浮动车交通参数检测及在道路交通状态分析中的应用研究
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摘要
准确的道路交通参数检测和道路交通状态分析是交通诱导与控制、道路交通管理及规划的基础。在道路交通参数检测方面,基于GPS浮动车交通参数检测技术作为一种新的检测方式,如何提高其检测性能是目前的核心问题。另一方面,仅仅依靠道路交通参数尚不足以全面反映道路交通状态,如何建立符合实际的道路交通的状态分析和评估方法,正受到广泛关注。
本文主要从面向GPS浮动车的交通参数检测关键技术以及道路交通状态分析两方面展开研究。在浮动车交通参数检测方面,重点通过解决目前在道路图层校正、数据预处理等领域存在的基础技术问题,研究解决利用混合浮动车(公交车、出租车等)改善路段平均速度估计性能的方法。在道路交通状态分析方面,从宏观的角度,研究了利用浮动车检测的交通参数探讨车流状态的内在规律,从微观的角度,利用车辆驾驶的微观跟驰特性研究了道路交通流的状态变化规律。论文的主要工作包括:
①充分利用GPS浮动车数据蕴含的丰富信息,通过数据规律分析,建立了面
向道路节点的道路图层校正模型,解决了商业地图准确度校正的关键问题。首先基于路网拓扑结构的道路图层节点匹配方法,对原始道路图层中道路节点存在的“不及”、“过头”和“交叉口相离”等不规范情况进行修正;在此基础上,提出依托道路节点构建道路节点静态缓冲区,并利用GPS浮动车数据在静态缓冲区的分布密度构建道路节点动态缓冲区,由该区域筛选出表征道路节点信息的有效数据;利用这些数据,建立了基于层次聚类的道路节点校正方法,实现了道路节点的校正;通过实际道路实验,证实了方法的有效性。
②针对公交车、出租车等不同浮动车,分别建立了特殊零数据的预处理方法,在此基础上,通过从单车到多车整合不同车型的GPS浮动车数据,建立了利用混合浮动车改善路段平均速度估计性能的方法。
针对浮动车经常存在零速度数据的情况,在分析出租车和公交车的行驶特性和GPS数据特征的基础上,分别提出了出租车和公交车的零数据处理算法,进而建立了考虑浮动车零速度数据的预处理模型,解决了浮动车在车站、路口以及运行中异常停车导致的延误问题。
针对GPS浮动车数据序列与路段起止节点之间相对位置的各种情形,并结合上述预处理模型,分别建立了基于局部行程时间的单型车单车路段区间平均速度估计模型,并以该车占同型车的有效数据比整合加权,形成了单型车的多车路段平均速度估计模型;另一方面,通过层次聚类整合路段上单车的瞬时速度序列,建立了基于瞬时速度的单型车多车路段平均速度估计模型;在此基础上,将上述两种路段平均速度估计模型进行整合,建立了单型车的路段平均速度估计模型;最后,分别针对公交车和出租车的不同特点,并考虑有效公交车数据和有效出租车数据的占比,整合形成了多车型的混合浮动车路段平均速度估计模型,并根据实际采集的浮动车GPS数据对模型进行了验证。
③提出了基于去趋势波动分析(Detrended Fluctuation Analysis, DFA)的交通离散时间序列分析方法,为利用交通参数变化揭示车流状态的内在规律提供了新的途径。
针对实际检测获取交通流参数大多是非平稳和有限的情况,从标度不变性的角度对交通流时间序列进行分析,通过引入去趋势波动分析法(DFA),分别对出租车和公交车的路段平均速度估计数据进行指数标定,以实际车流时间序列的标度指数探讨了车流时间序列的长程相关性及其内在规律,结果表明,公交车的路段平均速度估计存在长程相关关系,而出租车的路段平均速度估计不存在长程相关关系。
④研究了利用车辆驾驶的微观跟驰特性分析道路交通状态变化规律的方法,通过建立新的车辆跟驰模型,讨论了道路交通状态的稳定性问题,进而分析了道路交通状态的变化规律。
道路交通状态的变化本质上是车辆行驶行为的客观反映。现代传感、通信和车联网技术的发展为刻画车辆驾驶的微观特性提供了有利条件,为了准确描述道路上车辆行驶的跟驰特性,进而掌握道路交通状态变化规律,分别提出了一种扩展的双车跟驰模型(Two Car-following, TCF)和考虑后视效应的多车跟驰模型(Backward looking& Multiple Car-following, BLMCF)。并在上述模型的基础上,分别讨论了道路交通流的车头间距和车辆速度随时间变化的分布情况。仿真结果表明,当在道路交通系统中考虑次近邻车对跟驰车辆的影响及后视效应下多前车对跟驰车辆的影响时,不仅有助于提高道路交通状态的稳定性,也有助于抑制道路交通拥堵现象的产生,能有效改善道路交通流的运行状态。
综上所述,本文提出了基于GPS浮动车技术的面向道路节点的道路图层校正方法,提出了基于混合浮动车的路段平均速度估计方法,提出了基于去趋势波动分析(DFA)的交通离散时间序列分析方法,提出了利用车辆跟驰特性分析道路交通状态变化规律的方法,以准确分析道路交通流状态的演变规律。实验结果验证了上述工作的有效性。
Accurate parameters detection and road traffic condition analysis are the foundation of modern traffic management, control, guidance and planning. At present, in the field of traffic parameters detection, the crucial problems of GPS–FCD(GPS-Floating Car) based detection technique, as a new direction of traffic detection, is how to improve the performance of detection. On the other hand, it is insufficient to reflect the road traffic condition depending on road traffic parameters only, how to construct analysis and estimation methods that correspond the practical situation, has been paid wide attention.
In this paper, the project is mainly conducted base on the key detection technique of traffic parameters from GPS–FCD and road traffic condition analysis. For floating car traffic parameters detection, the key points are breaking through the foundation technique problems existed in road layer correction and data pre-processing field, to research the method that improves the performance of road average velocity estimation using mixture floating cars (bus, taxi etc). In road traffic condition analysis, discussing the inherent law of vehicles flow state through traffic parameters acquired by floating car, from the macroscopic viewpoint; researching the change and its law using microcosmic following characteristic of vehicles driving from the microcosmic viewpoint.
The main research in this paper as follows:
①Making full use of the abundant information implied in floating cars data, road layer correction model for road nodes through large scale data analysis to solve the key problem in business map accuracy correction has been established. Firstly, the paper adopts a method of road nodes matching based on road network topological structure to revising the non-normal nodes in original road layer. Secondly, constructs the static buffers on the basis of roadway nodes, meanwhile, using GPS data to construct dynamic buffers of nodes based on static buffer. Finally, the paper chooses the GPS points in the dynamic buffers to guarantee accuracy of clustering data maximally. a method for road nodes accurate information determine is proposed based on hierarchical cluster using these data, and correction of road nodes is implemented. The effectiveness of this method has been tested through actual road experiment.
②For different type of floating car (bus, taxi etc), a method is constructed in this paper, it aims at specific zero data pre-processing respectively. On the basis of this method, a method to improve estimation performance of road segment average velocity is put forward by means of integrating floating car data from single-car to multi-car.
For the case that GPS data usually have masses of zero velocity data, on the basis of bus and taxi driving properties and characteristics of GPS data, zero data processing algorithms is proposed for bus and taxi respectively. Furthermore, a preprocessing model taking floating car zero velocity data into account is established to solve the delay processing problem caused by floating car unusually stop at station, crossing or in run-time.
According to different situations of relative position among floating car data series and start-stop nodes and preprocessing model proposed above, to construct the single car road segment average velocity model for single class car based on local driving time respectively, and forms the multi-car road average velocity model for single-class car taking the proportion that the valid data of this car dominate in all valid data of same class cars as weighting. On the other hand, multi-car road segment instantaneous average velocity model for single-class car has been established by means of hierarchical cluster integrate the instantaneous time series of single car on road segment; two kinds of road segment average estimation model above-mentioned has been integrated, and constructs road segment average velocity model; Finally, according to the different features of bus and taxi, blend floating car road segment average velocity estimation model based multi-cars established take consideration of proportion of bus valid data dominate in taxi valid data. Model has been tested through practical data from floating car.
③The paper proposes a method for traffic discrete-time series analysis based on the Detrended Fluctuation Analysis (DFA), and provides a new technique to reveal inherent law of car flow status with variation traffic parameters. In most circumstances traffic flow parameters obtained from practical detection are unstable and limited; the paper analyzes traffic flow time series from the viewpoint of scale invariance, and demarcates the index of road segment average velocity of taxies and buses by introducing the Detrended Fluctuation Analysis (DFA). The paper discusses the long-range correlation and internal regularity of the time series of traffic flow using scaling exponents of actual traffic flow time series. The result indicates that the road segment average velocity of buses exits long-rang correlation, but taxies doesn’t.
④This paper proposes the method for road traffic condition change and its laws from microcosmic car following characteristics, and discusses the stability of road traffic state by mean of building a new car following model, and then analyzes the road traffic state change and its laws.
The change of road traffic condition is an intrinsic objective reflection of vehicle driving behavior. The development of modern sensor, communication and Internet of Vehicles technique provide advantages for depicting microcosmic features of vehicle driving. A extended Tow Car-following (TCF) model and a Backward Looking & Multiple Car-following (BLMCF) have been put forward respectively for description of the following features of vehicles driving on road accurately and grasp the change regularity of road traffic condition. This paper also discusses the distribution of vehicle spacing and velocity varying with time in road traffic flow, based on the models provided above. The simulation results show that it not only contributes to improving the stability of road traffic condition, but also restrains the occurrence of congestion phenomenon in road traffic, and improves the road traffic flow status while considering the effects on following vehicle from adjacent vehicles and leading vehicles which take backward looking effect into consideration in road traffic system effectively.
