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。     

基于遗传算法改进的交通干线信号优化研究

针对城市交通拥挤、道路通行能力低等问题,通过建立城市交通干线系统信号配时模型,设计了一种基于遗传算法改进的混合式优化算法.算法对遗传算法中适应度函数、交叉算子和变异箅子等进行了一些改进,并将混沌优化思想融入改进后的遗传算法中.用混合式优化算法对城市交通干线系统信号进行配时,有效地协调了各路口的信号灯,使交通干线系统的交通量得到了明显改善.仿真结果表明此算法具有较快的收敛速度和进化效率,能有效实现全局优化.     

基于传感器网络的城市智能运输系统的构建

基于传感器网络构建城市智能运输系统，实现对城区交通的智能控制．设计了智能运输系统专用的传感器节点和相应的硬件结构，并编制了相应的软件，建立了一个城市交通系统的智能控制模型，实现道路交通流实时动态信息的采集、处理和发布，及路段的规模协调控制．使用Matlab软件对控制模型中的交通信息的处理进行了仿真，表明该智能运输系统模型能够投入交通控制的实际应用．与当前普遍采用的基于GPS技术构建的ITS系统相比，本系统具有更高的性价比、灵活性和控制效率．     

多智能体在城市交通系统中应用现状综述

多智能体技术通过各自治智能体间的通讯、协调、合作来表达实际的复杂系统,特别适用于城市交通系统的建模。在分析了多智能体技术和城市交通系统特性的基础上,对多智能体在城市交通管理系统、出行信息系统和公交系统中的应用现状进行了综合论述,对各个领域最具代表性的应用进行了介绍和分析。研究认为,多智能体技术为城市交通问题提供了良好的解决方案,在具体应用中需要对交通环境和实体进行准确的建模,缓解通信需求、降低运算量及低协调复杂度、增强应用系统的稳定性和安全性。最后指出了多智能体技术在城市交通系统应用中的发展趋势。     

A Dynamic Road Incident Information Delivery Strategy to Reduce Urban Traffic Congestion

Advanced information and communication technologies can be used to facilitate traffic incident management. If an incident is detected and blocks a road link, in order to reduce the incident-induced traffic congestion, a dynamic strategy to deliver incident information to selected drivers and help them make detours in urban areas is proposed by this work. Time-dependent shortest path algorithms are used to generate a subnetwork where vehicles should receive such information. A simulation approach based on an extended cell transmission model is used to describe traffic flow in urban networks where path information and traffic flow at downstream road links are well modeled. Simulation results reveal the influences of some major parameters of an incident-induced congestion dissipation process such as the ratio of route-changing vehicles to the total vehicles, operation time interval of the proposed strategy, traffic density in the traffic network, and the scope of the area where traffic incident information is delivered. The results can be used to improve the state of the art in preventing urban road traffic congestion caused by incidents.      

基于混合系统状态估计的故障诊断

如果将故障的发生视为一个离散事件,则存在故障可能的系统可以看作随机混合系 统,那么故障诊断问题就可转化为混合系统的离散状态估计问题.文中试图从这个角度研究 在非高斯噪声环境下非线性系统的故障诊断问题.在发生故障后的系统模型是已知的假定条 件下,使用随机混合自动机对系统建模,并利用基于粒子滤波的混合估计算法估计出混合状 态,从而完成故障诊断.仿真结果表明,所提的方法是可行的,可以处理某类故障诊断.     

蒙特卡罗法在局域网交通信息估计中的应用

为解决理论计算需要事先已知车辆行驶路径的问题,提出采用蒙特卡罗法设计局域路网交通信息估计集成仿真模型,可利用有限交通采集设备,实现全面掌握城市路网交通运行状况、快速有效制定管理措施的目的。在实际路网上的研究结果表明,其具有良好的可靠性,可为先进交通管理系统（ATMS）相关交通模型的建立和交通采集设备的布设提供依据。     

Calibration of microsimulation traffic model using neural network approach

This paper presents the results of research on the applicability of neural networks in the process of computer calibration of a microsimulation traffic model. VISSIM microsimulation model is used for calibration done at the example of roundabouts in an urban area. The calibration method is based on the prediction of a neural network for one traffic indicator, i.e. for the traveling time between measuring points. Besides the traveling time, the calibration process further/also involves a comparison between the modeled and measured queue parameters at the entrance to the intersection. The process of validation includes an analysis of traveling time and queue parameters on new sets of data gathered both at the modeled and at a new roundabout. A comparison of the traffic indicators measured in the field and those simulated with the calibrated and uncalibrated microsimulation traffic model provides an insight into the performance of the calibration procedure.     

