城市交通动态研判应用技术研究
|
摘要
随着社会经济高速发展,机动车保有量不断增加,城市道路交通需求增长迅速,道路的交通拥挤问题日益凸显。在此背景下,公安部对全国交警明确提出了利用智能交通系统来解决城市道路拥挤的要求,而城市道路交通状态的研判正是实现智能交通管理与控制的基础。目前我国各大城市的交通信息数据采集系统以及交通诱导信息发布系统建设均已初具规模,但在交通状态判别过程中还存在一些问题亟需完善。
基于上述背景,本文结合国内外相关课题研究现状,从交通动态研判概念诠释交通状态研判过程,以基础数据的准确性和研判过程的动态性为目标展开城市交通动态研判应用技术展开系统的研究,对提高交通状态研判精度,完善智能交通实用性有一定的借鉴意义。
本文从城市道路交通拥挤成因、度量标准、指标体系构成出发,选择反映交通状态的指标和参数；针对利用时间序列对交通信息数据修复方法的不足提出了基于交通信息数据时间序列与空间位置相似关系的数据修复方法,确保了基础数据的合理性和准确性；利用交通流向的时空关系将预测交通流数据与实测交通流数据的差值作为偏差控制形成动态反馈,提出了基于控制论与时空关系的交通流动态预测模型,实现了交通流预测的动态化,提高了交通流预测的精度；利用历史交通信息数据进行模糊聚类分析确定各交通状态的聚类中心,根据实时交通数据与聚类中心隶属度关系确定实时交通状态,在此过程本文提出了以交通状态聚类中心作为研判阈值的方法,解决了交通状态判别阈值预设的局限性。最后,本文采用VISSIM仿真软件以及Matlab软件完成了案例分析。
The traffic congestion has become a increasingly prominent problem with the rapid development of social economy, vehicle ownership increasing continuously and rapid growth of urban road traffic demand. In this context, Ministry of Public Security clearly suggest traffic police to solve the urban traffic congestion by using ITS, while it's just the basis for intelligent traffic management and control that urban road traffic conditions judgement. Nowadays, traffic information data acquisition system and traffic guidance information release system construction have begun to take shape in major cities of China,however, some problems still exist urgent need to improve traffic conditions identification in process.
Based on the above background and the situation on relative subject at home and abroad, it's starting from the concept to explain traffic conditions judgement process, to research the urban traffic dynamic judgement application technology that it's aim at the accuracy of basic data and dynamic of judgement process, and it's certain referential significance to which improve traffic conditions judgement accuracy and Intelligent Transportation practicality.
This paper starts from urban road traffic congestion causes, metric standard and constitution of index system to choice indicators and parameters which reflect the traffic conditions; proposed a data recovery method based on the data in the time series and spatial location proportional relationship to improve the lack of only by using time series, ensured the reasonableness and accuracy of basic data; it has proposed a traffic flow dynamic prediction model based on control theory and space-time relationship,which utilizes difference of predicted and actual collection traffic flow data as deviation control to form dynamic feedback through traffic flow time-space relationship, in order to achieve traffic conditions judgement's dynamic and improve the accuracy of traffic flow forecasting; It has determined the cluster centers of each traffic condition utilizing the historical traffic information data through fuzzy clustering analysis, according to the membership relationship of real-time traffic data and cluster centers to determine the real-time traffic conditions, and this paper propose a method which use traffic conditions cluster centers as the judgement threshold, to solve the limitations of traffic conditions identification default threshold. Finally, simulation software VISSIM and software MATLAB complete case analysis.
