日期:2013-11-29 出版日期:2014-04-25 发布日期:2014-07-07
  • 作者简介:何兆成(1977-),男,广东梅州人,副教授,博士.

An On-line Map-matching Method for Tunnel Section Based on Floating Car Data

HE Zhao-cheng, CHU Jun-fei, ZHUANG Li-jian, YE Wei-jia   

  1. Research Center of Intelligent Transportation System, School of engineering, Sun Yat-Sen University, Guangzhou510006, China
  • Received:2013-06-07 Revised:2013-11-29 Online:2014-04-25 Published:2014-07-07

摘要:

修建城市隧道越来越成为城市实现交通分流、解决交通拥堵的重要途径,隧道 路段车辆信息的采集对于城市交通信息的发布具有重要意义.本文针对城市隧道路段,提 出了基于浮动车数据的在线地图匹配方法,引入道路时空可达性和基于模糊逻辑的隧道 路段车辆行为模式权重信息,分析了车辆在隧道路段及辅道上行驶时其行为模式的差异 性,建立了隧道路段地图匹配流程框架,通过时间序列有向图的构建,给出了连接权重的 计算方法.以广州市实地调查数据对方法进行效用评价.实验结果表明,该方法匹配精度 超过90%,可以用于城市隧道路段的交通信息处理.

关键词: 智能交通, 地图匹配, 隧道路段, 浮动车数据, 模糊逻辑

Abstract:

As an important way to achieve traffic diversion and to solve traffic congestion, urban tunnel has become more and more popular in the city. Collecting information about the vehicles on the tunnel section has a significant effect on releasing traffic information of the whole city. Aiming at the urban tunnel section, an on-line map-matching method is proposed based on floating car data(FCD). It introduces the information that road space- time accessibility and behavior pattern weight of vehicle on the tunnel section, which is based on fuzzy logic. What is more, the method analyzes the differences between behavior pattern of vehicle on the tunnel section and on the surrounding auxiliary road, and establishes a flow chart of matching framework for tunnel section. Finally, a calculation method of connection weight is given after the construction of a time series directed graph. Actual survey data from Guangzhou city is adopted to evaluate the performance of the method, and the experimental results show the matching accuracy is more than90%, which can be used for traffic information processing on the urban tunnel section.

Key words: intelligent transportation, map-matching, tunnel section, floating car data, fuzzy logic

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