基于Agent的驾驶员-车辆建模研究与实现

微观交通仿真技术是解决城市交通网络拥塞问题的有效工具.在各种交通元素中,驾驶员-车辆单元是交通流的主体.该文通过对交通流中驾驶员、车辆特性的分析,将驾驶员和车辆视为一个整体,并基于Agent理论对之进行建模.文中详细分析了Agent的生成和路径搜索及选择算法,研究了Agent的跟驰、换道和挤占道等行为.该文最后介绍了基于Agent模型建立的一种微观交通仿真器,并在该仿真器上进行仿真实验.结果表明该系统能够较好的仿真实际交通流状况.     

Fault detection model‐based controller for process systems

This paper develops a model‐based control system for fault detection and controller reconfiguration using stochastic model predictive control (MPC). The system can determine online the optimal control actions, detect faults quickly, and reconfigure the controller accordingly. Such a system can perform its function correctly in the presence of internal faults. A fault detection model based (FDMB) controller consists of two main parts: the first is fault detection and diagnosis (FDD) and the second is controller reconfiguration (CR). Systems subject to such abrupt failures are modeled as stochastic hybrid systems with variable‐structure. This paper deals with three challenging issues: design of the fault‐model set; estimation of hybrid multiple models; and stochastic MPC of hybrid multiple models. For the first issue, we propose a simple scheme for designing a fault model set based on random variables. For the second issue, we consider and select a fast and reliable FDD system applied to the above model set. Finally, we develop a stochastic MPC scheme for multiple model CR with soft switching signals based on the weighted probabilities of the outputs of different models. Simulations for the proposed FDMB controller are illustrated and analyzed. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Distributed Model Predictive Control Method for Optimal Coordination of Signal Splits in Urban Traffic Networks

Coordination and control approaches based on model predictive control (MPC) have been widely investigated for traffic signal control in urban traffic networks. However, due to the complex non‐linear characters of traffic flows and the large scale of traffic networks, a basic challenge faced by these approaches is the high online computational complexity. In this paper, to reduce the computational complexity and improve the applicability of traffic signal control approaches based on MPC in practice, we propose a distributed MPC approach (DCA‐MPC) to coordinate and optimize the signal splits. Instead of describing the dynamics of traffic flow within each link of the traffic network with a simplified linear model, we present an improved nonlinear traffic model. Based on the nonlinear model, an MPC optimization framework for the signal splits control is developed, whereby the interactions between subsystems are accurately modeled by employing two interconnecting constraints. In addition, by designing a novel dual decomposition strategy, a distributed coordination algorithm is proposed. Finally, with a benchmark traffic network, experimental results are given to illustrate the effectiveness of the proposed method.     

ATM网络基于队列长度阀值的传输调度

本文提出了ATM网络的一种实时传输调度和信元丢失控制的综合方案.这种方案是基于队列长度阀值而设计的,它适应于ATM网络面向连接的特性.本文给出了这种方案的随机Petri网性能模型,并给出模型分解和迭代的近似求解方法.     

Traffic Simulation in Urban Networks Using Stochastic Cell Transmission Model

The stochastic cell transmission model (SCTM) is a macroscopic traffic simulation model of high accuracy. One of its advantages is that it can represent the uncertainty of the traffic states and the changing travel demand and supply conditions. However, it has been applied to freeways and simple networks having only one origin‐destination pair. In this article, we propose a modified stochastic cell transmission model (M‐SCTM) that applies the conventional SCTM to urban networks. In M‐SCTM, we introduce vehicle agents as well as their route choice behavior on an urban network, which is more applicable to complex urban networks. Additionally, M‐SCTM was applied to networks in which the turning ratio is not priorly set through a route search algorithm. The results show that M‐SCTM can conduct simulations with as much accuracy as SCTM. Furthermore, we verified the appropriate reproducibility of the simulations based on M‐SCTM and compared the estimated value and the measured value in terms of the travel time of each vehicle.     

Stochastic modeling and analysis of road–tramway intersections

In the last decades, the socio-demographic evolution of the population has substantially changed mobility demand, posing new challenges in minimizing urban congestion and reducing environmental impact. In this scenario, understanding how different modes of transport can efficiently share (partially or totally) a common infrastructure is crucial for urban development. To this aim, we present a stochastic model-based analysis of critical intersections shared by tram traffic and private traffic, combining a microscopic model of the former with a macroscopic model of the latter. Advanced simulation tools are typically used for such kind of analyses, by playing various traffic scenarios. However, simulation is not an exhaustive approach, and some critical, possibly rare, event may be ignored. For this reason, our aim is instead to adopt suitable analytical solution techniques and tools that can support instead a complete, exhaustive analysis, so being able to take into account rare events as well. Transient analysis of the overall traffic model using the method of stochastic state classes is adopted to support the evaluation of relevant performance measures, namely the probability of traffic congestion over time and the average number of private vehicles in the queue over time. A sensitivity analysis is performed with respect to multiple parameters, notably including the arrival rate of private vehicles, the frequency of tram rides, and the time needed to recover from traffic congestion.