基于上述背景,本文结合国内外相关课题研究现状,从交通动态研判概念诠释交通状态研判过程,以基础数据的准确性和研判过程的动态性为目标展开城市交通动态研判应用技术展开系统的研究,对提高交通状态研判精度,完善智能交通实用性有一定的借鉴意义。
本文从城市道路交通拥挤成因、度量标准、指标体系构成出发,选择反映交通状态的指标和参数；针对利用时间序列对交通信息数据修复方法的不足提出了基于交通信息数据时间序列与空间位置相似关系的数据修复方法,确保了基础数据的合理性和准确性；利用交通流向的时空关系将预测交通流数据与实测交通流数据的差值作为偏差控制形成动态反馈,提出了基于控制论与时空关系的交通流动态预测模型,实现了交通流预测的动态化,提高了交通流预测的精度；利用历史交通信息数据进行模糊聚类分析确定各交通状态的聚类中心,根据实时交通数据与聚类中心隶属度关系确定实时交通状态,在此过程本文提出了以交通状态聚类中心作为研判阈值的方法,解决了交通状态判别阈值预设的局限性。最后,本文采用VISSIM仿真软件以及Matlab软件完成了案例分析。
The traffic congestion has become a increasingly prominent problem with the rapid development of social economy, vehicle ownership increasing continuously and rapid growth of urban road traffic demand. In this context, Ministry of Public Security clearly suggest traffic police to solve the urban traffic congestion by using ITS, while it's just the basis for intelligent traffic management and control that urban road traffic conditions judgement. Nowadays, traffic information data acquisition system and traffic guidance information release system construction have begun to take shape in major cities of China,however, some problems still exist urgent need to improve traffic conditions identification in process.
Based on the above background and the situation on relative subject at home and abroad, it's starting from the concept to explain traffic conditions judgement process, to research the urban traffic dynamic judgement application technology that it's aim at the accuracy of basic data and dynamic of judgement process, and it's certain referential significance to which improve traffic conditions judgement accuracy and Intelligent Transportation practicality.
This paper starts from urban road traffic congestion causes, metric standard and constitution of index system to choice indicators and parameters which reflect the traffic conditions; proposed a data recovery method based on the data in the time series and spatial location proportional relationship to improve the lack of only by using time series, ensured the reasonableness and accuracy of basic data; it has proposed a traffic flow dynamic prediction model based on control theory and space-time relationship,which utilizes difference of predicted and actual collection traffic flow data as deviation control to form dynamic feedback through traffic flow time-space relationship, in order to achieve traffic conditions judgement's dynamic and improve the accuracy of traffic flow forecasting; It has determined the cluster centers of each traffic condition utilizing the historical traffic information data through fuzzy clustering analysis, according to the membership relationship of real-time traffic data and cluster centers to determine the real-time traffic conditions, and this paper propose a method which use traffic conditions cluster centers as the judgement threshold, to solve the limitations of traffic conditions identification default threshold. Finally, simulation software VISSIM and software MATLAB complete case analysis.
引文
[1]徐磊.城市道路交通状态判别与预测系统设计与关键技术研究[D].吉林大学,2010
[2]江龙晖.城市道路交通状态判别及拥挤扩散范围估计方法研究[D].吉林大学,2007
[3]於毅.城市道路交通状态判别方法研究[D].北京交通大学,2007
[4]戢晓峰.城市道路交通状态分析方法回顾与展望[J].道路交通与安全.2008,3(8)：11-15
