基于浮动点车辆数据挖掘的动态路况研究
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摘要
有关基于浮动点车辆数据的动态交通实时路况信息的研究在我国刚刚起步,在最近两年里,在我国导航仪市场上相继出现了附有动态路况的导航产品,但一些产品存在路况信息不准确、路况信息不能及时更新及信息覆盖率低等问题。本文主要针对上述问题,基于浮动点交通数据,建立路况分析判断模型,实现动态实时更新及为深圳市车主出行提供动态路况信息服务的应用。
本课题研究的目的在于解决传统基于浮动点判断交通路况信息准确性的提高及降低对浮动点数据量要求。实时读取浮动车辆上传的GPS数据,分析车辆所处路段路况信息,再将判断后的路况信息存入数据库,方便查询及路况信息的及时发布。
此外,为充分提高单个车辆提供路况信息的利用率,在车载终端安装路况分析仪(Mobile Navigation Assistance,MNA),车辆在路面上一旦发生拥堵,主动上报拥堵信息。从而实现分布式计算,不但减轻了中心系统的计算压力,而且还降低了判断动态交通路况信息对于数据量的要求。最后融合两种判断信息的数据,以网站形式发布路况信息。本文的研究内容主要包括以下几个方面:
首先,为克服大规模浮动点数据定位时间长的现象,我们将地图网格化,经纬度数据首先定位网格,再定位网格内的路段,实现了将数据点经纬度定位时间的指数级降低。
其次,对大规模浮动点车辆数据的过滤,排序,确定模型计算。
再次,通过对单一车辆数据的采集,分析特征,建立判断路况模型,再实际验证模型,通过反复验证修改模型,最后到确定模型,再到模型算法向车台移植,车台与中心实现实时通讯。
最后,融合各种路况判断信息,建立优先级标准,将路况信息存入数据库,利用网站实时读取数据库数据,实现动态路况信息的实时发布。
本文通过真实路况检测及模拟拥堵状态,验证了两种判断路况模型的正确率及实时性;突破了传统集中式计算,实现了车载终端GPS模块实时探测路况信息的应用。
The research about vehicle data based on floating point in real-time traffic information has just started in China in the last two years, navigation products with dynamic real-time traffic information have appeared on the market w in China, but some products with dynamic real-time traffic information is not accurate , and traffic information didn’t timely update and the information on a low coverage. In response to solve the issues above, we build traffic analysis model based on the floating car datas and achieve dynamic real-time traffic information update and provide owners for dynamic real-time traffic information service at Shenzhen.
The purpose of this research project is to increase the accuracy of traditional judge road traffic information based on floating-point and reduce the requriements for amount of data. Real-timely read the GPS data uploaded by vehicles, analysis road traffic information , then put the traffic information into a database for convenient inquiries and timely release.
In addition, in order to make full use of single vehicle for provide road traffic information, we install traffic analyzer (Mobile Navigation Assistance, MNA) in vehiche terminal, once there is a event of congestion, congestion information was reported initiativly so achieved distributed computing. There is not only reduce the center system compute pressure, but also reduces the amount of data requirements ,the data which for judge road traffic information. In finally integration of two kinds of information and release it in Web site form. This paper mainly include the following aspects:
Firstly, in order to overcome the phenomenon that the large-scale floating point data spent a lot of time to local position, we divide the map into grid. First,local longitude and latitude data into grid, then locate the road section within the grid, so reduce the local time for latitude and longitude at exponential level.
Secondly, large-scale floating-point vehicle data filter, sort and determine the model calculations.
Once again, by a single vehicle data collection, analysis features, the establishment of traffic models to determine the road traffic conditon, and then the actual verification model changes through repeated verification model, and finally to determine the model, then model algorithm transplanted to vehicle, realize vehicle platform real-time communication with the central.
Finally, integration variety of traffic information and establish the priority standards, put traffic information into the database, using the Web site to read real-time database data, realiz dynamic traffic information broadcast in real time.
