基于数据融合与GIS技术的动态交通信息诱导系统研究与设计
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摘要
智能交通系统是目前公认的解决交通领域内众多问题及提高交通效率的最佳途径。在众多的智能交通管理或应用系统中,交通信息尤其是动态交通信息是最为核心的内容,系统的各种功能都是以信息应用为中心展开的。作为智能交通系统领域的一个研究热点,动态交通信息诱导系统就是以动态交通信息为核心的应用系统。该系统在为交通参与者提供实时、准确而全面的交通信息的基础上,能够为出行者提供最佳出行路径诱导,一方面使得交通管理部门采集的动态交通信息能够实时服务于出行者,另一方面也为相关管理部门制定决策提供了一定依据。动态交通信息诱导系统的研究与开发涉及到的主要技术有：动态交通信息采集技术、数据预处理技术、数据融合技术、最短路径诱导算法及GIS开发技术等。
本论文旨在对动态交通信息诱导系统进行相关研究及设计。论文以动态交通信息的采集、处理及应用为线索,重点研究了动态交通信息的数据融合及最短路径诱导这两项相关技术,对有关算法进行了改进以提高其效率。进而,按照动态交通信息的处理应用流程整合应用了以上两项研究成果,将它们与GIS开发技术相结合,设计并实现了一个动态交通信息诱导系统。
论文主要工作如下：
首先,简单介绍了智能交通系统的研究背景及交通信息诱导系统相关技术的研究现状；在介绍动态交通数据采集及预处理技术的基础上,着重分析了几种常用的交通数据融合技术,结合要处理的实际数据特点及应用目的,利用改进的综合统计分析方法对实际动态交通数据进行了融合处理,通过实际数据的仿真比较,验证了算法的可行性。
其次,在介绍动态交通路径诱导系统中常用最短路径算法思想的基础上,对经典的Dijkstra算法进行了详尽分析,针对其不足,对该算法的网络存储模型及搜索方式分别进行了改进；通过实例仿真,验证了改进后的Dijkstra算法可以提高路径寻优的时间、空间效率,同时也具有一定的实际应用意义。
随后,在对GIS开发技术进行介绍之后,论文从系统开发的角度,设计了动态交通信息诱导系统的开发方案及数据库,将数据融合算法和最短路径诱导算法的研究成果用于实际应用,利用GIS开发技术设计并实现了一个动态交通信息诱导系统。从而实现了理论研究与实际应用相结合,使交通信息诱导系统能够服务于普通出行者,进而提高城市交通效率。
最后,总结了本文所做的研究与设计工作,并对该课题下一步的工作进行了展望。
Intelligent Transportation Systems have been widely recognized as the solution to many problems in the field of transportation. For many traffic management or application systems, traffic information, especially the dynamic traffic information is the core content, which is centric to various features of systems. As the focused research issue in the field of intelligent transportation system, Dynamic Traffic Information System is based on the processing and application of dynamic traffic information. The system can provide real-time, accurate and comprehensive traffic information and the best travel route guidance. Thanks to this system, the dynamic traffic information collected by the traffic management department can serve the public. On the other hand, some references about decision-making could also be provided for the traffic management department. Dynamic Traffic Information System Research and Development The main technologies involved in the research and development of Dynamic Traffic Information System include collection technologies of dynamic traffic information, data preprocessing, data fusion technology, shortest route guidance algorithm and GIS.
This paper seeks to do some research and development of the Dynamic Traffic Information Guidance Systems. Some research in data fusion of dynamic traffic information and dynamic traffic route guidance technology has been done. Some related algorithms are improved to enhance their efficiency. And then, a Dynamic Traffic Information Guidance System has been developed based on GIS techniques.
The main contents of this paper are as follows.
To begin with, a brief introduction about Intelligent Transportation Systems and interrelated technologies including data collection methods and data preprocessing, data fusion technology is made. Then the paper puts the emphasis on the research of some common data fusion technology. After that, this paper develops the comprehensive statistical analysis algorithm to do data fusion for the dynamic traffic information.
Secondly, after introducing some commonly used shortest path algorithms in the Dynamic Traffic Route Guidance system, a detailed analysis the classical Dijkstra algorithm is made. Then the paper improved the network storage model and search method of Dijkstra algorithm. And then, simulation is completed and the results demonstrate that the improved algorithm is highly effective compared with the classical one.
And then, the paper designs the overall framework and data flow for the Dynamic Traffic Information Guidance System from the perspective of system development and design. After that, the data fusion module and shortest route guidance module are integrated with the GIS-platform. As a result, a Dynamic Traffic Information Guidance System has been designed and implemented to serve the general traveler and improve the efficiency of urban transport system.