[5]W.Wen. A dynamic and automatic traffic light control expert system for solving the road congestion problem [J]. Expert Systems with Applications 34 (2008) 2370-2381
[6]Yibing Wang, Markos Papageorgiou, Albert Messmer, Pierluigi Coppola, Athina Tzimitsi, Agostino Nuzzolo. An adaptive freeway traffic state estimator [J]. Automatica 45 (2009) 10_24
[7]Ruey L. Cheu, Stephen G. Ritchie. Automated detection of lane-blocking freeway incidents using artificial neural networks. [J]. Transportation Research Part C:Emerging Technologies.6(3) 1995 371-388
[8]Yaser E. Hawas. A fuzzy-based system for incident detection in urban street networks. [J]. Transportation Research Part C:Emerging Technologies.2(15) 200769-95
[9]杨兆升,张茂雷.基于模糊综合评判的道路交通状态分析模型[J].公路交通科技.2010,9(27)：121-126
[10]淦学甄,刘清,张贵宾.城市道路交通拥挤识别方法研究[J].河北交通科技.2008,3(5)：62-64
[11]李佳伟,于忠霞.面向对象的道路交通拥挤识别算法表决融合[J].交通科技与经济.2008,03-0110-04
[12]周伟,罗石贵.基于模糊综合识别的事件检测算法[J].西安公路交通大学学报.2001,2(21)：70-73
[13]杨兆升,杨庆芳,冯金巧.基于模糊综合推理的道路交通事件识别算法[J].公路交通科技.2003,4(20)：92-94
[14]李娟,罗霞,赵永进.道路交通状态识别技术研究[J].铁道运输与经济.2009,3(31)：77-79
[15]戴红.基于模糊模式识别的城市道路交通状态检测算法[J].吉林工程技术师范学院学报.2005,3(21)：41-45
[16]黄欣,杨新苗,常玉林,程杰.城市交通拥挤的成因探析[J].交通运输工程与信息学报.2007,2(5)：108-114
[17]中华经济社会发展统计数据库[EB]. http://tongji.cnki.net/kns55/index.aspx
[18]GJJ37-1990.城市道路设计规范[S]
[19]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
[20]Yibing Wang, Markos Papageorgiou, Albert Messmer. Real-time freeway traffic state estimation based on extended Kalman filter:Adaptive capabilities and real data testing [J]. Transportation Research Part A 42 (2008) 1340-1358
[21]GUAN Wei, HE Shuyan. Statistical Features and Phase Identification of Traffic Flow on Urban Freeway [J]. Journal Of Transportation Systems Engineering And Information Technology Volume 7, Issue 5, October 2007
[22]Guiyan Jiang, Shifeng Niu, Ande chang, ZhiqiangMeng, Chunqin Zhang. The Method of Traffic Congestion Identification and Spatial and Temporal Dispersion Range Estimation [J].2010 2nd InternationalAsia Conferenceon Informaticsin Control, Automationand Robotics
[23]张文溥.道路交通检测技术与应用[M].北京：人民交通出版社,2010
[24]美国交通部联邦公路管理局著,卢毅,李钰,张谨帆,王晓宇等译.美国高速公路运营管理手册[M].北京：人民交通出版社,2009
[25]Mandellos N A, Keramitsoglou I, Kiranoudis C T. A back-ground subtraction algorithm for detecting and tracking vehicles. Expert Systems with Applications, 2011,38(3):1619-1631
[26]Grant C, Gillis B, Guensler R. Collection of vehicle activity data by video detection for use in transportation planning.Journal of Intelligent Transportation Systems,2000,5(4):343-361
[27]宋颖华.交通检测技术及其发展[J].公路.2000,9,34-37
[28]姜桂艳,张玮,常安德.基于GPS浮动车的交通信息采集系统的数据组织方法[J].吉林大学学报.2010,2(40)：397-401
[29]王曦光,胡春龙,刘丽娟.浅议交通量数据采集的几种方法[J].北方交通.2009,10：76-78
[30]席建锋.动态交通信息采集技术、处理方法及组织管理研究[D].吉林大学,2003
[31]姜桂艳,江龙晖,张晓东,王江锋.动态交通数据故障识别与修复方法[J].交通运输工程学报.2004,4(1)
[32]裴玉龙,马骥.实时交通数据的筛选与恢复研究[1J].土木工程学报.2003.36(7)
[33]贾琨.基于数据挖掘技术的交通信息处理与分析系统[D].山东师范大学,2005
[34]Rainer Hegger, Holger Kantz. Practical implementation of nonlinear time series methods; The TISEAN package, Rainer Hegger, Holger Kantz Max Planck Institute for Physics of Complex Systems; Thomas Schreiber, Physics DepartmentUniversity of Wuppertal.
