A Survey of Model Predictive Control Methods for Traffic Signal Control

Enhancing traffic efficiency and alleviating (even circumventing) traffic congestion with advanced traffic signal control (TSC) strategies are always the main issues to be addressed in urban transportation systems. Since model predictive control (MPC) has a lot of advantages in modeling complex dynamic systems, it has been widely studied in traffic signal control over the past 20 years. There is a need for an in-depth understanding of MPC-based TSC methods for traffic networks. Therefore, this paper presents the motivation of using MPC for TSC and how MPC-based TSC approaches are implemented to manage and control the dynamics of traffic flows both in urban road networks and freeway networks. Meanwhile, typical performance evaluation metrics, solution methods, examples of simulations, and applications related to MPC-based TSC approaches are reported. More importantly, this paper summarizes the recent developments and the research trends in coordination and control of traffic networks with MPC-based TSC approaches. Remaining challenges and open issues are discussed towards the end of this paper to discover potential future research directions.      

基于Internet的分布式城市交通协同控制

研究了基于Internet的分布式城市交通协同控制系统模型,分析和规划了模型的组成结构;设计了以S3C44B0X为核心的嵌入式道路控制器,并通过外扩存储、通信、语音等模块,实现控制器的嵌入式模块化设计和软、硬件系统;最后分别在Linux和Windows系统平台下编写网络套接字,实现跨系统网络通信,对整个系统进行了集成和实现。试验结果表明修正的优化控制算法可将平均延误时间优化至10-3s,而且该控制器的软硬件系统运行稳定,性价比高、功能完善、可扩展性和通用性好。     

基于神经网络的交通控制诱导协同模型

针对传统交通控制与诱导模型及算法的不足,提出了具有中心协调系统（CCOS）的交通控制与诱导协同模 型。利用数据融合技术将历史数据的短时交通预测、交通事件检测结果以及实时交通流数据设计面向交通动态的信息融合, 并采用神经网络技术构建基于神经网络的交通控制诱导协同模型,同时对模型的参数进行了确定。通过典型的路网进行仿真 实验和对比分析,实验验证了该模型是可行和有效的。     

网络信息模式下分布式系统协调预测控制

工业系统中广泛存在一类由多个相互关联的子系统组成的大系统. 尽管分布式控制结构的性能没有集中式控制好,但由于其具有较高的灵活性和容错性,相对于集中控制更加适合控制上述系统.在保持容错性的情况下如何提高系统的整体性能是分布式控制的一个难点问题.本文提出了一种分布式预测控制(Distributed model predictive control, DMPC)方法,该方法通过在各子系统预测控制器的性能指标中加入输入变量对其下游子系统的影响的二次函数,来扩大分布式预测控制的协调度,进而在不增加网络连通度,不改变系统容错性的前提下,提高系统的性能.另外,本文给出了基于该协调策略的带输入约束的分布式预测控制器的设计方法,在初始可行的前提下,该方法相继可行并可保证系统渐近稳定.     

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．     

A Distributed Approach for Coordination of Traffic Signal Agents

Innovative control strategies are needed to cope with the increasing urban traffic chaos. In most cases, the currently used strategies are based on a central traffic-responsive control system which can be demanding to implement and maintain. Therefore, a functional and spatial decentralization is desired. For this purpose, distributed artificial intelligence and multi-agent systems have come out with a series of techniques which allow coordination and cooperation. However, in many cases these are reached by means of communication and centrally controlled coordination processes, giving little room for decentralized management. Consequently, there is a lack of decision-support tools at managerial level (traffic control centers) capable of dealing with decentralized policies of control and actually profiting from them. In the present work a coordination concept is used, which overcomes some disadvantages of the existing methods. This concept makes use of techniques of evolutionary game theory: intersections in an arterial are modeled as individually-motivated agents or players taking part in a dynamic process in which not only their own local goals but also a global one has to be taken into account. The role of the traffic manager is facilitated since s/he has to deal only with tactical ones, leaving the operational issues to the agents. Thus the system ultimately provides support for the traffic manager to decide on traffic control policies. Some application in traffic scenarios are discussed in order to evaluate the feasibility of transferring the responsibility of traffic signal coordination to agents. The results show different performances of the decentralized coordination process in different scenarios (e.g. the flow of vehicles is nearly equal in both opposing directions, one direction has a clearly higher flow, etc.). Therefore, the task of the manager is facilitate once s/he recognizes the scenario and acts accordingly.This revised version was published online in August 2005 with a corrected cover date.     

Autonomous Vehicle Platoons In Urban Road Networks: A Joint Distributed Reinforcement Learning and Model Predictive Control Approach

In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework: the deep reinforcement learning output (action) is translated into a set-point to be tracked by the model predictive controller; conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decision-making purposes; on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps (links, junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the aim to put in light the main features of the proposed approach. Moreover, it is important to underline that in different operating scenarios the proposed reinforcement learning scheme is capable of significantly reducing traffic congestion phenomena when compared with well-reputed competitors.

     

分布式区域交通信号自适应控制

基于无模型自适应动态规划,提出一种新的交通信号控制算法,应用于区域多路口协调优化控制.在四交叉路口的仿真环境下对该控制算法进行验证,取得良好的优化效果.     

