基于ITS体系结构的实时交通控制CPN建模仿真
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摘要
城市智能交通系统是一个由诸多异构系统集结而成的复杂系统，一直是国内外学者研究的热点。合理高效的城市智能交通系统建设不仅有助于提升现有交通体系能力，而且能有效改善城市的生存环境，产生可观的城市社会效益。
本文遵循现代复杂工程系统研究的五个基准：过程、体系架构、方法论、建模与评价，以城市智能交通系统中的交通信号控制系统为主要研究对象，在对国内外城市交通信号控制系统研究现状及发展进行分析的基础上，较系统地研究了城市交通信号控制的若干关键议题；重点研究并提出了一种新型的道路车辆数据采集系统的车辆检测传感器；研究建立符合城市智能交通系统体系结构框架标准的层次化交通信号控制系统建模方法，由此建立了基于层次着色Petri网（HCPN: HierarchicalColored Petri Nets）的城市交通流模型；基于HCPN交通流模型，研究并提出了一种新的实时自适应城市交通信号控制算法。
论文的主要内容包括以下几个方面：
研究并开发了一种基于各向异性磁阻（AMR:Anisotropic Magnetoresistive）传感器的车辆检测系统，提出了基于AMR车辆检测器的车辆检测分类方法。为了获得城市交通流实时路况数据，在讨论各种车辆检测方法的基础上，深入研究了AMR传感器的数据采集及车辆分类的原理，给出了代表车型的主要特征以及由此组成的特征向量的提取方法，然后，采用支持向量机（SVM: Support Vector Machine）分类学习算法检测车辆及进行车型分类。并从核函数，模型参数等方面对其分类性能进行了讨论。实验结果显示，该车辆检测方法具有长期稳定性的特点，不受外在天气环境路况的影响，有较强的实用性。进一步，以AMR车辆检测器为节点，应用高效率、低成本的串行接口总线，提出了网络化城市道路车辆检测传感器系统的建设构想。
研究城市交通系统体系结构和交通流理论的基础上，应用着色Petri网，研究建立符合城市智能交通系统体系结构框架标准的层次化交通信号控制系统建模方法，提出顶层为操作视图、中层为系统视图、底层为技术标准视图的城市ITS（IntelligentTransportation Systems）体系结构框架。论文依据城市交通流的特点，提出了城市交通流的层次模型。该层次模型由交通路网层，网络构件层，逻辑控制层等三层组成，实现了交通流与交通信号控制的无缝链接。基于ITS体系结构和层次着色Petri网，依据实际的道路参数，采用自顶而下的建模方法，依次建立各层子模型。然后，论文通过城市交通流着色Petri网建模实例，进一步介绍了交通流着色Petri网建模过程，讨论了分层结构所具有的易扩展性，验证了分层模型在解决交通流建模复杂性问题上的显著成效。
通过分析城市交通流模型，在重点考虑算法实时性和自适应性的前提下，分别提出了单交叉口和多交叉口的信号控制算法。首先分析了单交叉口的交通流特征以及短时交通流预测，以优化车辆延误为目标，提出了基于规则的单交叉口实时自适应控制算法。然后研究了多交叉口之间交通流的相互影响，以相邻交叉口中间路段上的车流量为协调变量，实现了基于规则的多交叉口两级协调控制算法。最后结合文章提出的HCPN交通流模型，建立了交通信号控制器的CPN模型，以及交通信号控制的评价指标，通过模型仿真，完成了基于规则的城市交通信号优化协调控制策略的评价研究。仿真结果证明了该实时自适应协调控制方法及其模型的正确性和实用性。
Urban traffic signal control system has been a hot topic of research by domestic andforeign scholars. Rational and efficient urban traffic signal control system does not onlyhelp to improve the existing transportation system performance, but also effectivelyimprove the living environment of the city and produce considerable social benefits for thecity.
According to complex engineering system benchmarks research areas: the process,the architecture frameworks (AFs), the methodologies, the modeling and evaluation.Based on the analysis of current urban traffic signal control system's research situation anddevelopment trends at home and abroad, a number of key technologies of urban trafficsignal control are systematically studied and discussed in this paper, especially research onvihicle detection and classification system, model of urban traffic flow based on ITSarchitecture and Hierarchical Colored Petri Nets(HCPN), and a new real-time adaptiveurban traffic signal control algorithm.
