一种改进的快速浮动车地图匹配方法

doi:10.13474/j.cnki.11-2246.2017.0019

测绘通报 ›› 2017, Vol. 0 ›› Issue (1): 87-92.doi: 10.13474/j.cnki.11-2246.2017.0019

一种改进的快速浮动车地图匹配方法

张健钦^1,2, 李明轩^1,2, 段颖超^1,2, 杜明义^1,2

1. 北京建筑大学测绘与城市空间信息学院, 北京 100044;
2. 现代城市测绘国家测绘地理信息局重点实验室, 北京 100044

收稿日期:2016-02-27 出版日期:2017-01-25 发布日期:2017-02-06
作者简介:张健钦(1977-),男,博士,副教授,研究方向为交通GIS、三维GIS及智能交通系统。E-mail:yc.duan@qq.com

An Improved Algorithm for Fast Map-matching of Floating Car

ZHANG Jianqin^1,2, LI Mingxuan^1,2, DUAN Yingchao^1,2, DU Mingyi^1,2

1. School of Surveying and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing 100044, China;
2. Key Laboratory for Urban Geomatics of National Administration of Surveying, Mapping and Geoinformation, Beijing 100044, China

Received:2016-02-27 Online:2017-01-25 Published:2017-02-06

摘要/Abstract

摘要： 浮动车地图匹配算法能够实现浮动车离散点与路段的快速准确匹配，是浮动车路况信息生成技术中的核心环节。本文针对现有方法的不足，实现了建立定位点的有效阈值缓冲区，并依据空间关系检索候选匹配路段，研究实现了一种利用行驶速度、行驶方向、投影距离、行驶距离4个参数进行行车轨迹判别的逻辑匹配算法。试验表明，该方法无需对路网数据进行大量的前期处理工作，简化了候选匹配路段的检索过程，在保证匹配正确率的同时也表现出了更高的效率。

关键词: 路网匹配, 浮动车数据, 路况信息, 路段

Abstract: Floating car map-matching algorithm enables fast and accurate matching between the discrete points of floating car and the road segments. It is the core part of floating car traffic information generation technology. For the deficiencies of the prior method, an efficient logical matching method is proposed and implemented. Firstly it establishes buffer zones with effective threshold for the anchor points, and then searches the road segments that intersect with the buffer zones. After that, it discriminates the driving track by four parameters(driving speed, driving direction, projection distance and driving distance). The experiments show that this method does not require extensive work to deal with road network data. It simplifies the process of searching candidate matching road segments and ensures matching accuracy while also shows a higher efficiency.

Key words: road network matching, floating car data, traffic information, road segments

中图分类号:

P283

张健钦, 李明轩, 段颖超, 杜明义. 一种改进的快速浮动车地图匹配方法[J]. 测绘通报, 2017, 0(1): 87-92.

ZHANG Jianqin, LI Mingxuan, DUAN Yingchao, DU Mingyi. An Improved Algorithm for Fast Map-matching of Floating Car[J]. 测绘通报, 2017, 0(1): 87-92.

参考文献

[1] 邵春福,熊志华,姚智胜.道路网短时交通需求预测理论、方法及应用[M].北京:清华大学出版社,2011.
[2] 王美玲,程林.浮动车地图匹配算法研究[J].测绘学报,2012,1(41),133-138.
[3] 刘培.基于浮动车数据的地图匹配算法研究[D].北京:北京交通大学,2007.
[4] 尹相勇,贾顺平. 基于MapObjects的浮动车中心地图匹配综合算法开发[J]. 计算机工程与应用, 2008, 44(36):881-884.
[5] 诸彤宇,郭胜敏. 浮动车信息处理技术研究[J]. 中国图象图形学报, 2009, 14(7):1230-1237.
[6] 章威, 徐建闽, 林绵峰. 基于大规模浮动车数据的地图匹配算法[J]. 交通运输系统工程与信息, 2007, 7(2):40-45.
[7] QUDDUS M A,NOLAND R B,OCHIENG W Y.A High Accuracy Fuzzy Logic Based Map Matching Algorithm for Road Transport[J]. Journal of Intelligent Transportation Systems, 2006, 10(3):103-115.
[8] NAJJAR M E E,BONNIFAIT P.A Road-matching Method for Precise Vehicle Localization Using Belief Theory and Kalman Filtering[J]. Autonomous Robots, 2005,19(2):173-191.
[9] JABBOUR M, BONNIFAIT P, CHERFAOUI V. Map-matching Integrity Using Multihy Pothesis Road-tracking[J]. Journal of Intelligent Transportation Systems, 2008, 12(4):189-201.
[10] 吕卫锋,吴东东,诸彤宇. 基于向量识别的启发式路径推测算法[J]. 计算机学报,2009,32(7):1443-1450.
[11] 李宇光,李清泉. 利用地图栅格化的海量浮动车数据道路匹配快速算法[J]. 武汉大学学报(信息科学版),2014,39(6):724-728,733.
[12] RAHMANI M,KOUTSOPOULOS H N.Path Inference from Sparse Floating Car Data for Urban Networks[J]. Transportation Research Part C:Emerging Technologies, 2013(30):41-54.
[13] SCHÄFER R P, THIESSENHUSEN K U, BROCKFELD E, et al. A Traffic Information System by Means of Real-time Floating-car Data[C]//ITS World Congress 2002.[S.l.]:ITS World,2002.
[14] CHERITON D R, BECHTOLSHEIM A V. Method for Traffic Management,Traffic Prioritization,Access Control,and Packet Forwarding in a Datagram Computer Network:U.S. Patent 6,091,725[P]. 2000-07-18.
[15] MCCLOGHRIE K,GAI S,MOHABAN S. Method and Apparatus for Identifying Network Data Traffic Flows and for Applying Quality of Service Treatments to the Flows:U.S. Patent 6,286,052[P]. 2001-09-04.
[16] DE FABRITⅡS C,RAGONA R, VALENTI G. Traffic Estimation and Prediction Based on Real Time Floating Car Data[C]//Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems. Beijing:IEEE, 2008:197-203.
[17] LEDUC G. Road Traffic Data:Collection Methods and Applications[J]. Working Papers on Energy, Transport and Climate Change,2008(1):55.
[18] FUSHIKI T, YAMANE K, INOUE T, et al. Method of Presuming Traffic Conditions by Using Floating Car Data and System for Presuming and Presenting Traffic Conditions by Using Floating Data:U.S. Patent 6,721,650[P]. 2004-04-13.
[19] EHMKE J F, MEISEL S, MATTFELD D C. Floating Car Data Based Analysis of Urban Travel Times for the Provision of Traffic Quality[M].New York:Springer, 2010:129-149.
[20] SCHÄFER R P, THIESSENHUSEN K U, BROCKFELD E, et al. A Traffic Information System by Means of Real-time Floating-car Data[C]//ITS World Congress 2002.[S.l.]:ITS World,2002.
[21] YOSHIDA M. Traffic Information System:U.S. Patent 5,699,056[P]. 1997-12-16.

一种改进的快速浮动车地图匹配方法

An Improved Algorithm for Fast Map-matching of Floating Car

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