混合Petri网交叉口信号灯建模的模糊优化

城市车流量急剧增加和道路通行能力之间的矛盾日益激化,交通拥堵已成为亟待解决的社会问题。道路交叉口信号灯优化控制是解决该问题的有效方法。基于城市道路交通系统随机性强、离散性连续性混杂、难以用数学模型精确建模等特点,提出了一种用连续Petri网建立道路交通流模型,用离散Petri网对道路交叉口信号灯进行控制的方案。根据车流量的动态变化,采用模糊控制对交叉口绿灯时间进行自适应优化。仿真结果表明,该方案能提高交叉口通行能力,减少车辆延误,优于传统的控制方法。     

基于TCPN的交叉口信号控制模型与优化

针对城市路网中交叉口车辆通行效率低下,交通信号控制策略难于满足输入路段上车流变化的问题,本文提出了一种基于时延赋色Petri网的交叉口交通流优化控制模型。首先建立路段车流、交叉口车流和交通信号控制的TCPN模型,其次建立以交叉口输入路段车辆数最小为目标的车流优化方程。在假设信号周期固定的前提下,利用15个周期采集的交叉口输入、输出路段车辆数,求解满足优化目标的相位配时,确保交叉口输出车辆数最大,且输入路段上待通过车辆平均数最小。仿真结果表明,交叉口的通行能力显著提高,各输出路段上的车辆平均数分别增加了13.3%,9.7%,9.8%和4.3%。     

Distributed and adaptive traffic signal control within a realistic traffic simulation

As traffic congestion rises within urban centers around the world, the intelligent control of traffic signals within cities is becoming increasingly important. Previous research within the area of intelligent traffic signal control has several shortcomings, including a reliance on historical data, the use of centralized systems which cannot handle city-sized problem instances and solutions which are not capable of addressing real-world traffic scenarios (e.g., constantly varying volumes and complex network structures). The research reported here proposes algorithms capable of controlling traffic signals that rely on traffic observations made by available sensor devices and local communication between traffic lights. This solution allows signals to be updated frequently to match current traffic demand, while also allowing for significantly large problem sizes to be addressed. To evaluate the developed system, a realistic traffic model was developed using information supplied by the City of Ottawa, Canada. It was found, through simulation within the SUMO traffic simulation environment, that the proposed adaptive system resulted in higher overall network performance when compared to the current fixed signal plan controllers, which were recreated using information from the City of Ottawa. This work also includes examples of why fixed signal controllers are inferior to an adaptive control system.     

Urban traffic congestion propagation and bottleneck identification

Bottlenecks in urban traffic network are sticking points in restricting network collectivity traffic efficiency. To identify network bottlenecks effectively is a foundational work for improving network traffic condition and preventing traffic congestion. In this paper, a congestion propagation model of urban network traffic is proposed based on the cell transmission model （CTM）. The proposed model includes a link model, which describes flow propagation on links, and a node model, which represents link-to-link flow propagation. A new method of estimating average journey velocity （AJV） of both link and network is developed to identify network congestion bottlenecks. A numerical example is studied in Sioux Falls urban traffic network. The proposed model is employed in simulating network traffic propagation and congestion bottleneck identification under different traffic demands. The simulation results show that continual increase of traffic demand is an immediate factor in network congestion bottleneck emergence and increase as well as reducing network collectivity capability. Whether a particular link will become a bottleneck is mainly determined by its position in network, its traffic flow （attributed to different OD pairs） component, and network traffic demand.     

A three-dimensional probabilistic fuzzy control system for network queue management

A novel probabilistic fuzzy control system is proposed to treat the congestion avoidance problem in transmission control protocol （TCP） networks. Studies on traffic measurement of TCP networks have shown that the packet traffic exhibits long range dependent properties called self-similarity, which degrades the network performance greatly. The probabilistic fuzzy control （PFC） system is used to handle the complex stochastic features of self-similar traffic and the modeling uncertainties in the network system. A three-dimensional （3-D） membership function （MF） is embedded in the PFC to express and describe the stochastic feature of network traffic. The 3-D MF has extended the traditional fuzzy planar mapping and further provides a spatial mapping among ＂fuzziness-randomness-state＂. The additional stochastic expression of 3-D MF provides the PFC an additional freedom to handle the stochastic features of self-similar traffic. Simulation experiments show that the proposed control method achieves superior performance compared to traditional control schemes in a stochastic environment.     