The paper mainly covers the following areas:
Firstly, a novel vehicle detection and classification system based on anisotropicmagnetoresistive (AMR) sensors and support vector machine (SVM) algorithm ispresented. The AMR sensors detect the change of earth magnetic field which will bedisturbed differently by different types of passing traffic vehicle. With the featuresextracted from the disturbance data of earth magnetic field, SVM classifier will be trainedand tested to classify the vehicle type. The results of our experiments indicated thatvehicle classification based on AMR sensors and SVM algorithm is effective and efficient.In addition, using AMR vehicle detection sensors as sensor network nodes, networkedsensors vehicle detection system based on the low cost and high-speed rs485serial bus ispresented.
Secondly, after researching on urban transportation system architecture and trafficflow theory, an urban ITS architecture and a hierarchical modeling approach for urbantraffic signal control system are established based on Colored Petri Nets, whicharchitecture inlcudes three levels: the top level-operational view, the middle-systemview, and the underlying-technical standard view. According to the characteristics ofurban traffic flow, a hierarchical structure model of urban traffic flow is presented, whichmodel consists of three levels, traffic network level, network conponents level and logic control level, and implements the seamless connection between traffic flow and logiccontroller. Based on hierarchical Colored Petri Nets theory and actual road parameters,models of every level are established using top-down modeling approach. Afterestablishing the hierarchical Colored Petri Nets model of a real traffic network forexample, introduced the design approach of Colored Petri Nets model, argued that themodel has good extensibility, and validated that the hierarchical structure model will makeus better to achieve remarkable results in complex problem solving tasks on traffic flowmodeling.
Finally, according to the analysis of urban traffic flow model and mainly taking thecapability of real-time and adaptive control into account, traffic signal control algorithmsfor isolated intersection and area traffic are presented respectively. Based on the trafficflow characteristics of an isolated intersection and precdiction for short-term traffic flow,aiming to minimize the vehicle delay, a rule-based traffic signal control algorithm ispresented. After analyzing the relationship of traffic flow between adjacent intersections, arule-based coordination control algorithm is presented with traffic volume in the linkbetween adjacent intersections as the coordination variable. The property of the trafficflow model with HCPN is analyze, and then through the simulation, the evaluatingindicator is created to evaluate the control strategy. The consequence of the simulationindicates the real-time adaptive traffic signal strategy achieves more effective control.
本文遵循现代复杂工程系统研究的五个基准：过程、体系架构、方法论、建模与评价，以城市智能交通系统中的交通信号控制系统为主要研究对象，在对国内外城市交通信号控制系统研究现状及发展进行分析的基础上，较系统地研究了城市交通信号控制的若干关键议题；重点研究并提出了一种新型的道路车辆数据采集系统的车辆检测传感器；研究建立符合城市智能交通系统体系结构框架标准的层次化交通信号控制系统建模方法，由此建立了基于层次着色Petri网（HCPN: HierarchicalColored Petri Nets）的城市交通流模型；基于HCPN交通流模型，研究并提出了一种新的实时自适应城市交通信号控制算法。
论文的主要内容包括以下几个方面：
研究并开发了一种基于各向异性磁阻（AMR:Anisotropic Magnetoresistive）传感器的车辆检测系统，提出了基于AMR车辆检测器的车辆检测分类方法。为了获得城市交通流实时路况数据，在讨论各种车辆检测方法的基础上，深入研究了AMR传感器的数据采集及车辆分类的原理，给出了代表车型的主要特征以及由此组成的特征向量的提取方法，然后，采用支持向量机（SVM: Support Vector Machine）分类学习算法检测车辆及进行车型分类。并从核函数，模型参数等方面对其分类性能进行了讨论。实验结果显示，该车辆检测方法具有长期稳定性的特点，不受外在天气环境路况的影响，有较强的实用性。进一步，以AMR车辆检测器为节点，应用高效率、低成本的串行接口总线，提出了网络化城市道路车辆检测传感器系统的建设构想。
研究城市交通系统体系结构和交通流理论的基础上，应用着色Petri网，研究建立符合城市智能交通系统体系结构框架标准的层次化交通信号控制系统建模方法，提出顶层为操作视图、中层为系统视图、底层为技术标准视图的城市ITS（IntelligentTransportation Systems）体系结构框架。论文依据城市交通流的特点，提出了城市交通流的层次模型。该层次模型由交通路网层，网络构件层，逻辑控制层等三层组成，实现了交通流与交通信号控制的无缝链接。基于ITS体系结构和层次着色Petri网，依据实际的道路参数，采用自顶而下的建模方法，依次建立各层子模型。然后，论文通过城市交通流着色Petri网建模实例，进一步介绍了交通流着色Petri网建模过程，讨论了分层结构所具有的易扩展性，验证了分层模型在解决交通流建模复杂性问题上的显著成效。
通过分析城市交通流模型，在重点考虑算法实时性和自适应性的前提下，分别提出了单交叉口和多交叉口的信号控制算法。首先分析了单交叉口的交通流特征以及短时交通流预测，以优化车辆延误为目标，提出了基于规则的单交叉口实时自适应控制算法。然后研究了多交叉口之间交通流的相互影响，以相邻交叉口中间路段上的车流量为协调变量，实现了基于规则的多交叉口两级协调控制算法。最后结合文章提出的HCPN交通流模型，建立了交通信号控制器的CPN模型，以及交通信号控制的评价指标，通过模型仿真，完成了基于规则的城市交通信号优化协调控制策略的评价研究。仿真结果证明了该实时自适应协调控制方法及其模型的正确性和实用性。