From the above, the contributions in this paper are: proposes a road layer correction method facing with road nodes based on floating car techniques, an estimation method for road segment average velocity based on blend floating car, an analysis method for traffic time series based on the DFA, and an analysis method for traffic state change regularity using car following characteristics. The effectiveness of the work has been tested and verified by experiment results.
本文主要从面向GPS浮动车的交通参数检测关键技术以及道路交通状态分析两方面展开研究。在浮动车交通参数检测方面,重点通过解决目前在道路图层校正、数据预处理等领域存在的基础技术问题,研究解决利用混合浮动车(公交车、出租车等)改善路段平均速度估计性能的方法。在道路交通状态分析方面,从宏观的角度,研究了利用浮动车检测的交通参数探讨车流状态的内在规律,从微观的角度,利用车辆驾驶的微观跟驰特性研究了道路交通流的状态变化规律。论文的主要工作包括:
①充分利用GPS浮动车数据蕴含的丰富信息,通过数据规律分析,建立了面
向道路节点的道路图层校正模型,解决了商业地图准确度校正的关键问题。首先基于路网拓扑结构的道路图层节点匹配方法,对原始道路图层中道路节点存在的“不及”、“过头”和“交叉口相离”等不规范情况进行修正;在此基础上,提出依托道路节点构建道路节点静态缓冲区,并利用GPS浮动车数据在静态缓冲区的分布密度构建道路节点动态缓冲区,由该区域筛选出表征道路节点信息的有效数据;利用这些数据,建立了基于层次聚类的道路节点校正方法,实现了道路节点的校正;通过实际道路实验,证实了方法的有效性。
②针对公交车、出租车等不同浮动车,分别建立了特殊零数据的预处理方法,在此基础上,通过从单车到多车整合不同车型的GPS浮动车数据,建立了利用混合浮动车改善路段平均速度估计性能的方法。
针对浮动车经常存在零速度数据的情况,在分析出租车和公交车的行驶特性和GPS数据特征的基础上,分别提出了出租车和公交车的零数据处理算法,进而建立了考虑浮动车零速度数据的预处理模型,解决了浮动车在车站、路口以及运行中异常停车导致的延误问题。
针对GPS浮动车数据序列与路段起止节点之间相对位置的各种情形,并结合上述预处理模型,分别建立了基于局部行程时间的单型车单车路段区间平均速度估计模型,并以该车占同型车的有效数据比整合加权,形成了单型车的多车路段平均速度估计模型;另一方面,通过层次聚类整合路段上单车的瞬时速度序列,建立了基于瞬时速度的单型车多车路段平均速度估计模型;在此基础上,将上述两种路段平均速度估计模型进行整合,建立了单型车的路段平均速度估计模型;最后,分别针对公交车和出租车的不同特点,并考虑有效公交车数据和有效出租车数据的占比,整合形成了多车型的混合浮动车路段平均速度估计模型,并根据实际采集的浮动车GPS数据对模型进行了验证。
③提出了基于去趋势波动分析(Detrended Fluctuation Analysis, DFA)的交通离散时间序列分析方法,为利用交通参数变化揭示车流状态的内在规律提供了新的途径。
针对实际检测获取交通流参数大多是非平稳和有限的情况,从标度不变性的角度对交通流时间序列进行分析,通过引入去趋势波动分析法(DFA),分别对出租车和公交车的路段平均速度估计数据进行指数标定,以实际车流时间序列的标度指数探讨了车流时间序列的长程相关性及其内在规律,结果表明,公交车的路段平均速度估计存在长程相关关系,而出租车的路段平均速度估计不存在长程相关关系。
④研究了利用车辆驾驶的微观跟驰特性分析道路交通状态变化规律的方法,通过建立新的车辆跟驰模型,讨论了道路交通状态的稳定性问题,进而分析了道路交通状态的变化规律。
道路交通状态的变化本质上是车辆行驶行为的客观反映。现代传感、通信和车联网技术的发展为刻画车辆驾驶的微观特性提供了有利条件,为了准确描述道路上车辆行驶的跟驰特性,进而掌握道路交通状态变化规律,分别提出了一种扩展的双车跟驰模型(Two Car-following, TCF)和考虑后视效应的多车跟驰模型(Backward looking& Multiple Car-following, BLMCF)。并在上述模型的基础上,分别讨论了道路交通流的车头间距和车辆速度随时间变化的分布情况。仿真结果表明,当在道路交通系统中考虑次近邻车对跟驰车辆的影响及后视效应下多前车对跟驰车辆的影响时,不仅有助于提高道路交通状态的稳定性,也有助于抑制道路交通拥堵现象的产生,能有效改善道路交通流的运行状态。
综上所述,本文提出了基于GPS浮动车技术的面向道路节点的道路图层校正方法,提出了基于混合浮动车的路段平均速度估计方法,提出了基于去趋势波动分析(DFA)的交通离散时间序列分析方法,提出了利用车辆跟驰特性分析道路交通状态变化规律的方法,以准确分析道路交通流状态的演变规律。实验结果验证了上述工作的有效性。
Accurate parameters detection and road traffic condition analysis are the foundation of modern traffic management, control, guidance and planning. At present, in the field of traffic parameters detection, the crucial problems of GPS–FCD(GPS-Floating Car) based detection technique, as a new direction of traffic detection, is how to improve the performance of detection. On the other hand, it is insufficient to reflect the road traffic condition depending on road traffic parameters only, how to construct analysis and estimation methods that correspond the practical situation, has been paid wide attention.
In this paper, the project is mainly conducted base on the key detection technique of traffic parameters from GPS–FCD and road traffic condition analysis. For floating car traffic parameters detection, the key points are breaking through the foundation technique problems existed in road layer correction and data pre-processing field, to research the method that improves the performance of road average velocity estimation using mixture floating cars (bus, taxi etc). In road traffic condition analysis, discussing the inherent law of vehicles flow state through traffic parameters acquired by floating car, from the macroscopic viewpoint; researching the change and its law using microcosmic following characteristic of vehicles driving from the microcosmic viewpoint.
The main research in this paper as follows:
①Making full use of the abundant information implied in floating cars data, road layer correction model for road nodes through large scale data analysis to solve the key problem in business map accuracy correction has been established. Firstly, the paper adopts a method of road nodes matching based on road network topological structure to revising the non-normal nodes in original road layer. Secondly, constructs the static buffers on the basis of roadway nodes, meanwhile, using GPS data to construct dynamic buffers of nodes based on static buffer. Finally, the paper chooses the GPS points in the dynamic buffers to guarantee accuracy of clustering data maximally. a method for road nodes accurate information determine is proposed based on hierarchical cluster using these data, and correction of road nodes is implemented. The effectiveness of this method has been tested through actual road experiment.
②For different type of floating car (bus, taxi etc), a method is constructed in this paper, it aims at specific zero data pre-processing respectively. On the basis of this method, a method to improve estimation performance of road segment average velocity is put forward by means of integrating floating car data from single-car to multi-car.
For the case that GPS data usually have masses of zero velocity data, on the basis of bus and taxi driving properties and characteristics of GPS data, zero data processing algorithms is proposed for bus and taxi respectively. Furthermore, a preprocessing model taking floating car zero velocity data into account is established to solve the delay processing problem caused by floating car unusually stop at station, crossing or in run-time.
According to different situations of relative position among floating car data series and start-stop nodes and preprocessing model proposed above, to construct the single car road segment average velocity model for single class car based on local driving time respectively, and forms the multi-car road average velocity model for single-class car taking the proportion that the valid data of this car dominate in all valid data of same class cars as weighting. On the other hand, multi-car road segment instantaneous average velocity model for single-class car has been established by means of hierarchical cluster integrate the instantaneous time series of single car on road segment; two kinds of road segment average estimation model above-mentioned has been integrated, and constructs road segment average velocity model; Finally, according to the different features of bus and taxi, blend floating car road segment average velocity estimation model based multi-cars established take consideration of proportion of bus valid data dominate in taxi valid data. Model has been tested through practical data from floating car.
③The paper proposes a method for traffic discrete-time series analysis based on the Detrended Fluctuation Analysis (DFA), and provides a new technique to reveal inherent law of car flow status with variation traffic parameters. In most circumstances traffic flow parameters obtained from practical detection are unstable and limited; the paper analyzes traffic flow time series from the viewpoint of scale invariance, and demarcates the index of road segment average velocity of taxies and buses by introducing the Detrended Fluctuation Analysis (DFA). The paper discusses the long-range correlation and internal regularity of the time series of traffic flow using scaling exponents of actual traffic flow time series. The result indicates that the road segment average velocity of buses exits long-rang correlation, but taxies doesn’t.
④This paper proposes the method for road traffic condition change and its laws from microcosmic car following characteristics, and discusses the stability of road traffic state by mean of building a new car following model, and then analyzes the road traffic state change and its laws.