智能交通信号灯形式化建模

主要针对协调和检测离散控制系统中的交通信号灯问题做出了形式化描述，在设计和分析时结合着色Petri网理论对问题进行建模和分析，并采用仿真工具Design／CPN对模型进行仿真研究。结合一个简单交通信号灯控制系统给出了形式化描述和分析结论。     

基于拥堵系数的道路交通网络关键路段辨识

城市道路交通的路段信息与车流量信息对道路交通的安全、高效运行至关重要.在交通高峰时期,通过对关键路段加以控制,可实现整个道路交通网络的完全能控.为寻找路网中的关键路段,将道路网络的交叉口-节点模型转化为道路网络的路段-节点模型,基于路段信息与车流量信息提出拥堵系数来衡量道路交通网络的车辆拥堵程度,并将其作为道路网络的路段-节点模型的边权重,最后运用关键路段辨识算法对道路交通网络的关键路段进行辨识.以沈阳市皇姑区主城区道路为例建立以拥堵系数为权重的网络模型, 按照所提方法辨识的关键路段数量为14条,约占道路网络总路段数的14.3%,具有较低的控制成本,且大部分为由北向南方向和由西向东方向.其中8条路段分布在皇姑区道路实时拥堵排行前5名,约占关键路段总数的57.1%,表明所给出的关键路段更多地分布在交通状态较为拥堵的路段上,符合实际情况.     

CTS: 基于拥堵溯源算法的信号灯多智能体强化学习组织方案

在交通路网的运行中红绿灯起着至关重要的调度作用,随着目前交通的飞速发展,道路越来越复杂、车辆越来越繁多,导致红绿灯的调度压力越来越大、调节能力却越来越弱。为了解决这一问题,建立了CTS（congestion trace source）方案,将交通疏导的主体对象红绿灯作为智能体进行强化学习以优化其对交通的疏导控制能力,通过构建拥堵链和拥堵环综合分析路网拥堵情况,佐以红绿灯相位及其配时数据以达到对红绿灯智能体对象状态的综合判断;CTS方案设计了红绿灯排队长度算法将拥堵情况数字化作为智能体奖励对优化效果进行评判。使用SUMO仿真环境进行实验,设计交通优化指标路口平均排队长度并进行对比,最终该方案的路口平均排队长度相较于原始数据提升了40%。     

城市复杂道路网络交通流的分解模型

针对城市道路交通流非线性、不确定性和模糊性特点,将城市道路与快速干道作为整体对待,提出了面向控制应用的城市交通网络宏观动态离散模型。将城市街区作为划分基点,把整个城市道路复杂交通网络分解为交叉口和单向环形道路两个子系统,分别建立了它们的宏观动态模型。通过对交叉口进行理想虚拟变形,将各个单向环形道路连接在一起,从而形成各种复杂网络。对西安市中心区域的实际交通流数据进行了仿真研究,结果表明该交通流模型基本实现了城市道路与快速干道的统一分析建模,较好地反映了城市路网的交通流信息,可以作为城市交通控制系统分析和设计的有力工具。     

Adaptive congestion control scheme in mobile ad-hoc networks

In the past, several authors have expressed their concerns over the poor congestion control in mobile wireless ad-hoc networks using traditional reference layer model. Many solutions were proposed to handle growing traffic and congestion in the network, using link layer information. Existing solutions have shown difficulties in dealing with congestion with varying packets drop. Moreover, ensuring the superior performance of congestion control schemes with traditional referenced layer model is a challenging issue, due to quick topology changes, dynamic wireless channel characteristics, link-layer contentions, etc. In this paper, we propose an effective cross-layer adaptive transmission method to handle the congestion in mobile wireless ad-hoc networks adequately. Simulation results exemplify the usefulness of the proposed method in handling congestion, and yields better results compared to existing approaches.     

