城市快速路实时交通状态估计方法研究

发布时间：2018-07-13 10:33

【摘要】：城市快速路的实时交通状态估计是实施有效交通控制和诱导的前提与基础。智能交通系统的发展，尤其是AVI（Advanced Vehicle Information）信息技术的进步，，使得城市道路交通信息获取更加准确全面。基于移动传感技术（如浮动检测车、智能手机），实时估计道路交通状况就是其中一个很有意义的研究方向。与传统固定式交通检测技术相比，移动式、便携式交通检测方法在大范围城市道路信息获取的实时性、准确性、覆盖面更广等方面都具有明显优势。本文研究了基于便携式速度检测器网络的城市快速路实时道路交通状态估计问题。利用路网车辆速度信息估计城市快速路的交通密度、流量等交通参数。单个简单的车辆速度检测装置，如交警使用的便携式测速枪，通过结合移动通讯技术，构成了用于大范围路网平均速度检测的无线传感器网络。与既往交通状态估计研究相比较，检测方法的两个特色在于：第一、可以采集到城市快速路任意时空位置、同一时刻不同路段上的车辆平均速度信息用于交通估计，检测器网络具有自组织特征，同时对路网交通的正常运行不造成干扰。第二、城市快速路交通流处于自由、拥堵等不同交通模式时，系统的可观测性各不相同。这样就会造成原本有效的交通检测信息，当道路交通模式发生改变后，对当前系统状态的估计不再起作用。为了有效应对交通大数据，发挥移动式交通检测技术的优势，采集关键时空位置处的交通流信息用于状态估计，也是文本探讨的一个问题。事实上，城市快速路交通流作为一类时空分布式参数系统，系统演化趋势由当前道路交通状态与路网边界（上下游、出入口匝道）交通流量共同确定。为了能够实现交通状态与边界流量的同步估计，本文在交通状态估计方法上主要进行了三方面问题的研究：首先，为了探究城市快速路交通流在不同交通模式下的可观测性，基于路段间的流量传输关系建立了一种状态切换的交通多模态切换模型，并且对模型的可观性进行了分析。通过对模型的可观测行分析发现，当城市快速路交通流在不同交通状态切换时，交通检测信息的有效性也随之改变。根据交通检测信息的有效性，可以合理布设交通检测器资源，同时降低检测器数据传输时间，有效应对交通大数据问题。其次，传统估计方法主要集中于对主线路段的研究，而且是在边界条件已知的情况下进行的。这样，交通估计问题就受限于检测器的时空位置。针对以上问题，在本文中提出了一种以城市快速路为研究对象的交通流模型，该模型中不仅含有状态变量（密度），还含有未知输入（边界流量）。为了实现交通状态和边界流量的同步估计，本文设计了一种循环递归滤波器，该滤波器在对交通状态进行估计的同时，还可以同步对交通流量进行估计。同时，由于系统方程不在受边界流量的限制，选取速度作为观测变量，通过便携式速度检测设备，结合移动通讯技术，构造交通路网速度检测的无线传感器网络。根据路网速度检测信息，对交通状态进行估计，使交通状态估计不在受检测器位置的约束，交通模型和滤波方法具有更高的适用性。最后，研究了大尺度的快速路网交通估计问题。大尺度交通路网的模型阶次高，算法复杂，且运行时间那以满足交通控制对实时性的要求。在本文提出的交通状态和边界流量同步估计的基础上，将大尺度的交通路网分割成若干个子路段，然后对每个子路段交通状态和边界流量进行估计，结合信息融合技术，对相邻两个子路段的边界流量（或重合的元胞密度）进行融合，从而得到交通路网的交通状态。这种分布式的交通状态估计方法，大大降低了模型阶次，提高了估计算法效率。
[Abstract]:The real-time traffic state estimation of urban expressway is the prerequisite and basis for implementing effective traffic control and induction. The development of intelligent transportation system, especially the progress of AVI (Advanced Vehicle Information) information technology, makes urban road traffic information get more accurate and comprehensive. Based on mobile sensing technology (such as floating detection car, smart hand) The real-time estimation of road traffic conditions is a very meaningful research direction. Compared with the traditional fixed traffic detection technology, mobile and portable traffic detection methods have obvious advantages in the real-time, accuracy and wide coverage of large urban road information acquisition.
In this paper, the real time road traffic state estimation problem of Urban Expressway Based on the portable speed detector network is studied. The traffic density and traffic parameters of urban expressway are estimated using the speed information of road network. A single simple vehicle speed detection device, such as the portable speed gun used by traffic police, is combined with the mobile communication technology. In comparison with previous traffic state estimation studies, the two features of the detection method are: first, the location of the urban expressway can be collected at any time and space, and the vehicle average speed information on the different sections of the same time is used for traffic estimation, and the detector network is used. The collaterals have self organization characteristics and do not interfere with the normal operation of road network traffic. Second, when the traffic flow in the urban expressway is in the free, congestion and other different traffic modes, the observability of the system is different. This will result in the original effective traffic detection information. After the road traffic pattern changes, the current system state will be changed. In order to effectively cope with large traffic data and give full play to the advantages of mobile traffic detection technology, it is also a problem for text discussion to collect traffic flow information at the key space and time position for state estimation.
In fact, urban expressway traffic flow is a kind of space-time distributed parameter system. The evolution trend of the system is determined by the traffic flow of the current road traffic state and the road network boundary (upper and lower reaches, the exit ramp). In order to realize the synchronous estimation of the traffic state and the boundary flow, this paper is mainly carried out in the traffic state estimation method. Three aspects of the study:
First, in order to explore the observability of urban expressway traffic flow under different traffic modes, a traffic multimodal switching model with state switching is established based on the flow transmission relationship between sections, and the observability of the model is analyzed. The traffic flow in urban expressway is found to be different by the observable line analysis of the model. The effectiveness of traffic detection information is also changed when the traffic state is switched. According to the effectiveness of traffic detection information, the traffic detector resources can be set up reasonably, and the data transmission time of the detector can be reduced, and the problem of traffic data can be effectively dealt with.
Secondly, the traditional estimation method is mainly focused on the study of the main line section, and is carried out in the case of known boundary conditions. In this way, the traffic estimation problem is limited to the space-time position of the detector. In this paper, a traffic flow model based on the urban expressway is proposed in this paper, which contains not only the model of the traffic flow, but also the traffic flow model in this paper. There is a state variable (density) and an unknown input (boundary flow). In order to realize the synchronous estimation of traffic state and boundary flow, a cyclic recursive filter is designed in this paper. The filter can estimate the traffic flow synchronously while the traffic state is estimated. Meanwhile, the system equation is not subject to the boundary flow. According to the speed detection information of the road network, the traffic state is estimated, and the traffic state estimation is not constrained by the detector position, the traffic model and the filtering method are made, and the traffic state estimation is not constrained by the detector position. There is a higher applicability.
Finally, the large scale road network traffic estimation problem is studied. The large scale traffic network model order is high, the algorithm is complex, and the operation time is to meet the real-time requirements of traffic control. On the basis of the proposed traffic state and boundary flow synchronization estimation, the large scale traffic network is divided into several sub sections. Then, the traffic state and boundary flow of each sub section are estimated, combined with the information fusion technology, the boundary flow (or the coincidence cell density) of the adjacent two sub sections is fused, thus the traffic state of the traffic network is obtained. This distributed traffic state estimation method greatly reduces the order of the model and improves the estimation algorithm. Efficiency.
【学位授予单位】：北京工业大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：U491

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