The change of road traffic condition is an intrinsic objective reflection of vehicle driving behavior. The development of modern sensor, communication and Internet of Vehicles technique provide advantages for depicting microcosmic features of vehicle driving. A extended Tow Car-following (TCF) model and a Backward Looking & Multiple Car-following (BLMCF) have been put forward respectively for description of the following features of vehicles driving on road accurately and grasp the change regularity of road traffic condition. This paper also discusses the distribution of vehicle spacing and velocity varying with time in road traffic flow, based on the models provided above. The simulation results show that it not only contributes to improving the stability of road traffic condition, but also restrains the occurrence of congestion phenomenon in road traffic, and improves the road traffic flow status while considering the effects on following vehicle from adjacent vehicles and leading vehicles which take backward looking effect into consideration in road traffic system effectively.
From the above, the contributions in this paper are: proposes a road layer correction method facing with road nodes based on floating car techniques, an estimation method for road segment average velocity based on blend floating car, an analysis method for traffic time series based on the DFA, and an analysis method for traffic state change regularity using car following characteristics. The effectiveness of the work has been tested and verified by experiment results.
引文
[1]杨兆升.智能运输系统概论[M].北京:人民交通出版社, 2003.
[2]张其善,吴今培,杨东凯.智能交通车辆定位导航系统及其应用[M].北京:科学出版社, 2002.
[3]田欢欢,薛郁,康三军,梁玉娟.元胞自动机混合交通流模型的能耗研究[J].物理学报, 2009, 58(7): 4506-4513.
[4]陆化普.城市交通现代化管理[M].北京:人民交通出版社, 1999.
[5]董均宇.基于GPS浮动车的城市路段平均速度估计技术研究[D].重庆:重庆大学, 2006.
[6] D. J. Dailey, S. Maclean, F. W. Cattey, D. Meyers. A self-describing data transfer model for ITS applications[J]. Intelligent Transportation Systems, 2002: 293-300.
[7]张剑平,任福继.地理信息系统与MapInfo应用[M].北京:科学出版社, 1999.
[8]齐锐,屈韶林,阳林赟.用MapX开发地理信息系统[M].北京:清华大学出版社, 2003.
[9] B. Ding, K. C. Wong. A System for automatic extraction of road network from maps[C]. Proc. IEEE International Joint Symposia on Intelligence and Systems, Rockville, USA. 1998: 359–366.
[10]屈静,鲍远律.交通矢量地图离散非线性校正算法[J].计算机仿真, 2006, 23(5): 77-80.
[11]段凌宇,鲍远律,张旺生. GPS Vehicle Navigation System [J].南京航空航天大学学报(英文版), 1998, 15(2): 172-178.
[12] Y. L. Bao, G. J. Wang, J. M. Ye. OverallDesign of Xiamen City GPS Intelligent Vehicle Monitoring System [C] . ICCA02, Xiamen, China, 2002: 887-891.
[13]苏春光,徐杰,王鸿谷.地图及工程图纸的智能矢量化方法[J].小型微型计算机系统, 1996, 5(17): 6-11.
[14]林治远等. CJJ37-90城市道路设计规范[M].北京:中国建筑工业出版社, 1990.
[15]徐循初等. GB50220-95城市道路交通规划设计规范[M].北京:中国建筑工业出版社, 1995.
[16]肖锋.面向道路交通状态监测的GPS与GIS数据预处理关键技术研究[D].重庆:重庆大学,2008
[17] R. Danescu, S. Nedevschi. Probabilistic Lane Tracking in Difficult RoadScenarios Using Stereovision[J]. IEEE Transactions on Intelligent Transportation Systems, 2009, 10(2): 272-282.
[18] F. Oniga, S. Nedevschi. Processing Dense Stereo Data Using Elevation Maps: Road Surface, Trafic Isle and Obstacle Detection[J]. IEEE Transactions on Vehicular Technology, 2010,59(3): 1172-1182.
[19] S. Theodoridis, K. Koutroumbas. Pattern recognition[M]. 2-nd edition, Elsevier Academic Press, 2003.
[20]朱振兴.从栅格数据直接建立拓扑关系的算法研究[J].环境遥感.1993, 8(3): 232-240.
[21]郭杰华,姚振旺,鲍远律,张旺生.矢量地图的一种自动校正算法[J].中国图像图形学报, 1999, 4(5): 423-426.
[22]张安胜,薛俊萍,钱辉环.一种基于几何变换的地图校正方法[J].微机发展, 2002(3): 30- 33.
[23]徐东平,周师盛,钟涛.基于矩阵变换的矢量地图校正算法[J].武汉理工大学学报(交通科学与工程版).2004,28(2) :274-277.
[24]杨凌,常江龙.一种基于道路知识的矢量地图数据校正方法[J].计算机仿真. 2008, 25(5): 230-233.
[25] Macqueen. Some methods for classification and analysis of multivariate observations [C]. Proc.5th Berkeley Symp.Math.Statist, 1967, (1): 281-297.
[26] R. R. He, L. K. Alain., B. Ran. Map Alignment Using Probe Vehicle Data[CD]. TRB 2003 Annual Meeting, America, 2003: 1-25.
[27] J. L. Martínez, M. A. Martínez. A new method of generating differential GPS corrections[J]. Control Engineering Practice, 2000, 8(3): 253-258.
[28]鲍远律,夏冰,鲍远慧. GPS车辆监控系统开发的关键技术[J].中国公路.交通信息产业, 2001,2(3):42-44.
[29]丁军.关于置信椭圆的讨论[J].测绘通报, 1990, (4): 21-23.
[30]杜江平.基于GPS/GIS车辆定位导航系统的研究[D].成都:电子科技大学, 2009.
[31]廖孝勇,孙棣华,周舒杰,田川,古曦.混合浮动车交通状态估计的样本数量研究[J].计算机工程与应用. 2011,47(24):229-232.
[32]周舒杰.基于公交浮动车的道路平均速度估计研究[D].重庆:重庆大学, 2009.
[33]戴冀峰,马健霄等.交通工程概论[M].北京:人民交通出版社, 2006.
[34] B. Rohini. Predicting speeds on urban streets using real-time GPS data[J]. Masters Abstracts International, 2000, 41(04): 1137.
[35] F. D. Anthony. A framework to transform real-time GPS data derived from transit vehicles to determine speed-flow characteristics of arterials[J]. Dissertation Abstracts International, Section: B, 2002, 64(12): 6215.
[36]陈永恒,王殿海,等.无物理隔离路段机动车与非机动车速度特性研究[J].交通运输系统工程与信息, 2009, 9(5): 53-57.
[37] K. L. Ding, H. T. Wang. A New algorithms for dynamic traffic information collection[C].IEEE Computer Society, 2009 Second International Conference on Intelligent Computation Technology and Automation, 2009: 437-439.
[38] Y. Y. Li, M. McDonald. Link travel time estimation using single GPS equipped probe vehicle[C]. Proceedings of Intelligent Transportation Systems, 2002: 932-937.
[39] A. D. Chang, G. Y. Jiang, S. F. Niu. Trffic Congestion Identification Method Based on GPS Equipped Floating Car[J]. 2010 IEEE computer socirty, 2010: 1069-1071.
[40] C. A. Quiroga, D. Bulloek. Travel Time Studies With Global Positioning and Geographic Information Systems: An Integrated Methodology[J]. Transportation Research Part C, 1998.
[41]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[J].计算机应用研究, 2007, 2: 243-245.
[42]李筱菁,孟庆春,魏振钢,魏天滨,王旭柱,郭忠文,丁鹏. GPS技术在城市交通状况实时检测技术中的应用[J].青岛海洋大学学报, 2002, 32(3): 475-481.
[43]王文辉.基于浮动车的道路交通状况评估中信息融合应用研究[D].北京:北京交通大学, 2008.
[44] Caveney, S. Derek. Method and system for predicting a future position of a vehicle using numerical integration[J]. Toyota Motor Engineering & Manufacturing.North America, Inc. 2008.
[45]陈青.基于GPS浮动车的城市道路交通状态判别技术研究[D].西安:长安大学, 2009.
[46] F. D. Anthony. A framework to transform real-time GPS data derived from transit vehicles to determine speed-flow characteristics of arterials[D]. Arlington: Texas University, 2003.
[47]吕宏义.基于支持向量回归机的路段平均速度短时预测方法研究[D].北京:北京交通大学, 2007.
[48] C. D. Fabritiis, R. Ragona,G. Valenti. Traffic Estimation and Prediction Based on Real Time Floating Car[C].11th International IEEE Conference on Intelligent Transportation Systems. Beijing, China, 2008.197-203
[49]杨兆升,于悦,杨薇.基于固定型检测器和浮动车的路段行程时间获取技术[J].吉林大学学报(工学版), 2009, 39(9): 168-171.
[50]杨兆升,杨庆芳等.路段平均速度组合融合算法及其应用[J].吉林大学学报(工学版), 2004, 34(4): 675-678.
[51]钱寒峰,林航飞,杨东援.浮动车车速处理分析系统中的数据融合技术[J].计算机工程与应用, 2007, 43(31): 230-232.
[52] X. Y. Liao, D. H. Sun, J. Y. Dong. The Fusing Algorithm of Link Speed based on Floating Vehicles Equipped with GPS[C]. World Congress on intelligent Control and Automation, Chongqing, China, 2008: 7676-7680.
[53]刘红红,杨兆升.基于数据融合技术的路段出行时间预测方法[J].交通运输工程学报, 2008, 8(6): 88-92.
[54]胡小文.基于探测车数据和定点检测器数据的路段行程时间估计[D].上海:同济大学, 2008.