A robust adaptive congestion control strategy for large scale networks with differentiated services traffic

In this paper, a robust decentralized congestion control strategy is developed for a large scale network with Differentiated Services (Diff-Serv) traffic. The network is modeled by a nonlinear fluid flow model corresponding to two classes of traffic, namely the premium traffic and the ordinary traffic. The proposed congestion controller does take into account the associated physical network resource limitations and is shown to be robust to the unknown and time-varying delays. Our proposed decentralized congestion control strategy is developed on the basis of Diff-Serv architecture by utilizing a robust adaptive technique. A Linear Matrix Inequality (LMI) condition is obtained to guarantee the ultimate boundedness of the closed-loop system. Numerical simulation implementations are presented by utilizing the QualNet and Matlab software tools to illustrate the effectiveness and capabilities of our proposed decentralized congestion control strategy.     

基于删边扩容策略的城市交通网络优化

建立一个面向控制的城市交通网络模型,以结构矩阵的形式进行数学表示,引入序参量的概念来表征城市交通的拥堵状况,并提出了基于介数的具体删边扩容策略. 将此策略应用到已建立的交通网络模型中进行仿真验证,证明了该策略在不影响行驶路径长度的前提下可以显著改善城市交通状况,并得出了删边比例与介数最大值成反比例的对应关系的结论.     

Urban traffic congestion estimation and prediction based on floating car trajectory data

Traffic flow prediction is an important precondition to alleviate traffic congestion in large-scale urban areas. Recently, some estimation and prediction methods have been proposed to predict the traffic congestion with respect to different metrics such as accuracy, instantaneity and stability. Nevertheless, there is a lack of unified method to address the three performance aspects systematically. In this paper, we propose a novel approach to estimate and predict the urban traffic congestion using floating car trajectory data efficiently. In this method, floating cars are regarded as mobile sensors, which can probe a large scale of urban traffic flows in real time. In order to estimate the traffic congestion, we make use of a new fuzzy comprehensive evaluation method in which the weights of multi-indexes are assigned according to the traffic flows. To predict the traffic congestion, an innovative traffic flow prediction method using particle swarm optimization algorithm is responsible for calculating the traffic flow parameters. Then, a congestion state fuzzy division module is applied to convert the predicted flow parameters to citizens’ cognitive congestion state. Experimental results show that our proposed method has advantage in terms of accuracy, instantaneity and stability.     

A quantum particle swarm optimization driven urban traffic light scheduling model

Urban traffic congestion becomes a severe problem for many cities all around the world. How to alleviate traffic congestions in real cities is a challenging problem. Benefited from concise and efficient evolution rules, the Biham, Middleton and Levine (BML) model has a great potential to provide favorable results in the dynamic and uncertain traffic flows within an urban network. In this paper, an enhanced BML model (EBML) is proposed to effectively simulate the urban traffic where the timing scheduling optimization algorithm (TSO) based on the quantum particle swarm optimization is creatively introduced to optimize the timing scheduling of traffic light. The main contributions include that: (1) The actual urban road network with different two-way multi-lane roads is firstly mapped into the theoretical lattice space of BML. And the corresponding updating rules of each lattice site are proposed to control vehicle dynamics; (2) compared with BML, a much deeper insight into the phase transition and traffic congestions is provided in EBML. And the interference among different road capacities on forming traffic congestions is elaborated; (3) based on the scheduling simulation of EBML, TSO optimizes the timing scheduling of traffic lights to alleviate traffic congestions. Extensive comparative experiments reveal that TSO can achieve excellent optimization performances in real cases.