Finally, the research and development, as well as the following improvement of this paper is summarized.
本论文旨在对动态交通信息诱导系统进行相关研究及设计。论文以动态交通信息的采集、处理及应用为线索,重点研究了动态交通信息的数据融合及最短路径诱导这两项相关技术,对有关算法进行了改进以提高其效率。进而,按照动态交通信息的处理应用流程整合应用了以上两项研究成果,将它们与GIS开发技术相结合,设计并实现了一个动态交通信息诱导系统。
论文主要工作如下：
首先,简单介绍了智能交通系统的研究背景及交通信息诱导系统相关技术的研究现状；在介绍动态交通数据采集及预处理技术的基础上,着重分析了几种常用的交通数据融合技术,结合要处理的实际数据特点及应用目的,利用改进的综合统计分析方法对实际动态交通数据进行了融合处理,通过实际数据的仿真比较,验证了算法的可行性。
其次,在介绍动态交通路径诱导系统中常用最短路径算法思想的基础上,对经典的Dijkstra算法进行了详尽分析,针对其不足,对该算法的网络存储模型及搜索方式分别进行了改进；通过实例仿真,验证了改进后的Dijkstra算法可以提高路径寻优的时间、空间效率,同时也具有一定的实际应用意义。
随后,在对GIS开发技术进行介绍之后,论文从系统开发的角度,设计了动态交通信息诱导系统的开发方案及数据库,将数据融合算法和最短路径诱导算法的研究成果用于实际应用,利用GIS开发技术设计并实现了一个动态交通信息诱导系统。从而实现了理论研究与实际应用相结合,使交通信息诱导系统能够服务于普通出行者,进而提高城市交通效率。
最后,总结了本文所做的研究与设计工作,并对该课题下一步的工作进行了展望。
Intelligent Transportation Systems have been widely recognized as the solution to many problems in the field of transportation. For many traffic management or application systems, traffic information, especially the dynamic traffic information is the core content, which is centric to various features of systems. As the focused research issue in the field of intelligent transportation system, Dynamic Traffic Information System is based on the processing and application of dynamic traffic information. The system can provide real-time, accurate and comprehensive traffic information and the best travel route guidance. Thanks to this system, the dynamic traffic information collected by the traffic management department can serve the public. On the other hand, some references about decision-making could also be provided for the traffic management department. Dynamic Traffic Information System Research and Development The main technologies involved in the research and development of Dynamic Traffic Information System include collection technologies of dynamic traffic information, data preprocessing, data fusion technology, shortest route guidance algorithm and GIS.
This paper seeks to do some research and development of the Dynamic Traffic Information Guidance Systems. Some research in data fusion of dynamic traffic information and dynamic traffic route guidance technology has been done. Some related algorithms are improved to enhance their efficiency. And then, a Dynamic Traffic Information Guidance System has been developed based on GIS techniques.
The main contents of this paper are as follows.
To begin with, a brief introduction about Intelligent Transportation Systems and interrelated technologies including data collection methods and data preprocessing, data fusion technology is made. Then the paper puts the emphasis on the research of some common data fusion technology. After that, this paper develops the comprehensive statistical analysis algorithm to do data fusion for the dynamic traffic information.
Secondly, after introducing some commonly used shortest path algorithms in the Dynamic Traffic Route Guidance system, a detailed analysis the classical Dijkstra algorithm is made. Then the paper improved the network storage model and search method of Dijkstra algorithm. And then, simulation is completed and the results demonstrate that the improved algorithm is highly effective compared with the classical one.
And then, the paper designs the overall framework and data flow for the Dynamic Traffic Information Guidance System from the perspective of system development and design. After that, the data fusion module and shortest route guidance module are integrated with the GIS-platform. As a result, a Dynamic Traffic Information Guidance System has been designed and implemented to serve the general traveler and improve the efficiency of urban transport system.
Finally, the research and development, as well as the following improvement of this paper is summarized.
引文
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[3]高方伟.多传感器融合的技术研究[M].西安：西安电子科技大学,2007.
[4]熊卫.Dempster-Shafer证据理论及其解释[J].华南师范大学学报(社会科学版),2000,No.3,19-21.
[5]杨兆升,蹇峰,保丽霞,李贻武.信息融合技术在智能交通系统中的应用研究综述[C].第二届北京国际智能交通系统(ITS)技术研讨暨技术与产品展览会论文集.2002,255-265.