[55] Q. Ou, J. W. C. van Lint, S. P. Hoogendoorn. An integrated algorithm for fusing travel times, local speed and flow[C]. 13th Conference on Information Fusion,Edinburgh, UK. 2010.1-8
[56] L. Wang, C. J. Wang, X. R. Shen, Y. Z. Fan. Probe vehicle sampling for real-time traffic data collection[C]. IEEE Proceeding of Intelligent Transportation System,2005: 886-888.
[57]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[J].交通运输系统工程与信息, 2006(4): 29-33.
[58] F. F. Zhang, Y. Wan. Speed estimation on freeways using floating cars equipped with GPS receivers[C]. International Conference on Transportation Engineering (ICTE),2007: 2936-2941.
[59]沙飞.基于浮动车技术的道路车辆行驶速度研究[D].北京:北京交通大学,2008.
[60]盛志杰,吴卉等.路网交通流区间平均速度的获取方法[P]. CN 1710624A,2005.
[61] S. H. Lee, B. W. Lee, Y. K. Yang. Estimation of link speed using pattern classification of GPS probe car data[C]: Computational Science and its Applications-ICCSA 2006, Glaskow, UK.495-504.
[62]吴卉.城市交通信息平台中地图匹配、车辆跟踪及速度估算的研究[D].上海:上海交通大学, 2006.
[63]李志鹏,刘允才.城市道路交通流平均速度获取方法[P]. CN 1937001A,2006.
[64] H. Shimizu, M. A. Kobayashi, H. Fujii, S. Katagiri. An Analysis of Mean Link Travel Time in Urban Road Networks and Its Applications[C]. SICE Annual Conference 2007: 1438-1443.
[65]秦玲,张剑飞,郭鹏,齐彤岩.浮动车交通信息采集与处理关键技术及其应用研究[J].交通运输系统工程与信息, 2007, 7(1): 39-42.
[66]韩舒.基于FCD的道路实时信息精度检验与改善放法研究[D].上海:同济大学, 2008.
[67] S. Sananmongkhonchai, P. Tangamchit, P. Pongpaibool. Road traffic estimation from multiple GPS data using incremental weighted update[C]: 8th International Conference on Intelligent Transport System Telecommunications(ITST), 2008: 62-66.
[68]李宇光,熊普选,乐阳.基于大样本浮动车数据的武汉市车辆行驶速度获取与分析[J].交通信息与安全, 2009, 27(4): 26-29.
[69] Y. I. Kim, M. H. Jung. Providing traffic information including average travel speed for a link and generation time[P]. EP2009170601.
[70] J. M. Ernst, M. Ndoye, J. V. Krogmeier, D.M. Bullock. Maximum-likelihood speedestimation using vehicle-induced magnetic signatures[C]. IEEE Conference on Intelligent Transportation Systems. 2009: 638-643.
[71]涂家伟.基于浮动车技术的道路交通特性分析及系统实现[D].天津:天津大学, 2009.
[72]姜桂艳,李继伟,张春勤.城市主干路拥挤路段基于地点交通参数的行程速度估计[J].吉林大学学报(工学版),2010, 40(5): 1203-1208.
[73] J. H. Guo, B. M. Williams. Real-Time Short-Term Traffic Speed Level Forecasting and Uncertainty Quantification Using Layered Kalman Filters[J]. Transportation Research Record: Journal of the Transportation Research Board 2010, 2175: 28-37.
[74]王殿海,祁宏生,李志慧.信号控制下的路段行程时间[J].吉林大学学报(工学版),2010, 40(3): 655-660.
[75]刘春,杨超,范业明.基于流动车数据的道路车速匹配与实时发布[J].武汉大学学报(信息科学版), 2010, 35(4): 394-398.
[76]王殿海,严宝杰.交通流理论[M].北京:人民交通出版社,2002.
[77]陆建,王炜.城市出租车拥有量确定方法[J].交通运输工程学报, 2004, 4(1): 92-95.
[78]翁剑成,刘文韬,陈智宏等.基于浮动车数据的出租车运营管理研究[J].北京工业大学学报, 2010, 36(6): 779-784.
[79]刘恩全.基于服务性车辆GPS数据的城市道路交通状态判别方法研究[D].长春;吉林大学, 2008.
[80]裴玉龙,张亚平.道路交通系统仿真[M].北京:人民交通出版社, 2004.
[81]姜桂艳,常安德,张玮.基于GPS浮动车的路段行程时间估计方法比较[J].吉林大学学报(工学版), 2009, 39(9): 182-186.
[82]孙棣华,廖孝勇,刘卫宁,赵敏,宋伟,周舒杰.城市路段交通流平均速度获取方法[P]. ZL200910104711.2.
[83]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.
[84]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.
[85]于春全,郭敏,梁玉庆.关于建立城市道路交通运行状况宏观评价系统的研究[J].道路交通与安全, 2007, 7(5): 1-6.
[86] J. Meier., H. Wehlan. Section-wise modeling of traffic flow and its application in traffic state estimation [C]. 2001 IEEE Intelligent Transportation Systems Conference Proceeding, USA, 2001: 440-445.
[87] B. S. Kerner, C. Demir., R. G. Herrtwich,et al. Traffic state detection with floating car data in road networks [C]. Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems, Vienna, Austria, 2005: 700-705.
[88]童小华,陈建阳.基于GIS和GPS的交通状态参数估计与仿真模型[J].同济大学学报(自然科学版), 2005, 33(12): 1604-1607.
[89]章威,徐建闽,王海峰.基于浮动车技术的城市路况计算方法[J].交通运输系统工程与信息, 2007, 7(1): 43-49.
[90]戢晓峰,刘澜,何增辉.基于模糊推理的区域路网交通状态分析方法[J].交通运输工程与信息学报, 2009, 9(3): 27-32
[91] R. L. Cheu, D. H. Lee, C. Xie. An arterial speed estimation model fusing data from stationary and mobile sensors [C]. Proc the IEEE Conference on Intelligent Transportation Systems. Oakland , CA , USA , 2001: 5732578-5732587.
[92]姜紫峰,刘小坤.基于神经网络的交通事件检测算法[J].西安公路交通大学学报, 2000, 20(3): 67-69.
[93]周伟,罗石贵.基于模糊综合识别的时间检测算法[J].西安公路交通大学学报, 2005, 21(3): 41-45.
[94]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, 21(3): 41-45.
[95] E. Yaser. Hawas. A fuzzy-based system for incident detection in urban street networks. Transportation Research Part C 2007, 15: 69-95.
[96]张和生,张毅,胡东成等.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, 47(1): 157-160.
[97]王伟,杨兆升,李贻武等.基于信息协同的子区交通状态加权计算与判别方法[J].吉林大学学报(工学版), 2007, 37(3): 524-527.
[98]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): 11-15.
[99]王力,王川久,张海等.基于浮动车的城市动态交通信息采集处理方法研究[C].第一届中国智能交通年会论文集, 2005.
[100]贺国光,冯蔚东.基于R/S分析研究交通流的长程相关性[J].系统工程学报, 2004(2): 19.
[101] T. Musha, H. Higuchi . Fluctuation of a traffic current on an expressway[J], Japanese Journal of Applied Physics. 1976, 15(7): 1271-1275.
[102] P. Sheng, S. L . Zhao, J. F. Wang, P. Tang, L. Gao. The effeet of stochastic aceelerationan Delay Probability on the velocity and the gap between vehieles in traffiec flow[J]. Chinese Physics B, 2009, 18(8):3347-3354.
[103] W. H. Wang, C. X. Yu, Y. Z. Wen, Y. H. Xu, B. L. Ling, X. Z. Gong, B. H. Liu, B. N. Wan. Experimental evidence of the self-similarity and long-range correlations of the edge fluetuations in HT-6M tokamak[J].Chinese Physies B, 2001, 10: 139-144.
[104]何文平,吴琼,张文,王启光,张勇.滑动去趋势波动分析与近似嫡在动力学结构突变检测中的性能比较[J].物理学报, 2009, 58: 2862-2871.
[105] T. Penzel, J. W. Kantelhardt, H. F. Becker, J. H. Peter, A. Bunde. Detrended Fluctuation Analysis and Spectral Analysis of Heart Rate Variabilityfor Sleep Stage and Sleep Apnea Identification[J]. Hospital of Philipps-University, Marburg, Germany. 2003, 9: 307-310.
[106]张斌等.元谋干热河谷近50年分季节降水变化的DFA分析[J].地理科学, 2009, 29(4):561-566
[107]贺国光,马寿峰,冯蔚东.交通流分形问题的初步研究[J].中国公路学报, 2002, 15(4): 82-85.
[108]李作敏,黄中祥,张亚平.高速公路交通流分形特性研究口[J].中国公路学报, 2000, 13(3): 82-85.
[109]张莉,于国海,马岩.分形理论在城市道路交通控制系统中的应用[J].东北林业大学学报, 2003, 31(2): 54-561.
[110]吴建军,徐尚义,孙会君.混合交通流时间序列的去趋势波动分析[J].物理学报, 2011, 60(1):019502-1-019502-7.
[111]于建玲,关积珍等.交通流的多重分形性质与交通拥堵关系的研究[C].第三届中国智能交通年会,南京, 2007.
[112]董科强,商朋见.除趋势交叉波动分析函数的统计特征[J].北京交通大学学报, 2010(6): 34.
[113] P. J. Shang , S. Kamae. Detecting long-range correlations of traffic time series with multifractal detrended fluctuation analysis, Chaos, Solitons & Fractals, 2008(36): 82–90.
[114] P. J. Shang, A. Lin , L. Liu. Chaotic SVD method for minimizing the effect of exponential trends in detrended fluctuation analysis, Physica. A 2009(388): 720–726.