In this paper, the real state of traffic congestion detection and simulation to verify the two kinds of traffic models to determine the correct rate and real-time; break the traditional centralized computing, achieve applicaton for vehicle terminal real-time detection traffic information with GPS module.
本课题研究的目的在于解决传统基于浮动点判断交通路况信息准确性的提高及降低对浮动点数据量要求。实时读取浮动车辆上传的GPS数据,分析车辆所处路段路况信息,再将判断后的路况信息存入数据库,方便查询及路况信息的及时发布。
此外,为充分提高单个车辆提供路况信息的利用率,在车载终端安装路况分析仪(Mobile Navigation Assistance,MNA),车辆在路面上一旦发生拥堵,主动上报拥堵信息。从而实现分布式计算,不但减轻了中心系统的计算压力,而且还降低了判断动态交通路况信息对于数据量的要求。最后融合两种判断信息的数据,以网站形式发布路况信息。本文的研究内容主要包括以下几个方面:
首先,为克服大规模浮动点数据定位时间长的现象,我们将地图网格化,经纬度数据首先定位网格,再定位网格内的路段,实现了将数据点经纬度定位时间的指数级降低。
其次,对大规模浮动点车辆数据的过滤,排序,确定模型计算。
再次,通过对单一车辆数据的采集,分析特征,建立判断路况模型,再实际验证模型,通过反复验证修改模型,最后到确定模型,再到模型算法向车台移植,车台与中心实现实时通讯。
最后,融合各种路况判断信息,建立优先级标准,将路况信息存入数据库,利用网站实时读取数据库数据,实现动态路况信息的实时发布。
本文通过真实路况检测及模拟拥堵状态,验证了两种判断路况模型的正确率及实时性;突破了传统集中式计算,实现了车载终端GPS模块实时探测路况信息的应用。
The research about vehicle data based on floating point in real-time traffic information has just started in China in the last two years, navigation products with dynamic real-time traffic information have appeared on the market w in China, but some products with dynamic real-time traffic information is not accurate , and traffic information didn’t timely update and the information on a low coverage. In response to solve the issues above, we build traffic analysis model based on the floating car datas and achieve dynamic real-time traffic information update and provide owners for dynamic real-time traffic information service at Shenzhen.
The purpose of this research project is to increase the accuracy of traditional judge road traffic information based on floating-point and reduce the requriements for amount of data. Real-timely read the GPS data uploaded by vehicles, analysis road traffic information , then put the traffic information into a database for convenient inquiries and timely release.
In addition, in order to make full use of single vehicle for provide road traffic information, we install traffic analyzer (Mobile Navigation Assistance, MNA) in vehiche terminal, once there is a event of congestion, congestion information was reported initiativly so achieved distributed computing. There is not only reduce the center system compute pressure, but also reduces the amount of data requirements ,the data which for judge road traffic information. In finally integration of two kinds of information and release it in Web site form. This paper mainly include the following aspects:
Firstly, in order to overcome the phenomenon that the large-scale floating point data spent a lot of time to local position, we divide the map into grid. First,local longitude and latitude data into grid, then locate the road section within the grid, so reduce the local time for latitude and longitude at exponential level.
Secondly, large-scale floating-point vehicle data filter, sort and determine the model calculations.
Once again, by a single vehicle data collection, analysis features, the establishment of traffic models to determine the road traffic conditon, and then the actual verification model changes through repeated verification model, and finally to determine the model, then model algorithm transplanted to vehicle, realize vehicle platform real-time communication with the central.
Finally, integration variety of traffic information and establish the priority standards, put traffic information into the database, using the Web site to read real-time database data, realiz dynamic traffic information broadcast in real time.
In this paper, the real state of traffic congestion detection and simulation to verify the two kinds of traffic models to determine the correct rate and real-time; break the traditional centralized computing, achieve applicaton for vehicle terminal real-time detection traffic information with GPS module.
