基于协同理论的城市路网状态特征信息提取与表达研究

The amount of the road network data increases in a geometric progression during the construction of the intelligent transportation system, thus causes information overload and weakness of expression ability,which leads to the system decision delay or failure. Therefore, the characteristic information extraction and expression of city road network have become an inevitable requirement of the sustainable development of the city road traffic. In this research, under the guidance of the synergy theory, information retention criteria under supervision is established by defining the feature parameters of the road network, and the feature extraction model of traffic state is built based on two-dimensional local sensitive discriminant analysis algorithm. On the basis of the model, identification and classification of the road network running state are achieved, supervised classification and unsupervised classification algorithm is designed, and traffic state classification model based on feature extraction is built. The phases determining parameters of the network state are defined based on the division of network state, and the description of the two-dimensional network evolution state is realized. The network evolution state model is established by using the integrated modeling method of the city road network state characteristic information and the state characteristic information can be displayed. This research can rapidly and accurately provide intuitionistic information for city traffic management and public travel, and can provide powerful technical support for the construction of "Harmonious Traffic".

智能交通系统建设过程中的路网数据量呈几何级数增长，带来了信息超载和表达能力弱等问题，导致了系统的决策延迟或失误。因此，路网特征信息提取及表达技术研究已成为城市道路交通可持续发展的必然要求。本课题以协同理论为指导，通过对城市路网表征参量形式化定义建立有监督的信息保留准则，基于二维局部敏感辨别分析算法构建路网状态数据特征信息提取模型；在此基础上，对路网运行状态进行识别和分类，分别设计监督分类和非监督分类算法，建立基于特征参数提取的交通状态分类模型；利用路网状态划分定义路网状态的相，从而确定路网状态特征参量，实现二维路网状态演化过程的描述；利用城市路网状态特征信息一体化建模方法生成路网状态演化表达模型，进行状态特征信息展现。本课题能为城市交通管理和公众出行准确、快速地提供直观信息，为构建“和谐交通”提供有力的技术支撑。

城市路网状态的演化规律和运行特征分析是制定缓解交通拥堵政策和措施的基础，以保证城市道路交通系统可靠和高效运转。路网状态运行规律和特征分析的基础是对表征路网状态参量的获取。因此，路网状态准确、客观地进行表征，提取演化过程中的本质因素成为亟需解决的问题。项目组在综合分析国内外同类研究方法和结果的基础上，按照机理分析、理论建模和仿真实验研究相结合的方式开展城市路网状态特征信息提取与表达的研究，围绕城市路网运行状态特征信息提取、路网交通状态分类以及网络系统状态演化分析等关键问题，在系统分析城市路网交通流特性的基础上，提出了交通参数的选取原则以及数据采集的可行性，提出了基于自适应邻域选择算法优化局部敏感判别分析算法实现了城市路网状态参量数据的特征提取，采用舟山市路网数据实现了路网状态信息的特征提取，验证了该算法较好的鲁棒性与可分性；在特征提取的基础上，利用基于密度的聚类算法进行聚类分析,实现了路网交通状态的判别；采用宏观基本图方法，实现了城市道路关键路径进行识别和基于宏观基本图的路网交通状态分析；最后基于深度学习的理论，通过组合建模方法，实现了城市路网运行演化分析和一体化表达。项目成果为城市交通规划、建设和管理中科学决策与信息发布准确、快速提供直观的信息，为构建人-车-路-环境的“和谐交通”提供有力的技术支撑。