[115] P. J. Shang et al. Emprical mode decomposition and correlation properties of traffic fluctuarion and noise letters, 2010, 9(2): 167–178.
[116] C. Peng, S. V. Buldyrev, S. Havlin, M. Simons, H. E. Stanley, A. L. Goldberger. Mosaic oranization of DNA nueleotides [J]. Physical Review E,1994, 49: 1685-1689.
[117]杨萍,侯威,封国林.基于去趋势波动分析方法确定极端事件阑值[J].物理学报, 2008, 57: 5333-5342.
[118] J. W. Kantelhardt, E. K. Bunde, H. A. Rego, S. Havlin, A. Bunde. Deteeting long-range correlations with detrended fluetuation analysis [J]. Physica A, 2001: 441-449.
[119] J. J. Wu, H. J. Sun, Z. Y. Gao. Long-range correlations of density fluetuations in the Kenler-Klenov-Wolf cellular automata three-Phase traffic flow model [J]. European Physical Journal B. 2008, 78: 036103.
[120] D. Ngoduy, S. P. Hoogendoorn, R. Liu. Continuum modeling of cooperative traffic flowdynamics[J]. Physica A. 2009, 388(13): 2705-2716.
[121] K. Shingo,T. Nagatani. Enhancement and stabilization of traffic flow by moving in groups[J]. Physical Review E 2001, 64(1):1-10.
[122] M. Bando, K. Hasebe, K. Nakanishi, A. Nakayama. Analysis of optimal velocity model with explicit delay[J]. Physical Review E, 1998, 58(5): 5429-5435.
[123]王涛,高自友,赵小梅.多速度差模型及稳定性分析[J].物理学报, 2006, 55(2): 634-640.
[124] L. Li, P. F. Shi. Phase transitions in a new car-following trafic flow model[J]. Chinese Physics. 2005, 14: 0576-0582.
[125] X. M. Zhao, Z. Y. Gao. A new car-following model: full velocity and acceleration difference model[J]. European Physical Journal B. 2005, 47: 145-150.
[126] Z. P. Li, Y. C. Liu. A Velocity-Difference-Separation Model for Car-Following Theory[J]. Chinese Physics. 2006, 15: 1570-1576.
[127] Z. P. Li, Y. C. Liu, F. Q. Liu. A Dynamical Model with Next-nearest-neighbor Interaction in Relative Velocity[J]. International Journal of Modern Physics C, 2007, 18: 819-832.
[128] Z. P. Li, X. B. Gong, Y. C. Liu. An Improved Car-Following Model for Multiphase Vehicular Traffic Flow and Numerical Tests[J]. Communications in Theoretical Physics, 2006, 46: 367-373.
[129] Z. P. Li, Y. C. Liu. Analysis of stability and density waves of traffic flow model in an ITS environment[J]. European Physical Journal B. 2006, 53: 367-374.
[130] Y. Lei, Z. K. Shi. Nonlinear analysis of an extended traffic flow model in ITS environment[J]. Chaos, Solitons and Fractals, 2008, 36: 550-558.
[131] H. X. Ge. Two velocity difference model for a car following theory[J]. Physica A, 2008, 360: 1-7.
[132] W. X. Zhu, Y. C. Liu. A total generalized optimal velocity model and its numerical tests[J]. J. Shanghai Jiaotong Univ. (Science), 2008, 13(2): 166-170.
[133] D. F. Xie, Z. Y. Gao, X. M. Zhao. Stabilization of traffic flow based on the multiple information of preceding cars[J]. Communications in Computational Physics, 2008, 3: 899-912.
[134] H. Lenz, C. K. Wagner, R. Sollacher. Multi-anticipative car-following model[J]. European Physical Journal B, 1999, 7(2): 331-335.
[135] K. Hasebe, A. Nakayama, Y. Sugiyama. Dynamical model of a cooperative driving system for freeway traffic[J]. Physical Review E, 2003, 68(2): 026102.1-026102.6
[136] H. X. Ge, S. Q. Dai, L. Y. Dong, Y. Xue. Stabilization effect of traffic flow in an extended car-following model based on an intelligent transportation system application[J]. PhysicalReview E. 2004, 70(6): 066134-066139
[137] Z. P. Li,Y. C. Liu. Analysis of stability and density waves of traffic flow model in an ITS environment[J]. European Physical Journal B. 2006, 53(3): 367-374.
[138]王涛,张晶.多速度差模型的交通流特性分析[J].系统工程理论与实践, 2008, (10): 150-155.
[139] W. X. Zhu,Y. C. Liu. A total generalized optimal velocity model and its numerical tests[J]. Journal of Shanghai Jiaotong University (English Edition), 2008: 166-70.
[140] D. F. Xie, Z. Y. Gao, X. M. Zha. Stabilization of traffic flow based on the multiple information of preceding cars[J]. Communications in Computational Physics, 2008, 3(4): 899-912.
[141]张晶,单宝明,王涛.交通流加速度与多速度差模型及稳定性分析[J].科学技术与工程, 2009, 9(17): 5249-5252.
[142]葛红霞,祝会兵,戴世强.智能交通系统的元胞自动机交通流模型[J].物理学报, 2005, 54(10): 4621-4626.
[143] M. Bando, K. Hasebe, A. Nakayama, A. Shibata, Y. Sugiyama. Dynamical model of traffic congestion and numerical simulation[J]. Physical Review E. 1995, 51:1035-1042.
[144] T. Komatasu, S. Sasa. Kink solution charactering traffic congestion[J]. Physical Review E. 1995, 52: 5574-5581.
[145] M. Muramstsu,T.Nagatani.Soliton and kink jams in traffic flow with open boundaries[J]. Physical Review E. 1999, 60: 180-187.
[146] T. Nagatani. Density waves in traffic flow[J]. Physical Review E. 2000, 61: 3564-3570.
[147] M. Treiber, A. Hennecke, D. Helbing. Congested traffic states in empirical observations and microscopic simulation[J]. Physical Review E. 2000, 62: 1805-1824.
[148] M. Treiber, D. Helbing. Macroscopic Simulation of Widely Scattered Synchronized Traffic States [J]. Physica A. 1999, 32: 17-23.
[149] D. Helbing, B. Tilch. Generalized force model of traffic dynamics[J]. Physical Review E. 1998, 58: 133-138.
[150] H. X. Ge, S. Q. DAI, Y. Xue, L. Y. Dong. Stabilization analysis and modified Korteweg–de Vries equation in a cooperative driving system[J]. Physical Review E. 2005, 71: 066119-066126.
[151] D. F. Xie, Z. Y. Gao, X. M. Zhao. Stabilization of traffic flow based on the multiple information of preceding cars[J]. Communications in Computational Physics.2008, 3: 899-912.
[152] G. H. Peng, D. H. Sun. Multiple car-following model of traffic flow and numericalsimulation[J]. Chinese Physics B. 2009, 18: 5420-5430.
[153] M. Treiber, A. Henneche, D. Helbing. Derivation, properties, and simulation of a gas-kinetic-based, nonlocal traffic model[J]. Physical Review E. 1999, 59(1): 239-253.
[154] H. X. Ge, R. H. Cheng, S. Q. Dai. KdV and kink-antikink solitons in car-following models[J]. Physica A. 2005, 357: 466-476.
[155] Z. P. Li, Y. C. Liu. Analysis of stability and density waves of traffic flow model in an ITS environment[J]. European Physical Journal B. 2006, 53: 367-374.
[156] H. X. Ge, H. B. Zhu, S. Q. Dai. Eeffect of looking backward on traffic flow in a cooperative driving car following model[J]. European Physical Journal B. 2006, 54: 503-507.
[157] Y. Lei, Z. K. Shi. Nonlinear analysis of an extended traffic flow model in ITS environment[J]. Chaos, Solitons and Fractals. 2008, 36: 550-558.
[158] R. Jiang, Q. S. Wu, Z. J. Zhu. Full velocity difference model for a car-following theory [J]. Physical Review E. 2001, 64: 017101.1-017101.4.
[159]薛郁.随机计及相对速度的交通流跟驰模型[J].物理学报, 2003, 52: 2750-2757.
[160] Y. Xue. Analysis of the stability and density waves for traffic flow[J]. Chinese Physics, 2002.11:1128-1137.
[161] R. Herman, R. B. Potts. Single lane traffic theory and experiment[C]. In Proceedings- Symposium on theory of traffic flow. Elsevier, New York, 1961.
[162] L. Y. Dong, Q. X. Meng. Effect of the relative on the optimal velocity model[J]. Journal of Shanghai University(English Edition). 2005, 9(4): 283-285.
[163] H. X. Ge, S. Q. Dai, Y. Xue, L. Y. Dong. Stabilization analysis and modified Korteweg–de Vries equation in a cooperative driving system[J]. Physical Review E. 2005, 71: 066119-1-7.
[164]葛红霞.基于诱导信息的交通流动力学特性与非线性密度波研究[D].上海:上海大学, 2006.
[165] R. Mahnke, J. Kaupuzs, I. Lubashevsky. Probabilistic description of traffic flow[J]. Physics Reports. 2005, 408: 1-130.
[166]付传技.交通流模型的研究[D].合肥:中国科学技术大学, 2007.
[167] H. X. Gong, H. C. Liu, B. H. Wang. An asymmetric full velocity difference car-following model[J]. Physica A. 2008, 387: 2595–2602.