Urban traffic signal control system has been a hot topic of research by domestic andforeign scholars. Rational and efficient urban traffic signal control system does not onlyhelp to improve the existing transportation system performance, but also effectivelyimprove the living environment of the city and produce considerable social benefits for thecity.
According to complex engineering system benchmarks research areas: the process,the architecture frameworks (AFs), the methodologies, the modeling and evaluation.Based on the analysis of current urban traffic signal control system's research situation anddevelopment trends at home and abroad, a number of key technologies of urban trafficsignal control are systematically studied and discussed in this paper, especially research onvihicle detection and classification system, model of urban traffic flow based on ITSarchitecture and Hierarchical Colored Petri Nets(HCPN), and a new real-time adaptiveurban traffic signal control algorithm.
The paper mainly covers the following areas:
Firstly, a novel vehicle detection and classification system based on anisotropicmagnetoresistive (AMR) sensors and support vector machine (SVM) algorithm ispresented. The AMR sensors detect the change of earth magnetic field which will bedisturbed differently by different types of passing traffic vehicle. With the featuresextracted from the disturbance data of earth magnetic field, SVM classifier will be trainedand tested to classify the vehicle type. The results of our experiments indicated thatvehicle classification based on AMR sensors and SVM algorithm is effective and efficient.In addition, using AMR vehicle detection sensors as sensor network nodes, networkedsensors vehicle detection system based on the low cost and high-speed rs485serial bus ispresented.
Secondly, after researching on urban transportation system architecture and trafficflow theory, an urban ITS architecture and a hierarchical modeling approach for urbantraffic signal control system are established based on Colored Petri Nets, whicharchitecture inlcudes three levels: the top level-operational view, the middle-systemview, and the underlying-technical standard view. According to the characteristics ofurban traffic flow, a hierarchical structure model of urban traffic flow is presented, whichmodel consists of three levels, traffic network level, network conponents level and logic control level, and implements the seamless connection between traffic flow and logiccontroller. Based on hierarchical Colored Petri Nets theory and actual road parameters,models of every level are established using top-down modeling approach. Afterestablishing the hierarchical Colored Petri Nets model of a real traffic network forexample, introduced the design approach of Colored Petri Nets model, argued that themodel has good extensibility, and validated that the hierarchical structure model will makeus better to achieve remarkable results in complex problem solving tasks on traffic flowmodeling.
Finally, according to the analysis of urban traffic flow model and mainly taking thecapability of real-time and adaptive control into account, traffic signal control algorithmsfor isolated intersection and area traffic are presented respectively. Based on the trafficflow characteristics of an isolated intersection and precdiction for short-term traffic flow,aiming to minimize the vehicle delay, a rule-based traffic signal control algorithm ispresented. After analyzing the relationship of traffic flow between adjacent intersections, arule-based coordination control algorithm is presented with traffic volume in the linkbetween adjacent intersections as the coordination variable. The property of the trafficflow model with HCPN is analyze, and then through the simulation, the evaluatingindicator is created to evaluate the control strategy. The consequence of the simulationindicates the real-time adaptive traffic signal strategy achieves more effective control.
引文
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