     

Deploying a network of smart cameras for traffic monitoring on a “city kernel”

A relevant aspect when evaluating the city smartness is related to the innovative approach to urban traffic management. We present a system called city kernel, designed to handle several subsystems, each addressing a specific sensor network and we describe an infrastructure for wide traffic control via a vision sensor network. This infrastructure consists of a network of smart cameras operating over an outdoor public lighting thanks to power line communication technology and equipped with a vehicle counting and classification algorithm. We discuss the deployment of this network on the city kernel and some related services exposed to urban actors.     

城市交通信号的迭代学习控制及其对路网宏观基本图的影响

针对在城市交通信号控制中存在对交通流难以精确建模的问题,首先利用交通流的重复性特点,提出了一种基于迭代学习的城市交通信号控制方法,并证明了在不确定初态下迭代学习控制算法的收敛性.其次,结合路网宏观基本图的特性分析了基于迭代学习的交通信号控制策略对路网交通态势的影响.结果表明,当迭代的初始状态在期望初态值的小范围内波动时,系统的跟踪误差仍能收敛到一个界内;通过对交通信号的迭代学习控制,路段的实际占有率能够逐步逼近期望占有率,从而使路网内的车辆密度分布更加均匀,确保交通流在更优的宏观基本图下运行,防止因车辆密度分布不均引起的通行效率下降及交通拥堵的发生.最后,通过仿真实验对所提方法的有效性进行了验证.     

城市快速路交通诱导和匝道控制集成仿真模型

为缓解城市快速道路网络交通网络拥挤问题,将网络交通流模型、匝道控制模型、基于可变信息标志（VMS）的路径选择模型等整合为一体,建立了城市快速路交通诱导和匝道控制集成仿真模型。与各种边界条件相结合,测试了匝道控制和路径诱导的控制效果。结果表明城市快速路集成控制方法有助于在空间和时间上均衡交通负荷,提高交通系统整体运行效率。     

城市单交叉路口交通信号实时优化控制与仿真

针对城市道路单点交叉口交通流的到达特性,将路口到达的交通流划分为4种状态,提出了“基于状态划分的多相位交通信号实时控制方法”,该方法根据路口各状态下交通流的到达特征和控制目标,为不同交通状态选择合适的性能指标,并建立各状态下交通信号的动态配时模型。同时,设计了一种改进的自适应实数编码遗传算法对交通信号配时模型进行求解,该算法采用基于分类的排序惩罚机制对约束进行处理,并引入模拟退火算子增强遗传算法的局部搜索。最后,采用3种算法对实例进行大量的数值计算和Paramics仿真,计算和仿真结果均表明所设计的算法求解精度高且模型具有良好的控制效果。     

基于多学科设计优化的路网交通分布式协同控制

城市路网交通控制直接影响着交通运行效率,对其优化研究已成为缓解城市交通拥堵问题的热点之一.鉴于此,针对高峰交通路网将其分为过饱和区域与过饱和关联区域,在采用灰色关联分析-谱聚类方法对关联区域划分的基础上,构建路网交通分布式协同控制模型,进一步提出基于多学科设计优化的过饱和区域及其关联区域协同优化求解方法.通过搭建实例路网模型分析算法优化效果,结果表明所提出方法能够明显改善路网交通运行效率,有助于缓解城市通勤高峰时段的交通拥堵和扩散问题.     

城市交通网络正系统模型与稳态信号控制

城市路网内车流分布不均衡为交通拥堵原因之一,且交通控制系统为典型正系统．为此,本文采用Compartment正系统对网络交通流演化建模,以网络均衡为控制目标,提出稳态信号控制方法．首先,建立网络交通流Compartment正系统模型,可描述具有任意控制结构网络的过饱和特征;进一步,给定网络车流输入,由非负矩阵理论可得网络存在唯一稳定平衡点,给出平衡点解析计算公式．由此,提出网络稳态信号控制律,为路段状态反馈控制律．最后,以北京市奥林匹克公园区域拓扑路网为例,在VISSIM软件中建立仿真环境,比较稳态信号控制方法与定时控制方法,仿真结果表明在高需求网络条件下,稳态信号控制方法可改善网络整体性能和缓解局部拥堵．