[168]廖孝勇,孙棣华,彭光含.双前导车信息对交通流稳定性影响的分析[J].重庆大学学报(自然科学版), 2011, 34(8): 13-17.
[169] D. H. Sun, X. Y. Liao, G. H. Peng. Effect of looking backward on traffic flow in an extended multiple car-following model[J]. Physica A, 2011, 390(4): 631-635.
[170]彭光含.交通流复杂耦合动态特性模拟研究[D].重庆:重庆大学, 2009.
[2]张其善,吴今培,杨东凯.智能交通车辆定位导航系统及其应用[M].北京:科学出版社, 2002.
[3]田欢欢,薛郁,康三军,梁玉娟.元胞自动机混合交通流模型的能耗研究[J].物理学报, 2009, 58(7): 4506-4513.
[4]陆化普.城市交通现代化管理[M].北京:人民交通出版社, 1999.
[5]董均宇.基于GPS浮动车的城市路段平均速度估计技术研究[D].重庆:重庆大学, 2006.
[6] D. J. Dailey, S. Maclean, F. W. Cattey, D. Meyers. A self-describing data transfer model for ITS applications[J]. Intelligent Transportation Systems, 2002: 293-300.
[7]张剑平,任福继.地理信息系统与MapInfo应用[M].北京:科学出版社, 1999.
[8]齐锐,屈韶林,阳林赟.用MapX开发地理信息系统[M].北京:清华大学出版社, 2003.
[9] B. Ding, K. C. Wong. A System for automatic extraction of road network from maps[C]. Proc. IEEE International Joint Symposia on Intelligence and Systems, Rockville, USA. 1998: 359–366.
[10]屈静,鲍远律.交通矢量地图离散非线性校正算法[J].计算机仿真, 2006, 23(5): 77-80.
[11]段凌宇,鲍远律,张旺生. GPS Vehicle Navigation System [J].南京航空航天大学学报(英文版), 1998, 15(2): 172-178.
[12] Y. L. Bao, G. J. Wang, J. M. Ye. OverallDesign of Xiamen City GPS Intelligent Vehicle Monitoring System [C] . ICCA02, Xiamen, China, 2002: 887-891.
[13]苏春光,徐杰,王鸿谷.地图及工程图纸的智能矢量化方法[J].小型微型计算机系统, 1996, 5(17): 6-11.
[14]林治远等. CJJ37-90城市道路设计规范[M].北京:中国建筑工业出版社, 1990.
[15]徐循初等. GB50220-95城市道路交通规划设计规范[M].北京:中国建筑工业出版社, 1995.
[16]肖锋.面向道路交通状态监测的GPS与GIS数据预处理关键技术研究[D].重庆:重庆大学,2008
[17] R. Danescu, S. Nedevschi. Probabilistic Lane Tracking in Difficult RoadScenarios Using Stereovision[J]. IEEE Transactions on Intelligent Transportation Systems, 2009, 10(2): 272-282.
[18] F. Oniga, S. Nedevschi. Processing Dense Stereo Data Using Elevation Maps: Road Surface, Trafic Isle and Obstacle Detection[J]. IEEE Transactions on Vehicular Technology, 2010,59(3): 1172-1182.
[19] S. Theodoridis, K. Koutroumbas. Pattern recognition[M]. 2-nd edition, Elsevier Academic Press, 2003.
[20]朱振兴.从栅格数据直接建立拓扑关系的算法研究[J].环境遥感.1993, 8(3): 232-240.
[21]郭杰华,姚振旺,鲍远律,张旺生.矢量地图的一种自动校正算法[J].中国图像图形学报, 1999, 4(5): 423-426.
[22]张安胜,薛俊萍,钱辉环.一种基于几何变换的地图校正方法[J].微机发展, 2002(3): 30- 33.
[23]徐东平,周师盛,钟涛.基于矩阵变换的矢量地图校正算法[J].武汉理工大学学报(交通科学与工程版).2004,28(2) :274-277.
[24]杨凌,常江龙.一种基于道路知识的矢量地图数据校正方法[J].计算机仿真. 2008, 25(5): 230-233.
[25] Macqueen. Some methods for classification and analysis of multivariate observations [C]. Proc.5th Berkeley Symp.Math.Statist, 1967, (1): 281-297.
[26] R. R. He, L. K. Alain., B. Ran. Map Alignment Using Probe Vehicle Data[CD]. TRB 2003 Annual Meeting, America, 2003: 1-25.
[27] J. L. Martínez, M. A. Martínez. A new method of generating differential GPS corrections[J]. Control Engineering Practice, 2000, 8(3): 253-258.
[28]鲍远律,夏冰,鲍远慧. GPS车辆监控系统开发的关键技术[J].中国公路.交通信息产业, 2001,2(3):42-44.
[29]丁军.关于置信椭圆的讨论[J].测绘通报, 1990, (4): 21-23.
[30]杜江平.基于GPS/GIS车辆定位导航系统的研究[D].成都:电子科技大学, 2009.
[31]廖孝勇,孙棣华,周舒杰,田川,古曦.混合浮动车交通状态估计的样本数量研究[J].计算机工程与应用. 2011,47(24):229-232.
[32]周舒杰.基于公交浮动车的道路平均速度估计研究[D].重庆:重庆大学, 2009.
[33]戴冀峰,马健霄等.交通工程概论[M].北京:人民交通出版社, 2006.
[34] B. Rohini. Predicting speeds on urban streets using real-time GPS data[J]. Masters Abstracts International, 2000, 41(04): 1137.
[35] F. D. Anthony. A framework to transform real-time GPS data derived from transit vehicles to determine speed-flow characteristics of arterials[J]. Dissertation Abstracts International, Section: B, 2002, 64(12): 6215.
[36]陈永恒,王殿海,等.无物理隔离路段机动车与非机动车速度特性研究[J].交通运输系统工程与信息, 2009, 9(5): 53-57.
[37] K. L. Ding, H. T. Wang. A New algorithms for dynamic traffic information collection[C].IEEE Computer Society, 2009 Second International Conference on Intelligent Computation Technology and Automation, 2009: 437-439.
[38] Y. Y. Li, M. McDonald. Link travel time estimation using single GPS equipped probe vehicle[C]. Proceedings of Intelligent Transportation Systems, 2002: 932-937.
[39] A. D. Chang, G. Y. Jiang, S. F. Niu. Trffic Congestion Identification Method Based on GPS Equipped Floating Car[J]. 2010 IEEE computer socirty, 2010: 1069-1071.
[40] C. A. Quiroga, D. Bulloek. Travel Time Studies With Global Positioning and Geographic Information Systems: An Integrated Methodology[J]. Transportation Research Part C, 1998.
[41]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[J].计算机应用研究, 2007, 2: 243-245.
[42]李筱菁,孟庆春,魏振钢,魏天滨,王旭柱,郭忠文,丁鹏. GPS技术在城市交通状况实时检测技术中的应用[J].青岛海洋大学学报, 2002, 32(3): 475-481.
[43]王文辉.基于浮动车的道路交通状况评估中信息融合应用研究[D].北京:北京交通大学, 2008.
[44] Caveney, S. Derek. Method and system for predicting a future position of a vehicle using numerical integration[J]. Toyota Motor Engineering & Manufacturing.North America, Inc. 2008.
[45]陈青.基于GPS浮动车的城市道路交通状态判别技术研究[D].西安:长安大学, 2009.
[46] F. D. Anthony. A framework to transform real-time GPS data derived from transit vehicles to determine speed-flow characteristics of arterials[D]. Arlington: Texas University, 2003.
[47]吕宏义.基于支持向量回归机的路段平均速度短时预测方法研究[D].北京:北京交通大学, 2007.
[48] C. D. Fabritiis, R. Ragona,G. Valenti. Traffic Estimation and Prediction Based on Real Time Floating Car[C].11th International IEEE Conference on Intelligent Transportation Systems. Beijing, China, 2008.197-203
[49]杨兆升,于悦,杨薇.基于固定型检测器和浮动车的路段行程时间获取技术[J].吉林大学学报(工学版), 2009, 39(9): 168-171.
[50]杨兆升,杨庆芳等.路段平均速度组合融合算法及其应用[J].吉林大学学报(工学版), 2004, 34(4): 675-678.
[51]钱寒峰,林航飞,杨东援.浮动车车速处理分析系统中的数据融合技术[J].计算机工程与应用, 2007, 43(31): 230-232.
[52] X. Y. Liao, D. H. Sun, J. Y. Dong. The Fusing Algorithm of Link Speed based on Floating Vehicles Equipped with GPS[C]. World Congress on intelligent Control and Automation, Chongqing, China, 2008: 7676-7680.
[53]刘红红,杨兆升.基于数据融合技术的路段出行时间预测方法[J].交通运输工程学报, 2008, 8(6): 88-92.
[54]胡小文.基于探测车数据和定点检测器数据的路段行程时间估计[D].上海:同济大学, 2008.
[55] Q. Ou, J. W. C. van Lint, S. P. Hoogendoorn. An integrated algorithm for fusing travel times, local speed and flow[C]. 13th Conference on Information Fusion,Edinburgh, UK. 2010.1-8
[56] L. Wang, C. J. Wang, X. R. Shen, Y. Z. Fan. Probe vehicle sampling for real-time traffic data collection[C]. IEEE Proceeding of Intelligent Transportation System,2005: 886-888.
[57]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[J].交通运输系统工程与信息, 2006(4): 29-33.
