基于3GS浮动车的城市路段单车行程时间提取技术研究
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摘要
城市道路交通拥挤成为困扰世界各大城市的主要社会问题之一,严重影响着城市的可持续发展和人们的日常工作与生活。获取实时、准确、大量的动态交通信息是解决城市交通拥挤问题的基础和保证,而路段行程时间作为一项十分重要的交通信息历来受到人们的关注。如何借助现有的3GS浮动车系统采集路段行程时间是本文重点研究的内容。
本文的主要研究工作及创新之处体现在:根据GPS/GIS/GPRS浮动车系统的技术特点,对城市路网中不同等级道路的划分规则与联通关系进行了深入细致的研究,并从GIS矢量地图数据库组织的角度,设计了合理的路网数据组织与查找结构;根据GPS/GIS浮动车组合定位的特点,结合前人优秀的科研成果,设计了沿道路方向纵向地图匹配(Map Matching)的算法模型,为GPS/GIS组合定位环境下,沿道路方向的定位数据修正提供了新的思路;根据路段划分中所提出的特征点标定方法,设计了基于动态实节点查找的路段行程时间提取技术,并对其中一些关键的技术环节,如实节点时刻提取算法、浮动车运行方向判别技术、下游实节点查找技术等底层技术细节进行了详细的说明与讨论,同时,还根据GPS定位数据的特性,说明了数据中断的情况与处理方法;最后,本文结合GPS定位数据的采样周期,GPRS通信网络的数据传输周期,以及行程时间数据的分析周期,从数据精度需求、数据发送与存储成本与数据质量保证等方面,分析了行程时间数据采集时各个相关数据周期的确定原则。
论文所讨论的内容都是从大处着眼,小处着手进行的分析与论述,根据3GS浮动车的单元独立性,研究了利用每一辆浮动车进行路段行程时间提取的方法与技术细节,只要保证单车行程时间采集方法的成功实现,通过服务器/终端的结构框架,就可以应用到路网上同类的所有浮动车当中,为未来3GS浮动车的大规模使用奠定必要的技术基础。
Travel-time of sections, as one of the most important traffic parameter data, has been highly valued by traffic management and control departments. So, how to dynamic, cheapness and efficient get the travel-time of every section using GPS/GIS/GPRS(3GS for short) integrated probe vehicle becomes a very important concern in modern urban city of traffic crowed management.
In order to get travel-time using concept, first, we need clearly know the exactly borderline positions of section which would be reserarched, after that, could collect travel-time parameter data bases vehicle’s in-time from the entrance and out-time after the exit. In area of urban city, it is a complex work to fix on borderline positions for sections of all kinds of roads. So, founding an abstractive sections-of model is the foundation and gurantee in Large-scale travel-time collection. Further, it also need use the longitiude, latitude coordinates and UTC information which got by the GPS receiver and combining 3GS probe vehicle’s system functions, and development effective travel-time collection methodology, to make sure travel-time collection’s success in large area urban road network.
This paper describes a new and systemic method for sections travel-time collection by using 3GS probe vehicle. It solves sections-of for urban road network using virtual-node and real-node firstly, including related data organizing rules in GIS vector map which making sure the frame could be operated easily. With the demand of travel-time collection, also including GPS data quality and GIS map characters, this paper researches and improves map matching algorithm to improve GPS data accuracy. Based on the virtual-nodes and real-nodes, and improved GPS data(MMGPS), according to the concept of travel-time which defined by Traffic Engineering, the author devises time calculating method in real-nodes for GPS points, further more, also devises direction distinguishing method and real-node selecting method, to make sure travel-time dynamic and continous collection by 3GS probe vehicle in urban road networks. Sample periods of GPS, transmit periods of GPRS, and analysis periods for travel-time composes the last research concerns of this paper.
By using real GPS data and GIS vector map, verifying the technology based on 3GS probe vehicle for sections travel-time collection is right. Because the 3GS probe vehicle could work individually, if one vehicle could make true travel-time collection, it also make all of them do as itself.