     

Urban two-lane highway with quasi-dense traffic: analysis,finite element modelling,perturbation method and optimal control†

For an urban highway with quasi-dense traffic, neither modelling via continuum equations nor modelling via stochastic counting processes are fully satisfactory. A model is suggested which consists of adding white noise to the deterministic equations, and which is supported by a physical analysis of the phenomenon. The theoretical stochastic distributed system so obtained is considered and a distributed diffusion equation is derived. For the practical purpose of implementing the traffic control, another modelling via the finite element technique is designed. In this way the initial distributed system is converted into a finite set of lumped parameter systems in a cascaded combination. The study is performed on this combination. Despite this paper being of an ‘ applied ’ nature, it gives rise to some new problems to which possible solutions are suggested. Notable, the catastrophe theory is utilized to check the validity of the finite element technique, and the study of the limiting probability density by using the diffusion equation. The content of the paper applies to a broad class of stochastic distributed systems     

A simulation framework for modeling urban freight operations impacts on traffic networks

This paper presents a traffic simulation framework to reproduce urban freight movements, particularly concerning double-parked delivery operations. Since freight movements affect traffic and vice versa, we propose a hybrid framework that simulates traffic phenomena macroscopically and, at the same time, allows tracking delivery vehicles along their routes throughout the entire simulation. The traffic simulation framework is based on the Lighthill–Whitham–Richards macroscopic model as well as the theory of bottlenecks. The traffic component of the model can be coupled with a generic parking model. Since a novel faster version of the Lax-Hopf Formula is used in the traffic simulation, the proposed framework can perform efficient simulations. Because of this hybrid nature, the framework is suitable for simulations of large scenarios and for evaluations of City Logistic measures to tackle the last-mile problem. We show this in the second part of the study with two different measures: shifting delivery operations to off-peak hours, and prohibiting deliveries on critical streets. While the benefits deriving from the first strategy are evident, the effects of the second one are less clear because of the complexity of network interactions.     

On modelling urban transportation networks via hybrid Petri nets

In this paper, a class of hybrid Petri nets is shown to provide a valuable model of urban networks of signalised intersections. In fact, such networks can be fruitfully considered to be hybrid systems, where the vehicle flow behaviour is represented by means of a time-driven model, and the traffic light dynamics is represented by an event-driven model. The proposed model has been validated using real-traffic data about the Italian city of Turin. Some relevant experimental results are reported and discussed. Finally, some possible control-oriented applications of the model are described.     

基于改进时空残差卷积神经网络的城市路网短时交通流预测

城市路网交通流预测受到历史交通流和相邻路口交通流的影响,具有复杂的时空关联性.针对传统时空残差模型缺乏对交通流数据进行相关性分析、捕获微小变化而容易忽略长期时间特征等问题,提出一种基于改进时空残差卷积神经网络(CNN)的城市路网短时交通流预测模型.该模型将原始交通流数据转化成交通栅格数据,利用皮尔逊相关系数(PCC)对...     

基于agen t 的城市交通信号控制

利用agent技术对城市交通信号控制进行研究．首先给出了区域agent(ARA)的组成和结构，然后给出了城市交通控制的模型和协调算法．基于agent技术的城市交通控制系统能对交通状况进行实时反映和处理．在此模型基础上，应用博弈论的相关知识给出城市交通信号协调控制算法．最后通过仿真程序验证了该模型和算法的有效性和实用性．     

Automatic mesh generation for mobile network dimensioning using evolutionary approach

We focus on the dimensioning process of cellular networks that addresses the evaluation of equipment global costs to cover a city. To deal with frequency assignment, that constitutes the most critical resource in mobile systems, the network is usually modeled as a pattern of regular hexagonal cells. Each cell represents the area covered by the signal of a transmitter or base station (BS). Our work emphasizes on the design of irregular hexagonal cells in an adaptive way. Hexagons transform themselves and adapt their shapes according to a traffic density map and to geometrical constraints. This process, called adaptive meshing (AM), may be seen as a solution to minimize the required number of BS to cover a region and to propose a basis for transmitter positioning. The solution we present to the mesh generation problem for mobile network dimensioning is based on the use of an evolutionary algorithm. This algorithm, called hybrid island evolutionary strategy (HIES), performs distributed computation. It allows the user to tackle problem instances with large traffic density map requiring several hundreds of cells. HIES combines local search fast computation on individuals, incorporated into a global island-like strategy. Experiments are done on one real case representing the mobile traffic load of the second French city of Lyon and on several other traffic maps from urban fictive data sets.