[35]王殿海.交通流理论[M].北京：人民交通出版社,2002
[36]朱顺应,王红,向红艳.交通流参数及交通事件动态预测方法[M].南京：东南大学出版社,2008
[37]郭敏,肖翔,蓝金辉.道路交通流短时预测方法综述[J].自动化技术与应用.2009,6(28)：8-9
[38]姚智胜.基于实时数据的道路网短时交通流预测理论与方法研究[D].北京交通大学,2007
[39]于德新,杨兆升,刘雪杰.城市交通流诱导系统中的路段行程时间间接预 测方法研究[J].交通与计算机.2006,6(24)：18-25
[40]PEI Y L, MA J. Screening and rconstruction of real-time traffic data [J]. Journal of Harbin Institute of Technology,2003,10(1):1-7.
[41]DOU Huili, WANG Guohua, GUO Min. Traffic Guidance Oriented Model of Traffic State Probability Forecast [J]. JOURNAL OF TRANSPORTATION SYSTEMS ENGINEERING AND INFORMATION TECHNOLOGY Volume 11, Issue 2, April 2011
[42]S Turksma. The various uses of floating car data. Transport Information and Control Conference Publication.IEE,No.472,2002
[43]许贤良,王传礼.控制工程基础[M].北京：国防工业出版社,2008
[44]MAITRAM, CHATTERJEE A.Hybrid multiresolution Slantlet trans-form and fuzzy C-means clustering approach for norma-1 pathological brainMR image segregation [J]. Medical Engineering & Physics,2008,30(5):615-623.
[45]高新波.模糊聚类分析及应用[M].西安：西安电子科技大学出版社,2004
[46]诸静.模糊控制理论与系统原理[M].北京：机械工业出版社,2005
[47]胡宝清.模糊理论基础[M].武汉：武汉大学出版社,2004
[48]王新洲,史文中,王树良.模糊空间信息处理[M].武汉：武汉大学出版社,2003
[49]贾森.基于实时信息的城市道路交通状态判别方法研究[D].北京交通大学,2007
[50]王建玲,蒋阳升.交通拥挤状态的识别与分析[J].系统工程.2006,10(24)：115-109
[51]任其亮,肖裕民.城市路网交通拥堵H-Fuzzy评判方法研究[J].重庆交通大学学报.2008,5(27)：763-766
[52]张雷元,袁建华,姚琛.基于多源数据的交通状态判定研究[J].道路交通与安全.2009,2(9)：29-36
[53]李相勇,蒋葛夫.城市道路服务水平的模糊综合评判[J].交通运输系统工程与信息.2002,3(2)：48-55
[54]田世艳,刘伟铭.基于模糊综合评价的路段实时交通状态判别方法研究[J].科学技术与工程.2010,29(10)：7206-7210
[55]孙超,张云龙,王波,徐建闽.基于Fuzzy-logic的城市交叉口交通状态评价研究[J].交通与运输.2010.12,28-32
[56]范九伦,吴成茂.FCM算法中隶属度的新解释及其应用[J].电子学报.2004,2(32)：350-352
[57]王季方,卢正鼎.模糊控制中隶属度函数的确定方法[J].河南科学.2000,4(18)：348-351
[58]程学庆,唐瑞雪,朱海,樊旭斌.基于VISSIM的高速公路交通事件仿真及数据处理[1J].交通运输工程与信息学报.2010,4(8)：14-20
[2]江龙晖.城市道路交通状态判别及拥挤扩散范围估计方法研究[D].吉林大学,2007
[3]於毅.城市道路交通状态判别方法研究[D].北京交通大学,2007
[4]戢晓峰.城市道路交通状态分析方法回顾与展望[J].道路交通与安全.2008,3(8)：11-15
[5]W.Wen. A dynamic and automatic traffic light control expert system for solving the road congestion problem [J]. Expert Systems with Applications 34 (2008) 2370-2381
[6]Yibing Wang, Markos Papageorgiou, Albert Messmer, Pierluigi Coppola, Athina Tzimitsi, Agostino Nuzzolo. An adaptive freeway traffic state estimator [J]. Automatica 45 (2009) 10_24
[7]Ruey L. Cheu, Stephen G. Ritchie. Automated detection of lane-blocking freeway incidents using artificial neural networks. [J]. Transportation Research Part C:Emerging Technologies.6(3) 1995 371-388