[58] F. F. Zhang, Y. Wan. Speed estimation on freeways using floating cars equipped with GPS receivers[C]. International Conference on Transportation Engineering (ICTE),2007: 2936-2941.
[59]沙飞.基于浮动车技术的道路车辆行驶速度研究[D].北京:北京交通大学,2008.
[60]盛志杰,吴卉等.路网交通流区间平均速度的获取方法[P]. CN 1710624A,2005.
[61] S. H. Lee, B. W. Lee, Y. K. Yang. Estimation of link speed using pattern classification of GPS probe car data[C]: Computational Science and its Applications-ICCSA 2006, Glaskow, UK.495-504.
[62]吴卉.城市交通信息平台中地图匹配、车辆跟踪及速度估算的研究[D].上海:上海交通大学, 2006.
[63]李志鹏,刘允才.城市道路交通流平均速度获取方法[P]. CN 1937001A,2006.
[64] H. Shimizu, M. A. Kobayashi, H. Fujii, S. Katagiri. An Analysis of Mean Link Travel Time in Urban Road Networks and Its Applications[C]. SICE Annual Conference 2007: 1438-1443.
[65]秦玲,张剑飞,郭鹏,齐彤岩.浮动车交通信息采集与处理关键技术及其应用研究[J].交通运输系统工程与信息, 2007, 7(1): 39-42.
[66]韩舒.基于FCD的道路实时信息精度检验与改善放法研究[D].上海:同济大学, 2008.
[67] S. Sananmongkhonchai, P. Tangamchit, P. Pongpaibool. Road traffic estimation from multiple GPS data using incremental weighted update[C]: 8th International Conference on Intelligent Transport System Telecommunications(ITST), 2008: 62-66.
[68]李宇光,熊普选,乐阳.基于大样本浮动车数据的武汉市车辆行驶速度获取与分析[J].交通信息与安全, 2009, 27(4): 26-29.
[69] Y. I. Kim, M. H. Jung. Providing traffic information including average travel speed for a link and generation time[P]. EP2009170601.
[70] J. M. Ernst, M. Ndoye, J. V. Krogmeier, D.M. Bullock. Maximum-likelihood speedestimation using vehicle-induced magnetic signatures[C]. IEEE Conference on Intelligent Transportation Systems. 2009: 638-643.
[71]涂家伟.基于浮动车技术的道路交通特性分析及系统实现[D].天津:天津大学, 2009.
[72]姜桂艳,李继伟,张春勤.城市主干路拥挤路段基于地点交通参数的行程速度估计[J].吉林大学学报(工学版),2010, 40(5): 1203-1208.
[73] J. H. Guo, B. M. Williams. Real-Time Short-Term Traffic Speed Level Forecasting and Uncertainty Quantification Using Layered Kalman Filters[J]. Transportation Research Record: Journal of the Transportation Research Board 2010, 2175: 28-37.
[74]王殿海,祁宏生,李志慧.信号控制下的路段行程时间[J].吉林大学学报(工学版),2010, 40(3): 655-660.
[75]刘春,杨超,范业明.基于流动车数据的道路车速匹配与实时发布[J].武汉大学学报(信息科学版), 2010, 35(4): 394-398.
[76]王殿海,严宝杰.交通流理论[M].北京:人民交通出版社,2002.
[77]陆建,王炜.城市出租车拥有量确定方法[J].交通运输工程学报, 2004, 4(1): 92-95.
[78]翁剑成,刘文韬,陈智宏等.基于浮动车数据的出租车运营管理研究[J].北京工业大学学报, 2010, 36(6): 779-784.
[79]刘恩全.基于服务性车辆GPS数据的城市道路交通状态判别方法研究[D].长春;吉林大学, 2008.
[80]裴玉龙,张亚平.道路交通系统仿真[M].北京:人民交通出版社, 2004.
[81]姜桂艳,常安德,张玮.基于GPS浮动车的路段行程时间估计方法比较[J].吉林大学学报(工学版), 2009, 39(9): 182-186.
[82]孙棣华,廖孝勇,刘卫宁,赵敏,宋伟,周舒杰.城市路段交通流平均速度获取方法[P]. ZL200910104711.2.
[83]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.
[84]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.
[85]于春全,郭敏,梁玉庆.关于建立城市道路交通运行状况宏观评价系统的研究[J].道路交通与安全, 2007, 7(5): 1-6.
[86] J. Meier., H. Wehlan. Section-wise modeling of traffic flow and its application in traffic state estimation [C]. 2001 IEEE Intelligent Transportation Systems Conference Proceeding, USA, 2001: 440-445.
[87] B. S. Kerner, C. Demir., R. G. Herrtwich,et al. Traffic state detection with floating car data in road networks [C]. Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems, Vienna, Austria, 2005: 700-705.
[88]童小华,陈建阳.基于GIS和GPS的交通状态参数估计与仿真模型[J].同济大学学报(自然科学版), 2005, 33(12): 1604-1607.
[89]章威,徐建闽,王海峰.基于浮动车技术的城市路况计算方法[J].交通运输系统工程与信息, 2007, 7(1): 43-49.
[90]戢晓峰,刘澜,何增辉.基于模糊推理的区域路网交通状态分析方法[J].交通运输工程与信息学报, 2009, 9(3): 27-32
[91] R. L. Cheu, D. H. Lee, C. Xie. An arterial speed estimation model fusing data from stationary and mobile sensors [C]. Proc the IEEE Conference on Intelligent Transportation Systems. Oakland , CA , USA , 2001: 5732578-5732587.
[92]姜紫峰,刘小坤.基于神经网络的交通事件检测算法[J].西安公路交通大学学报, 2000, 20(3): 67-69.
[93]周伟,罗石贵.基于模糊综合识别的时间检测算法[J].西安公路交通大学学报, 2005, 21(3): 41-45.
[94]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, 21(3): 41-45.
[95] E. Yaser. Hawas. A fuzzy-based system for incident detection in urban street networks. Transportation Research Part C 2007, 15: 69-95.
[96]张和生,张毅,胡东成等.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, 47(1): 157-160.
[97]王伟,杨兆升,李贻武等.基于信息协同的子区交通状态加权计算与判别方法[J].吉林大学学报(工学版), 2007, 37(3): 524-527.
[98]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): 11-15.
[99]王力,王川久,张海等.基于浮动车的城市动态交通信息采集处理方法研究[C].第一届中国智能交通年会论文集, 2005.
[100]贺国光,冯蔚东.基于R/S分析研究交通流的长程相关性[J].系统工程学报, 2004(2): 19.
[101] T. Musha, H. Higuchi . Fluctuation of a traffic current on an expressway[J], Japanese Journal of Applied Physics. 1976, 15(7): 1271-1275.
[102] P. Sheng, S. L . Zhao, J. F. Wang, P. Tang, L. Gao. The effeet of stochastic aceelerationan Delay Probability on the velocity and the gap between vehieles in traffiec flow[J]. Chinese Physics B, 2009, 18(8):3347-3354.
[103] W. H. Wang, C. X. Yu, Y. Z. Wen, Y. H. Xu, B. L. Ling, X. Z. Gong, B. H. Liu, B. N. Wan. Experimental evidence of the self-similarity and long-range correlations of the edge fluetuations in HT-6M tokamak[J].Chinese Physies B, 2001, 10: 139-144.
[104]何文平,吴琼,张文,王启光,张勇.滑动去趋势波动分析与近似嫡在动力学结构突变检测中的性能比较[J].物理学报, 2009, 58: 2862-2871.
[105] T. Penzel, J. W. Kantelhardt, H. F. Becker, J. H. Peter, A. Bunde. Detrended Fluctuation Analysis and Spectral Analysis of Heart Rate Variabilityfor Sleep Stage and Sleep Apnea Identification[J]. Hospital of Philipps-University, Marburg, Germany. 2003, 9: 307-310.
[106]张斌等.元谋干热河谷近50年分季节降水变化的DFA分析[J].地理科学, 2009, 29(4):561-566
[107]贺国光,马寿峰,冯蔚东.交通流分形问题的初步研究[J].中国公路学报, 2002, 15(4): 82-85.
[108]李作敏,黄中祥,张亚平.高速公路交通流分形特性研究口[J].中国公路学报, 2000, 13(3): 82-85.
[109]张莉,于国海,马岩.分形理论在城市道路交通控制系统中的应用[J].东北林业大学学报, 2003, 31(2): 54-561.
[110]吴建军,徐尚义,孙会君.混合交通流时间序列的去趋势波动分析[J].物理学报, 2011, 60(1):019502-1-019502-7.
[111]于建玲,关积珍等.交通流的多重分形性质与交通拥堵关系的研究[C].第三届中国智能交通年会,南京, 2007.
[112]董科强,商朋见.除趋势交叉波动分析函数的统计特征[J].北京交通大学学报, 2010(6): 34.
[113] P. J. Shang , S. Kamae. Detecting long-range correlations of traffic time series with multifractal detrended fluctuation analysis, Chaos, Solitons & Fractals, 2008(36): 82–90.
[114] P. J. Shang, A. Lin , L. Liu. Chaotic SVD method for minimizing the effect of exponential trends in detrended fluctuation analysis, Physica. A 2009(388): 720–726.
[115] P. J. Shang et al. Emprical mode decomposition and correlation properties of traffic fluctuarion and noise letters, 2010, 9(2): 167–178.
[116] C. Peng, S. V. Buldyrev, S. Havlin, M. Simons, H. E. Stanley, A. L. Goldberger. Mosaic oranization of DNA nueleotides [J]. Physical Review E,1994, 49: 1685-1689.