引文
1曹晓飞.城市道路交通拥堵评价方法及其应用.北京交通大学工程硕士学位论文. 2008:11~16
2易汉文.美国2005年城市机动性报告摘要.城市交通. 2006:92117
3姜华平.道路交通事故社会经济损失评价理论研究.吉林大学博士论文. 2005:14~18
4 D. Ulrich Fastenrath. Floating Car Data On a Larger Scale. DDG Gesellschaft für Verkehrsdaten mbH. 2002: 40547
5季常煦,梁谦.城市交通信息服务系统的应用.中国科技息. 2006:50
6 S. Eichler. Anonymous And Authenticated Data Provisioning For Floating Car Systems. IEEE. 2006:4244~0411
7唐克双,姚恩建.日本ITS开发和运用的实例—名古屋基于浮动车信息的P- DRGS简介.城市交通. 2006,4(3) :1672~5328
8 D. Leuw. ADVANCE. Illinois Universities Transportation Research Consortium American Automobile Association. 1996:2-1~2-8
9 http://www.tranbbs.com/news/cnnews/ITS/news_44540.shtml
10杜勇,陈智宏,汪祖云等.北京动态交通服务系统建设.城市通. 2008,6(2):42~45
11郭继孚,温慧敏,高永.北京市动态交通信息服务系统建设及制约因素.城市交通. 2008,6(2):12~17
12王明辉,浮动车化解北京拥堵难题,城市智能交通. 2006:132~135
13孙棣华,董均宇,廖孝勇.基于GPS探测车的道路交通状态估计技术.计算机应用研究. 2007:243~247
14 S. Lorkowski. P. Mieth. New ITS applications for metropolitan areas based on Floating Car Data. Institute of Transport Research German Aerospace Center. 2003
15喻泉.基于浮动车的城市路网动态交通流的建模与分析.上海交通大学硕士学位论文. 2006:12~15
16 F. Yeming, L. Chun. Computation and Matching of Road Segment Velocity with Floating Car Data and Its Web Publication Based on Spatial Road Network. Proc. of SPIE Vol. 2006:6418
17张威.基于浮动车技术的城市路况计算方法.交通系统工程与信息. 2007,7(1):43~50
18 F. Zheng, Y. Wan. Speed Estimation on Freeways Using Floating Cars Equipped with GPS Receivers. International Conference on Transportation Engineering. 2007:2936~2942
19万宇.基于GPS定位的浮动车样本数量确定方法研究.交通与安全. 2006:183~186
20李擎,谢四江.一种用于车辆最短路径规划的自适应遗传算法及其与Dijkstra和A*算法的比较.北京科技大学学报. 2006,28(11):1082~1087
21殷伟,郭磷.一种基于FCD的城市道路车流速度估计算法.中国科学技术大学学报. 2008,38(9):1113~1118
22 A. Humblet. An Adaptive Distrivuted Dijkstra Shortest Path Algorithm. LIDS. 1988:1775
23 A. John Sanjeev Kumar. Intelligent Transport Route Planning Using Genetic Algorithms in Path Computation Algorithms. European Journal of Scientific Research. 2009:463~468
24王力,张海,范耀祖.基于探测车技术的路段平均速度估计模型.交通运输系统工程与信息. 2006,6(4):29~44
25 S. He, W. Guan. Link Travel Time Estimation at Signalized Road Segments with Floating Car Data. Traffic and Transportation Studies Congress. 2008:880~890
26 P. Laborczi. Travel Time Estimation Based On Incomplete Probe Car Information. 13th World Congress on ITS. London.2006
27郭继孚,温慧敏,陈峰.浮动车系统功能分析与应用设计研究.交通运输系统工程与信息. 2007,7(3):39~45
28 A. Chen, N. Jain. Scaling Real-Time Telematics Applications using Programmable Middleboxes: A Case Study in Traffic Prediction. IEEE. 2004:388~394
29袁芳,宋星.道路交通信息分析与建模.计算机工程与设计.2008,29(13):3499~3502
30陈艳艳,王东柱.分布式车载导航系统路线优化有约束A*算法.交通与计算机. 2005,6(23):10~14
31 Greenshields B. D. A study of traffic capacity. Proceedings of Highway. Research Board.1935, Vol.14: 448-477
32 B. C. Bermejo. Interfacing the Advanced Traveler Information System With Inductive Loop Detector Technology. Proceedings of the Eastern Asia Society for Tarnsportation Studies. Vol.4,October, 2003:1106~1121