本文的主要研究工作及创新之处体现在:根据GPS/GIS/GPRS浮动车系统的技术特点,对城市路网中不同等级道路的划分规则与联通关系进行了深入细致的研究,并从GIS矢量地图数据库组织的角度,设计了合理的路网数据组织与查找结构;根据GPS/GIS浮动车组合定位的特点,结合前人优秀的科研成果,设计了沿道路方向纵向地图匹配(Map Matching)的算法模型,为GPS/GIS组合定位环境下,沿道路方向的定位数据修正提供了新的思路;根据路段划分中所提出的特征点标定方法,设计了基于动态实节点查找的路段行程时间提取技术,并对其中一些关键的技术环节,如实节点时刻提取算法、浮动车运行方向判别技术、下游实节点查找技术等底层技术细节进行了详细的说明与讨论,同时,还根据GPS定位数据的特性,说明了数据中断的情况与处理方法;最后,本文结合GPS定位数据的采样周期,GPRS通信网络的数据传输周期,以及行程时间数据的分析周期,从数据精度需求、数据发送与存储成本与数据质量保证等方面,分析了行程时间数据采集时各个相关数据周期的确定原则。
论文所讨论的内容都是从大处着眼,小处着手进行的分析与论述,根据3GS浮动车的单元独立性,研究了利用每一辆浮动车进行路段行程时间提取的方法与技术细节,只要保证单车行程时间采集方法的成功实现,通过服务器/终端的结构框架,就可以应用到路网上同类的所有浮动车当中,为未来3GS浮动车的大规模使用奠定必要的技术基础。
Travel-time of sections, as one of the most important traffic parameter data, has been highly valued by traffic management and control departments. So, how to dynamic, cheapness and efficient get the travel-time of every section using GPS/GIS/GPRS(3GS for short) integrated probe vehicle becomes a very important concern in modern urban city of traffic crowed management.
In order to get travel-time using concept, first, we need clearly know the exactly borderline positions of section which would be reserarched, after that, could collect travel-time parameter data bases vehicle’s in-time from the entrance and out-time after the exit. In area of urban city, it is a complex work to fix on borderline positions for sections of all kinds of roads. So, founding an abstractive sections-of model is the foundation and gurantee in Large-scale travel-time collection. Further, it also need use the longitiude, latitude coordinates and UTC information which got by the GPS receiver and combining 3GS probe vehicle’s system functions, and development effective travel-time collection methodology, to make sure travel-time collection’s success in large area urban road network.
This paper describes a new and systemic method for sections travel-time collection by using 3GS probe vehicle. It solves sections-of for urban road network using virtual-node and real-node firstly, including related data organizing rules in GIS vector map which making sure the frame could be operated easily. With the demand of travel-time collection, also including GPS data quality and GIS map characters, this paper researches and improves map matching algorithm to improve GPS data accuracy. Based on the virtual-nodes and real-nodes, and improved GPS data(MMGPS), according to the concept of travel-time which defined by Traffic Engineering, the author devises time calculating method in real-nodes for GPS points, further more, also devises direction distinguishing method and real-node selecting method, to make sure travel-time dynamic and continous collection by 3GS probe vehicle in urban road networks. Sample periods of GPS, transmit periods of GPRS, and analysis periods for travel-time composes the last research concerns of this paper.
By using real GPS data and GIS vector map, verifying the technology based on 3GS probe vehicle for sections travel-time collection is right. Because the 3GS probe vehicle could work individually, if one vehicle could make true travel-time collection, it also make all of them do as itself.
引文
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[2] 数 字 解 读 北 京 交 通 现 状 为 北 京 奥 运 畅 通 献 计[EB/OL].http://sports.tom.com/2007-03-22/0CQF/22081176.html.
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[9] Boyce D, Kirson A, Schofer J. Design and implementation of ADVANCE[R]. IEEE Proceeding of 3rd International Conference on Vehicle Navigation and Information Systems. Ottawa,1993:415-426
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[12] 曾松, 杨晓光. 行程时间数据质量与接收水平关系分析[J]. 中国公路学报. 2002 年10 月 第 15 卷 第 4 期
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[15] 王炜. 城市交通规划[M].人民交通出版社.2002 122
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[23] NRC, National Research Council, 2002, Collecting, processing and integrating GPS data into GIS[C]. NCHRP Synthesis 301.National Academy Press; Washington D.C.