[8]Yaser E. Hawas. A fuzzy-based system for incident detection in urban street networks. [J]. Transportation Research Part C:Emerging Technologies.2(15) 200769-95
[9]杨兆升,张茂雷.基于模糊综合评判的道路交通状态分析模型[J].公路交通科技.2010,9(27)：121-126
[10]淦学甄,刘清,张贵宾.城市道路交通拥挤识别方法研究[J].河北交通科技.2008,3(5)：62-64
[11]李佳伟,于忠霞.面向对象的道路交通拥挤识别算法表决融合[J].交通科技与经济.2008,03-0110-04
[12]周伟,罗石贵.基于模糊综合识别的事件检测算法[J].西安公路交通大学学报.2001,2(21)：70-73
[13]杨兆升,杨庆芳,冯金巧.基于模糊综合推理的道路交通事件识别算法[J].公路交通科技.2003,4(20)：92-94
[14]李娟,罗霞,赵永进.道路交通状态识别技术研究[J].铁道运输与经济.2009,3(31)：77-79
[15]戴红.基于模糊模式识别的城市道路交通状态检测算法[J].吉林工程技术师范学院学报.2005,3(21)：41-45
[16]黄欣,杨新苗,常玉林,程杰.城市交通拥挤的成因探析[J].交通运输工程与信息学报.2007,2(5)：108-114
[17]中华经济社会发展统计数据库[EB]. http://tongji.cnki.net/kns55/index.aspx
[18]GJJ37-1990.城市道路设计规范[S]
[19]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
[20]Yibing Wang, Markos Papageorgiou, Albert Messmer. Real-time freeway traffic state estimation based on extended Kalman filter:Adaptive capabilities and real data testing [J]. Transportation Research Part A 42 (2008) 1340-1358
[21]GUAN Wei, HE Shuyan. Statistical Features and Phase Identification of Traffic Flow on Urban Freeway [J]. Journal Of Transportation Systems Engineering And Information Technology Volume 7, Issue 5, October 2007
[22]Guiyan Jiang, Shifeng Niu, Ande chang, ZhiqiangMeng, Chunqin Zhang. The Method of Traffic Congestion Identification and Spatial and Temporal Dispersion Range Estimation [J].2010 2nd InternationalAsia Conferenceon Informaticsin Control, Automationand Robotics
[23]张文溥.道路交通检测技术与应用[M].北京：人民交通出版社,2010
[24]美国交通部联邦公路管理局著,卢毅,李钰,张谨帆,王晓宇等译.美国高速公路运营管理手册[M].北京：人民交通出版社,2009
[25]Mandellos N A, Keramitsoglou I, Kiranoudis C T. A back-ground subtraction algorithm for detecting and tracking vehicles. Expert Systems with Applications, 2011,38(3):1619-1631
[26]Grant C, Gillis B, Guensler R. Collection of vehicle activity data by video detection for use in transportation planning.Journal of Intelligent Transportation Systems,2000,5(4):343-361
[27]宋颖华.交通检测技术及其发展[J].公路.2000,9,34-37
[28]姜桂艳,张玮,常安德.基于GPS浮动车的交通信息采集系统的数据组织方法[J].吉林大学学报.2010,2(40)：397-401
[29]王曦光,胡春龙,刘丽娟.浅议交通量数据采集的几种方法[J].北方交通.2009,10：76-78
[30]席建锋.动态交通信息采集技术、处理方法及组织管理研究[D].吉林大学,2003
[31]姜桂艳,江龙晖,张晓东,王江锋.动态交通数据故障识别与修复方法[J].交通运输工程学报.2004,4(1)
[32]裴玉龙,马骥.实时交通数据的筛选与恢复研究[1J].土木工程学报.2003.36(7)
[33]贾琨.基于数据挖掘技术的交通信息处理与分析系统[D].山东师范大学,2005
[34]Rainer Hegger, Holger Kantz. Practical implementation of nonlinear time series methods; The TISEAN package, Rainer Hegger, Holger Kantz Max Planck Institute for Physics of Complex Systems; Thomas Schreiber, Physics DepartmentUniversity of Wuppertal.