33 B. Kerner, C. Demir, R. G. Herrtwich. Traffic State Detection with Floating Car Data in Road Networks. Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems Vienna, Austria. 2005:700~706
34陈岗. GPS车辆监控系统车载终端软件设计.科技信息. 2007:1~7
35韩舒.基于FCD的道路实时信息精度检验与改善方法研究.同济大学硕士学位论文. 2008:5~10
36中国公路学会编委会.交通工程手册.第一版,人民交通出版社. 1998:238~246
37杨祖元,黄席樾,杜长海等.基于FFCM聚类的城市交通拥堵判别研究.计算机应用研究. 2008,25(9):2768~2771
38 W. Huber. Extended Floating-Car Data For The Acquisition Of Traffic Information.Research Engineer. Munich,Germany. 2008
39 G. Min, Y. Du. Simulation Study of Mixed Traffic in China--a Practice in Beijing. Proceedings of the 11th International IEEE,Beijing,China. 2008:281~286
40 L. Huang, J. Wu. Evaluation of Performance of Beijing Expressway Based on Integrated GPS/GIS Data. IEEE Intelligent Transportation Systems Conference Toronto, Canada. 2006:927~931
41 M. E.Fouladvand, A. H. Darooneh. Statistical analysis of floating-car data: an empirical study. The European Physical Journal B. 2005:319~329
42 F. Gossel, E. Michler, B. Wrase. Spectral analysis of Floating Car Data. Advances in Radio Science. 2003:139~143
43 C. de Fabritiis, R. Ragona, G.Valenti. Traffic Estimation And Prediction Based On Real Time Floating Car Data. Proceedings of the 11th International IEEEConference on Intelligent Transportation Systems Beijing, China. 2008:197~204
2易汉文.美国2005年城市机动性报告摘要.城市交通. 2006:92117
3姜华平.道路交通事故社会经济损失评价理论研究.吉林大学博士论文. 2005:14~18
4 D. Ulrich Fastenrath. Floating Car Data On a Larger Scale. DDG Gesellschaft für Verkehrsdaten mbH. 2002: 40547
5季常煦,梁谦.城市交通信息服务系统的应用.中国科技息. 2006:50
6 S. Eichler. Anonymous And Authenticated Data Provisioning For Floating Car Systems. IEEE. 2006:4244~0411
7唐克双,姚恩建.日本ITS开发和运用的实例—名古屋基于浮动车信息的P- DRGS简介.城市交通. 2006,4(3) :1672~5328
8 D. Leuw. ADVANCE. Illinois Universities Transportation Research Consortium American Automobile Association. 1996:2-1~2-8
9 http://www.tranbbs.com/news/cnnews/ITS/news_44540.shtml
10杜勇,陈智宏,汪祖云等.北京动态交通服务系统建设.城市通. 2008,6(2):42~45
11郭继孚,温慧敏,高永.北京市动态交通信息服务系统建设及制约因素.城市交通. 2008,6(2):12~17
12王明辉,浮动车化解北京拥堵难题,城市智能交通. 2006:132~135
13孙棣华,董均宇,廖孝勇.基于GPS探测车的道路交通状态估计技术.计算机应用研究. 2007:243~247
14 S. Lorkowski. P. Mieth. New ITS applications for metropolitan areas based on Floating Car Data. Institute of Transport Research German Aerospace Center. 2003
15喻泉.基于浮动车的城市路网动态交通流的建模与分析.上海交通大学硕士学位论文. 2006:12~15
16 F. Yeming, L. Chun. Computation and Matching of Road Segment Velocity with Floating Car Data and Its Web Publication Based on Spatial Road Network. Proc. of SPIE Vol. 2006:6418
17张威.基于浮动车技术的城市路况计算方法.交通系统工程与信息. 2007,7(1):43~50
18 F. Zheng, Y. Wan. Speed Estimation on Freeways Using Floating Cars Equipped with GPS Receivers. International Conference on Transportation Engineering. 2007:2936~2942