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[26] 王国江. 交通矢量地图自动生成平台的研究与发展[D]. 中国科学技术大学(硕士论文) 2003.05
[27] Xiaoguo ZHANG, Qing WANG, Dejun WAN. The Relationship Among Vehicle Positioning Performance, Map Quality, And Sensitivities And Feasibilities of Map-Matching Algorithms[J]. IEEE 2003 468～473
[28] David Bernstein., Alain Kornhauster., Princeton University, August 1996, An introduction to map matching for personal navigation assistants[EB/OL]. http://www.njtide.org/reports/mapmatchingtro.pdf. Accessed Oct 25, 2007
[29] Mohammed A. Quddus. High Integrity Map Matching Algorithms for Advanced Transport Telematics Applications[D]. PhD, Centre for Transport Studies, Department of Civil and Environmental Engeering Imperial College London, United Kingdom. Jan 2006
[30] Wuk Kim, Gyu-In Jee, JangGyu Lee. Efficient use of digital road map in various positioning for ITS[J]. Position Location and Navigation Symposium, IEEE 2000
[31] Mohammed A.Quddus, Robert B.Noland, Washington Y.Ochieng. A High Accuracy Fuzzy Logic Based Map Matching Algorithm for Road Transport[J]. Journal of Intelligent Transportation Systems,10(3):103-115,2006
[32] George Taylor, Geoffery Blewitt. Virtual Differential GPS & Road Reduction Filtering by Map Matching[J]. ION GPS’99,14-17 September 1999,Nashville,TN
[33] 苏杰,周东方,岳春生. GPS 车辆导航中的实时地图匹配算法[J]. 测绘学报.2001.08 第30 卷,第 3 期 253~256
[34] George Taylor, Chris Brunsdon, Jing Li, Andrew Olden, Dorte Steup, Marylin Winter. GPS accuracy estimation using map matching techniques:Applied to vehicle positioning and odometer calibration. Computers, Environment and Urban Systems 30(2006) 757-772
[35] Mohammed A.Quddus, Robert B.Noland, Washington Y. Ochieng. Current map-matching algorithms for transport applications: State-of-the art and future research directions. Transportation Research Part C (2007), doi:10.1016/j.trc.2007.05.002
[36] 鲍远律 刘振安. 卫星定位、交通监控与数字地图[M]. 国防工业出版社. 2006.08
[37] 姜桂艳. 道路交通状态判别技术与应用[M]. 人民交通出版社. 2002
[38] 张宏, 温永宁, 刘爱利. 地理信息系统算法基础[M]. 科学出版社. 2006
[39] Cesar A. Quiroga, Darcy Bullock. Travel time studies with global positioning and geographic information systems: an integrated methodology. Transportation Research Part C 6 (1998) 101~127
[40] 陈思. 基于 GPS/GIS 浮动车技术的旅行时间预测模型[D]. 北京交通大学(硕士论文)2006.12
[41] 吴卉, 盛志才, 喻全, 刘允才. GIS/GPS 城市交通流监控中的地图匹配算法[J]. 计算机工程. 2006.04 第 32 卷, 第 7 期 237～239
[42] Ken’ichiiro,Yamamane. Study on Probe Data Interval and the Performance [EB/OL].http://www.citylogistics.org/CityLogistics2005/Presentation/Yamamane.pdf.
[43] 杨宏业, 张跃. GPS 定位数据压缩算法的设计与实现[J]. 电子技术应用. 2002. (12):29~32
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[2] 数 字 解 读 北 京 交 通 现 状 为 北 京 奥 运 畅 通 献 计[EB/OL].http://sports.tom.com/2007-03-22/0CQF/22081176.html.
[3]2004 上海城市综合交通发展报告(摘要一). 上海城市综合交通规划研究所. 专稿JIAOTONG YU YUNSHU 2004.05 pp:4~7
[4] Turner S.M., Eisele W.L., Benz R.J. et al. Travel Time Data Collection Handbook [P], FHWA-PL-98-035, Texas Transportation Institute. 1998, chapter four.
[5] 2007 年中国GPS导航市场分析及投资咨询报告[M]. 中国投资咨询网. 2007.01 http://www.ocn.com.cn/reports/2006224GPSdaohang.htm
[6] 发达国家电子地图发展一览[J]. 中国测绘. 2007
[7] 导 航 电 子 地 图 产 业 结 构 及 其 行 业 发 展 趋 势 ( 二 ) [EB/OL]. http://www.enet.com.cn/article/2008/0121/A20080121122702.shtml
[8] 张存保, 杨晓光, 严新平. 基于浮动车的交通信息采集系统研究[J]. 交通与计算机. 2006 第 5 期 第 24 卷 总第 132 期, 31～34
[9] Boyce D, Kirson A, Schofer J. Design and implementation of ADVANCE[R]. IEEE Proceeding of 3rd International Conference on Vehicle Navigation and Information Systems. Ottawa,1993:415-426
[10] 陈思. 基于 GPS/GIS 浮动车技术的旅行时间预测模型[D]. 北京交通大学. 2006 年12 月
[11] Geospatial Positioning Accuracy Standards Part 3:National Standard for Spatial Data Accuracy. Subcommittee for Base Cartographic Data Federal Geographic Data Committee[J]. FGDC-STD-007.3-1998
[12] 曾松, 杨晓光. 行程时间数据质量与接收水平关系分析[J]. 中国公路学报. 2002 年10 月 第 15 卷 第 4 期
[13] 徐俊明. 图论及其应用(第 2 版)[M]. 中国科学技术大学出版社. 2004
[14] 杨兆升. 城市交通流诱导系统理论与模型[M]. 人民交通出版社. 2000
[15] 王炜. 城市交通规划[M].人民交通出版社.2002 122
[16] 冮龙晖. 城市道路交通状态判别及拥挤扩散范围估计方法研究[D]. 长春:吉林大学, 2007
[17] 张和生, 张毅, 胡东成, 王明军. 区域交通状态分析的时空分层模型[J]. 清华大学学报(自然科学版) 2007 年 第 47 卷 第 1 期. 157～160
[18] 郑祖舵 动态路径优化关键技术研究[D]. 长春:吉林大学, 2005
[19] 张首信 GPS 卫星测量定位理论与应用[M]. 长沙:国防科技大学.1996.07
[20] 谢世杰,刘豪杰 GPS 误差分析[J]. 测绘工程. 第 8 卷 第 3 期 1999 年.