[6]郭庆胜,王晓延.地理信息系统工程设计与管理[M].武汉：武汉大学出版社,2003.
[7]董苏华,崔煦.地理信息系统在交通管理中的应用：交通地理信息系统[M].北京：科学出版社,2006.
[8]边馥玲.地理信息系统原理和方法[M].北京：测绘出版社,2006.
[9]杨兆升.基础交通信息融合技术及其应用[M].北京：中国铁道出版社,2005.
[10]姜桂艳.道路交通状态判别技术与应用[M].北京：人民交通出版社,2004.
[11]Mooney M. A. Health Monitoring During Vibratory Plate ComPaetion of soil[R]. Smart Structures and Materials, SanDiego,2002,4696(13).
[12]克莱因(Lawrence A.Klein)(美)著.多传感器数据融合理论及应用[M].北京：北京理工大学出版社,2004.
[13]Sehulz W.er al.Real-Time Damage Assessment of Civil Strueture using Fiber Grating Sensors and Modal Analysis[R]. Smart Struetures and Materials, SanDiego,2002,4696-28.
[14]S.M.鲍齐克.数字滤波和卡尔曼滤波[M].北京：科学出版社,1984.
[15]Kornprobst P, Deriche R, Aubert G. Image sequence analysis viapartial difference equations[J]. Journal of Mathematical Imaging and Vision,1999, 5-26.
[16]Dempster A P. Upper and lower probabilities induced by a multivalued mapping[J]. The Annals of Mathematical Statistics,1967,38(4):325-339.
[17]Shafer G A. Mathematical theory of evidence[M]. Princeton N J:Princeton University Press,1976.
[18]GUAN Xin, YI Xiao, and HE You. An Improved Dempster-Shafer Algorithm for Resolving the Conflicting Evidences[J]. International Journal of Information Technology,2005, Vol.11. No.12,32-34.
[19]罗蓉,孙东,邓勇,蒲小勃.基于模糊集和改进的D-S理论的数据融合方法[R].2006年航空试验测试技术学术交流会,2006,325-330.
[20]孔金生,李文艺.基于模糊集合的证据理论信息融合方法[J].计算机工程与应用,2008,44-16.
[21]郭惠昕.基于模糊集的证据组合方法及其应用[J].控制与决策,2008,Vol.23,No.2,17-19.
[22]孙全,叶秀清,顾伟康.一种新的基于证据理论的合成公式[J].电子学报,2000,28(8)：117-119.
[23]Ronald R. Yager. On the Dempster-Shafer framework and new combination rules[J]. Information Sciences,1987,41:93-137.
[24]Dubois D, Prade H. Representation and commbination of uncertainty with belief functions and possibility measures[J]. Computational Intelligence.1998, Vol.4: 244-264.
[25]汪云亮.一种新的基于权重系数的证据理论合成方法[J].电子信息对抗技术,2007,Vol.22,18-19.
[26]陈洪亮.智能交通系统中的路径牵引算法[J].微型电脑应用,1999,45-46.
[27]Clarke G, Wright J W. Seheduling of vehieles from a central depot to a number of delivery Points[J]. Operations Research,1964(12),568581.
[28]Holland J H. Adaptation in Nature and Artificial Systems[M]. MIT Press,1992.
[29]邹亮,徐建闽,朱玲湘.A*算法在基于电子地图的动态路径诱导中的应用[J].武汉理工大学学报.第30卷,第5期.2006,40-42.
[30]刘浩,鲍远律.A*算法在矢量地图最优路径搜索中的应用[J].计算机仿真.第25卷,第4期.2008,25-27.
[31]李得伟,韩宝明,韩宇.一种逆向改进型A*路径搜索算法[J].系统仿真学
报.第19卷,第22期.2007,31-32.
[32]Ono S. Researeh About damage identifieation of the struetural building using the Quasi-Newton Method[R]. Smart Structures and Materials, San Diego,2002, 4696-16.
[33]程满中.蚂蚁算法在车辆路径问题中的研究[D].武汉：中南民族大学,2007.
[34]杨海.蚁群算法及其在智能交通中的应用[D].山东：山东师范大学,2008.
[35]陈珊.改进型蚁群算法在运输调度优化问题中的应用研究[D].河南：河南大学,2008.
[36]沈卉.基于改进蚁群算法的配送线路优化研究[D].南京：东南大学,2006.
[37]詹玉洪,吴清红.求解VRP的蚁群算法研究[J].计算机科学,2008,30(9)：6065.
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