[117]杨萍,侯威,封国林.基于去趋势波动分析方法确定极端事件阑值[J].物理学报, 2008, 57: 5333-5342.
[118] J. W. Kantelhardt, E. K. Bunde, H. A. Rego, S. Havlin, A. Bunde. Deteeting long-range correlations with detrended fluetuation analysis [J]. Physica A, 2001: 441-449.
[119] J. J. Wu, H. J. Sun, Z. Y. Gao. Long-range correlations of density fluetuations in the Kenler-Klenov-Wolf cellular automata three-Phase traffic flow model [J]. European Physical Journal B. 2008, 78: 036103.
[120] D. Ngoduy, S. P. Hoogendoorn, R. Liu. Continuum modeling of cooperative traffic flowdynamics[J]. Physica A. 2009, 388(13): 2705-2716.
[121] K. Shingo,T. Nagatani. Enhancement and stabilization of traffic flow by moving in groups[J]. Physical Review E 2001, 64(1):1-10.
[122] M. Bando, K. Hasebe, K. Nakanishi, A. Nakayama. Analysis of optimal velocity model with explicit delay[J]. Physical Review E, 1998, 58(5): 5429-5435.
[123]王涛,高自友,赵小梅.多速度差模型及稳定性分析[J].物理学报, 2006, 55(2): 634-640.
[124] L. Li, P. F. Shi. Phase transitions in a new car-following trafic flow model[J]. Chinese Physics. 2005, 14: 0576-0582.
[125] X. M. Zhao, Z. Y. Gao. A new car-following model: full velocity and acceleration difference model[J]. European Physical Journal B. 2005, 47: 145-150.
[126] Z. P. Li, Y. C. Liu. A Velocity-Difference-Separation Model for Car-Following Theory[J]. Chinese Physics. 2006, 15: 1570-1576.
[127] Z. P. Li, Y. C. Liu, F. Q. Liu. A Dynamical Model with Next-nearest-neighbor Interaction in Relative Velocity[J]. International Journal of Modern Physics C, 2007, 18: 819-832.
[128] Z. P. Li, X. B. Gong, Y. C. Liu. An Improved Car-Following Model for Multiphase Vehicular Traffic Flow and Numerical Tests[J]. Communications in Theoretical Physics, 2006, 46: 367-373.
[129] Z. P. Li, Y. C. Liu. Analysis of stability and density waves of traffic flow model in an ITS environment[J]. European Physical Journal B. 2006, 53: 367-374.
[130] Y. Lei, Z. K. Shi. Nonlinear analysis of an extended traffic flow model in ITS environment[J]. Chaos, Solitons and Fractals, 2008, 36: 550-558.
[131] H. X. Ge. Two velocity difference model for a car following theory[J]. Physica A, 2008, 360: 1-7.
[132] W. X. Zhu, Y. C. Liu. A total generalized optimal velocity model and its numerical tests[J]. J. Shanghai Jiaotong Univ. (Science), 2008, 13(2): 166-170.
[133] D. F. Xie, Z. Y. Gao, X. M. Zhao. Stabilization of traffic flow based on the multiple information of preceding cars[J]. Communications in Computational Physics, 2008, 3: 899-912.
[134] H. Lenz, C. K. Wagner, R. Sollacher. Multi-anticipative car-following model[J]. European Physical Journal B, 1999, 7(2): 331-335.
[135] K. Hasebe, A. Nakayama, Y. Sugiyama. Dynamical model of a cooperative driving system for freeway traffic[J]. Physical Review E, 2003, 68(2): 026102.1-026102.6
[136] H. X. Ge, S. Q. Dai, L. Y. Dong, Y. Xue. Stabilization effect of traffic flow in an extended car-following model based on an intelligent transportation system application[J]. PhysicalReview E. 2004, 70(6): 066134-066139
[137] Z. P. Li,Y. C. Liu. Analysis of stability and density waves of traffic flow model in an ITS environment[J]. European Physical Journal B. 2006, 53(3): 367-374.
[138]王涛,张晶.多速度差模型的交通流特性分析[J].系统工程理论与实践, 2008, (10): 150-155.
[139] W. X. Zhu,Y. C. Liu. A total generalized optimal velocity model and its numerical tests[J]. Journal of Shanghai Jiaotong University (English Edition), 2008: 166-70.
[140] D. F. Xie, Z. Y. Gao, X. M. Zha. Stabilization of traffic flow based on the multiple information of preceding cars[J]. Communications in Computational Physics, 2008, 3(4): 899-912.
[141]张晶,单宝明,王涛.交通流加速度与多速度差模型及稳定性分析[J].科学技术与工程, 2009, 9(17): 5249-5252.
[142]葛红霞,祝会兵,戴世强.智能交通系统的元胞自动机交通流模型[J].物理学报, 2005, 54(10): 4621-4626.
[143] M. Bando, K. Hasebe, A. Nakayama, A. Shibata, Y. Sugiyama. Dynamical model of traffic congestion and numerical simulation[J]. Physical Review E. 1995, 51:1035-1042.
[144] T. Komatasu, S. Sasa. Kink solution charactering traffic congestion[J]. Physical Review E. 1995, 52: 5574-5581.
[145] M. Muramstsu,T.Nagatani.Soliton and kink jams in traffic flow with open boundaries[J]. Physical Review E. 1999, 60: 180-187.
[146] T. Nagatani. Density waves in traffic flow[J]. Physical Review E. 2000, 61: 3564-3570.
[147] M. Treiber, A. Hennecke, D. Helbing. Congested traffic states in empirical observations and microscopic simulation[J]. Physical Review E. 2000, 62: 1805-1824.
[148] M. Treiber, D. Helbing. Macroscopic Simulation of Widely Scattered Synchronized Traffic States [J]. Physica A. 1999, 32: 17-23.
[149] D. Helbing, B. Tilch. Generalized force model of traffic dynamics[J]. Physical Review E. 1998, 58: 133-138.
[150] H. X. Ge, S. Q. DAI, Y. Xue, L. Y. Dong. Stabilization analysis and modified Korteweg–de Vries equation in a cooperative driving system[J]. Physical Review E. 2005, 71: 066119-066126.
[151] D. F. Xie, Z. Y. Gao, X. M. Zhao. Stabilization of traffic flow based on the multiple information of preceding cars[J]. Communications in Computational Physics.2008, 3: 899-912.
[152] G. H. Peng, D. H. Sun. Multiple car-following model of traffic flow and numericalsimulation[J]. Chinese Physics B. 2009, 18: 5420-5430.
[153] M. Treiber, A. Henneche, D. Helbing. Derivation, properties, and simulation of a gas-kinetic-based, nonlocal traffic model[J]. Physical Review E. 1999, 59(1): 239-253.
[154] H. X. Ge, R. H. Cheng, S. Q. Dai. KdV and kink-antikink solitons in car-following models[J]. Physica A. 2005, 357: 466-476.
[155] Z. P. Li, Y. C. Liu. Analysis of stability and density waves of traffic flow model in an ITS environment[J]. European Physical Journal B. 2006, 53: 367-374.
[156] H. X. Ge, H. B. Zhu, S. Q. Dai. Eeffect of looking backward on traffic flow in a cooperative driving car following model[J]. European Physical Journal B. 2006, 54: 503-507.
[157] Y. Lei, Z. K. Shi. Nonlinear analysis of an extended traffic flow model in ITS environment[J]. Chaos, Solitons and Fractals. 2008, 36: 550-558.
[158] R. Jiang, Q. S. Wu, Z. J. Zhu. Full velocity difference model for a car-following theory [J]. Physical Review E. 2001, 64: 017101.1-017101.4.
[159]薛郁.随机计及相对速度的交通流跟驰模型[J].物理学报, 2003, 52: 2750-2757.
[160] Y. Xue. Analysis of the stability and density waves for traffic flow[J]. Chinese Physics, 2002.11:1128-1137.
[161] R. Herman, R. B. Potts. Single lane traffic theory and experiment[C]. In Proceedings- Symposium on theory of traffic flow. Elsevier, New York, 1961.
[162] L. Y. Dong, Q. X. Meng. Effect of the relative on the optimal velocity model[J]. Journal of Shanghai University(English Edition). 2005, 9(4): 283-285.
[163] H. X. Ge, S. Q. Dai, Y. Xue, L. Y. Dong. Stabilization analysis and modified Korteweg–de Vries equation in a cooperative driving system[J]. Physical Review E. 2005, 71: 066119-1-7.
[164]葛红霞.基于诱导信息的交通流动力学特性与非线性密度波研究[D].上海:上海大学, 2006.
[165] R. Mahnke, J. Kaupuzs, I. Lubashevsky. Probabilistic description of traffic flow[J]. Physics Reports. 2005, 408: 1-130.
[166]付传技.交通流模型的研究[D].合肥:中国科学技术大学, 2007.
[167] H. X. Gong, H. C. Liu, B. H. Wang. An asymmetric full velocity difference car-following model[J]. Physica A. 2008, 387: 2595–2602.
[168]廖孝勇,孙棣华,彭光含.双前导车信息对交通流稳定性影响的分析[J].重庆大学学报(自然科学版), 2011, 34(8): 13-17.
[169] D. H. Sun, X. Y. Liao, G. H. Peng. Effect of looking backward on traffic flow in an extended multiple car-following model[J]. Physica A, 2011, 390(4): 631-635.
[170]彭光含.交通流复杂耦合动态特性模拟研究[D].重庆:重庆大学, 2009.