[21] 隋心,徐爱功. 取消 SA 后 GPS 在城市车辆定位导航中性能分析[J].辽宁工程技术大学学报.第 24卷 增刊 1 期 2005 年 4 月.
[22] 杜歆,李宏东,顾伟康 一种提到差分 GPS 基准站定位精度的新方法[J].浙江大学学报(理学版). 第 29 卷 第 6 期 2002年 11 月.
[23] NRC, National Research Council, 2002, Collecting, processing and integrating GPS data into GIS[C]. NCHRP Synthesis 301.National Academy Press; Washington D.C.
[24] 赵亦林. 车辆定位与导航系统[M] 北京:电子工业出版社 1998
[25] 廖克. 现代地图学的最新进展与新世纪的展望[J]. 测绘科学. 第 29 卷 第 1 期 2004年 2 月
[26] 王国江. 交通矢量地图自动生成平台的研究与发展[D]. 中国科学技术大学(硕士论文) 2003.05
[27] Xiaoguo ZHANG, Qing WANG, Dejun WAN. The Relationship Among Vehicle Positioning Performance, Map Quality, And Sensitivities And Feasibilities of Map-Matching Algorithms[J]. IEEE 2003 468～473
[28] David Bernstein., Alain Kornhauster., Princeton University, August 1996, An introduction to map matching for personal navigation assistants[EB/OL]. http://www.njtide.org/reports/mapmatchingtro.pdf. Accessed Oct 25, 2007
[29] Mohammed A. Quddus. High Integrity Map Matching Algorithms for Advanced Transport Telematics Applications[D]. PhD, Centre for Transport Studies, Department of Civil and Environmental Engeering Imperial College London, United Kingdom. Jan 2006
[30] Wuk Kim, Gyu-In Jee, JangGyu Lee. Efficient use of digital road map in various positioning for ITS[J]. Position Location and Navigation Symposium, IEEE 2000
[31] Mohammed A.Quddus, Robert B.Noland, Washington Y.Ochieng. A High Accuracy Fuzzy Logic Based Map Matching Algorithm for Road Transport[J]. Journal of Intelligent Transportation Systems,10(3):103-115,2006
[32] George Taylor, Geoffery Blewitt. Virtual Differential GPS & Road Reduction Filtering by Map Matching[J]. ION GPS’99,14-17 September 1999,Nashville,TN
[33] 苏杰,周东方,岳春生. GPS 车辆导航中的实时地图匹配算法[J]. 测绘学报.2001.08 第30 卷,第 3 期 253~256
[34] George Taylor, Chris Brunsdon, Jing Li, Andrew Olden, Dorte Steup, Marylin Winter. GPS accuracy estimation using map matching techniques:Applied to vehicle positioning and odometer calibration. Computers, Environment and Urban Systems 30(2006) 757-772
[35] Mohammed A.Quddus, Robert B.Noland, Washington Y. Ochieng. Current map-matching algorithms for transport applications: State-of-the art and future research directions. Transportation Research Part C (2007), doi:10.1016/j.trc.2007.05.002
[36] 鲍远律 刘振安. 卫星定位、交通监控与数字地图[M]. 国防工业出版社. 2006.08
[37] 姜桂艳. 道路交通状态判别技术与应用[M]. 人民交通出版社. 2002
[38] 张宏, 温永宁, 刘爱利. 地理信息系统算法基础[M]. 科学出版社. 2006
[39] Cesar A. Quiroga, Darcy Bullock. Travel time studies with global positioning and geographic information systems: an integrated methodology. Transportation Research Part C 6 (1998) 101~127
[40] 陈思. 基于 GPS/GIS 浮动车技术的旅行时间预测模型[D]. 北京交通大学(硕士论文)2006.12
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