[35]王殿海.交通流理论[M].北京：人民交通出版社,2002
[36]朱顺应,王红,向红艳.交通流参数及交通事件动态预测方法[M].南京：东南大学出版社,2008
[37]郭敏,肖翔,蓝金辉.道路交通流短时预测方法综述[J].自动化技术与应用.2009,6(28)：8-9
[38]姚智胜.基于实时数据的道路网短时交通流预测理论与方法研究[D].北京交通大学,2007
[39]于德新,杨兆升,刘雪杰.城市交通流诱导系统中的路段行程时间间接预 测方法研究[J].交通与计算机.2006,6(24)：18-25
[40]PEI Y L, MA J. Screening and rconstruction of real-time traffic data [J]. Journal of Harbin Institute of Technology,2003,10(1):1-7.
[41]DOU Huili, WANG Guohua, GUO Min. Traffic Guidance Oriented Model of Traffic State Probability Forecast [J]. JOURNAL OF TRANSPORTATION SYSTEMS ENGINEERING AND INFORMATION TECHNOLOGY Volume 11, Issue 2, April 2011
[42]S Turksma. The various uses of floating car data. Transport Information and Control Conference Publication.IEE,No.472,2002
[43]许贤良,王传礼.控制工程基础[M].北京：国防工业出版社,2008
[44]MAITRAM, CHATTERJEE A.Hybrid multiresolution Slantlet trans-form and fuzzy C-means clustering approach for norma-1 pathological brainMR image segregation [J]. Medical Engineering & Physics,2008,30(5):615-623.
[45]高新波.模糊聚类分析及应用[M].西安：西安电子科技大学出版社,2004
[46]诸静.模糊控制理论与系统原理[M].北京：机械工业出版社,2005
[47]胡宝清.模糊理论基础[M].武汉：武汉大学出版社,2004
[48]王新洲,史文中,王树良.模糊空间信息处理[M].武汉：武汉大学出版社,2003
[49]贾森.基于实时信息的城市道路交通状态判别方法研究[D].北京交通大学,2007
[50]王建玲,蒋阳升.交通拥挤状态的识别与分析[J].系统工程.2006,10(24)：115-109
[51]任其亮,肖裕民.城市路网交通拥堵H-Fuzzy评判方法研究[J].重庆交通大学学报.2008,5(27)：763-766
[52]张雷元,袁建华,姚琛.基于多源数据的交通状态判定研究[J].道路交通与安全.2009,2(9)：29-36
[53]李相勇,蒋葛夫.城市道路服务水平的模糊综合评判[J].交通运输系统工程与信息.2002,3(2)：48-55
[54]田世艳,刘伟铭.基于模糊综合评价的路段实时交通状态判别方法研究[J].科学技术与工程.2010,29(10)：7206-7210
[55]孙超,张云龙,王波,徐建闽.基于Fuzzy-logic的城市交叉口交通状态评价研究[J].交通与运输.2010.12,28-32
[56]范九伦,吴成茂.FCM算法中隶属度的新解释及其应用[J].电子学报.2004,2(32)：350-352
[57]王季方,卢正鼎.模糊控制中隶属度函数的确定方法[J].河南科学.2000,4(18)：348-351
[58]程学庆,唐瑞雪,朱海,樊旭斌.基于VISSIM的高速公路交通事件仿真及数据处理[1J].交通运输工程与信息学报.2010,4(8)：14-20