19万宇.基于GPS定位的浮动车样本数量确定方法研究.交通与安全. 2006:183~186
20李擎,谢四江.一种用于车辆最短路径规划的自适应遗传算法及其与Dijkstra和A*算法的比较.北京科技大学学报. 2006,28(11):1082~1087
21殷伟,郭磷.一种基于FCD的城市道路车流速度估计算法.中国科学技术大学学报. 2008,38(9):1113~1118
22 A. Humblet. An Adaptive Distrivuted Dijkstra Shortest Path Algorithm. LIDS. 1988:1775
23 A. John Sanjeev Kumar. Intelligent Transport Route Planning Using Genetic Algorithms in Path Computation Algorithms. European Journal of Scientific Research. 2009:463~468
24王力,张海,范耀祖.基于探测车技术的路段平均速度估计模型.交通运输系统工程与信息. 2006,6(4):29~44
25 S. He, W. Guan. Link Travel Time Estimation at Signalized Road Segments with Floating Car Data. Traffic and Transportation Studies Congress. 2008:880~890
26 P. Laborczi. Travel Time Estimation Based On Incomplete Probe Car Information. 13th World Congress on ITS. London.2006
27郭继孚,温慧敏,陈峰.浮动车系统功能分析与应用设计研究.交通运输系统工程与信息. 2007,7(3):39~45
28 A. Chen, N. Jain. Scaling Real-Time Telematics Applications using Programmable Middleboxes: A Case Study in Traffic Prediction. IEEE. 2004:388~394
29袁芳,宋星.道路交通信息分析与建模.计算机工程与设计.2008,29(13):3499~3502
30陈艳艳,王东柱.分布式车载导航系统路线优化有约束A*算法.交通与计算机. 2005,6(23):10~14
31 Greenshields B. D. A study of traffic capacity. Proceedings of Highway. Research Board.1935, Vol.14: 448-477
32 B. C. Bermejo. Interfacing the Advanced Traveler Information System With Inductive Loop Detector Technology. Proceedings of the Eastern Asia Society for Tarnsportation Studies. Vol.4,October, 2003:1106~1121
33 B. Kerner, C. Demir, R. G. Herrtwich. Traffic State Detection with Floating Car Data in Road Networks. Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems Vienna, Austria. 2005:700~706
34陈岗. GPS车辆监控系统车载终端软件设计.科技信息. 2007:1~7
35韩舒.基于FCD的道路实时信息精度检验与改善方法研究.同济大学硕士学位论文. 2008:5~10
36中国公路学会编委会.交通工程手册.第一版,人民交通出版社. 1998:238~246
37杨祖元,黄席樾,杜长海等.基于FFCM聚类的城市交通拥堵判别研究.计算机应用研究. 2008,25(9):2768~2771
38 W. Huber. Extended Floating-Car Data For The Acquisition Of Traffic Information.Research Engineer. Munich,Germany. 2008
39 G. Min, Y. Du. Simulation Study of Mixed Traffic in China--a Practice in Beijing. Proceedings of the 11th International IEEE,Beijing,China. 2008:281~286
40 L. Huang, J. Wu. Evaluation of Performance of Beijing Expressway Based on Integrated GPS/GIS Data. IEEE Intelligent Transportation Systems Conference Toronto, Canada. 2006:927~931
41 M. E.Fouladvand, A. H. Darooneh. Statistical analysis of floating-car data: an empirical study. The European Physical Journal B. 2005:319~329
42 F. Gossel, E. Michler, B. Wrase. Spectral analysis of Floating Car Data. Advances in Radio Science. 2003:139~143
43 C. de Fabritiis, R. Ragona, G.Valenti. Traffic Estimation And Prediction Based On Real Time Floating Car Data. Proceedings of the 11th International IEEEConference on Intelligent Transportation Systems Beijing, China. 2008:197~204