两相邻路口交通信号的协调控制

传统路口的控制算法大多研究单个路口的信号控制情况.该文根据路口之间的相互关系,利用高阶广义神经网络及模糊推理提出了两个相邻交通路口的协调算法.利用此算法设计的交通信号控制器,可以有效地协调两相邻路口的红绿灯信号,在一定程度上改善了交通路口的交通状况.     

基于分布式模型的动车组预测控制方法

针对动车组由若干动车/拖车组成的动力单元固定编组耦 合构成,难以用集中式模型进行有效描述的问题,提出一种动车组运 行过程的分布式描述与建模方法. 基于动车组牵引/制动特性曲线和实际运 行数据,采用子空间模型辨识方法建立了动车组各动力单元的分布式状态空间模型;提出基于分布式模型的动车组预测控制方法,给出了各动力单元牵引/制动力和运 行速度同步跟踪控制算法.基于CRH380AL型动车组运行过程数据的对比仿真结果验证了本文方法的有效性.     

基于分布式状态空间模型预测控制的多级流程雁阵阵形调整

流程雁阵（process goose queue,PGQ）是一种新颖的流程工业系统分解协调优化方法．针对在过程干扰下多级流程雁阵的阵形调整问题,采用递阶求解的分布式模型预测控制算法,利用输入输出数据的Hankel矩阵,通过子空间辨识方法直接获取流程雁阵的脉冲响应序列,建立了预测控制算法．将此算法应用于一个氧化铝碳酸化分解过程,仿真验证了方法的有效性.     

城市路网交通分散协调控制

针对城市路网交通系统规模大和非线性、不确定性强等特点，利用模糊神经网络设计了一种新的实时分散协调控制算法．把城市区域和市内快速公路作为一个路网大系统，子系统为路网中的各个交叉口；每个子系统都有一个模糊神经网络控制器，该控制器根据它自己和相邻子系统的交通流信息来动态管理相序及绿灯时间．控制器由3个模块组成：相序选择模块、绿灯判断模块和相位切换模块．控制器的控制目标是保持快速公路主线密度均衡和区域内各车辆平均延误时间最短．仿真研究表明，该算法能有效处理各种路网交通环境．     

基于自适应动态规划的城市交通信号优化控制方法综述

针对日益受到重视的自适应动态规划(Adaptive dynamicprogramming, ADP)的方法和应用进行了概述. 详细分析了自适应动态规划的关键问题, 如收敛性、稳定性和协调性的研究现状和趋势. 介绍了城市交通信号控制问题的特点和目前采用的控制方法, 以及自适应动态规划方法在街区路口和快速路入口匝道的交通信号优化控制的应用现状和前景.     

基于串联结构的分布式模型预测控制

分布式模型预测控制(Distributed model predictive control, DMPC)是一类用于多输入多输出的大规模系统的控制方式.每个智能体通过相互协作完成整个系统的控制. 已有的分布式预测控制算法可以划分为迭代式算法和非迭代算法:迭代算法在迭代到收敛情况下,具有集中式预测控制(Centralized model predictive control, CMPC)算法的性能,但迭 代次数过多,子系统间通信量大;非迭代算法不需要迭代,但性能有一定损失.本文提出了一种基于串联结构的非迭代分布式预测控 制算法.本文算法在串联结构系统中可以有效减少计算量,并结合氧化铝碳分解(Alumina continuous carbonation decomposition process, ACCDP)这一串联过程,通过仿真验证了算 法的有效性;同时分析了算法运用在串联结构下的性能并证明了其稳定性.     

三维城市道路仿真模型构建

通过对现有城市道路模型的分析,提出了使用ArcGIS软件构建三维城市道路仿真模型。构造了道路几何网络模块、交通检测模块和控制管理设施模块的二维和三维模型。重点介绍了交叉口节点的二维模型建立,以及基于数字高程图得到城市道路的三维模型。最后将该模型应用于微观交通仿真系统中,可以为仿真系统中其它仿真模型提供空间参照、几何模型和逻辑模型,为各类交通行为数学模型提供存储空间和作用空间,为各种程序算法提供调度接口。与其它道路模型比,该模型能更准确、细致地反映道路网的拓扑关系和道路几何条件,从而为交通仿真模型真实地反映路网交通状态奠定了基础。     

城市高速公路交通的神经网络建模与控制

从城市高速公路交通流的宏观、动态特性出发 ,分析了交通流控制中常用的宏观、动态、确定性模型 在此基础上 ,利用人工神经网络技术建立了城市高速公路的神经网络模型 ,并提出了入口匝道放行和路段速度相结合的多变量神经网络控制策略 利用该控制策略建立的自适应神经网络控制器 ,可以使高速公路上的交通密度维持在理想的密度值附近 .进一步分析可以得到 ,该控制器是一个状态和控制作用均可跟踪的伺服系统 .以杭州某高架高速公路为背景的仿真结果表明 :该控制器具有较强的鲁帮性 ,控制效果令人满意 .     

风光互补发电系统的分布式模型预测控制

风光互补发电系统中,风力和光伏独立发电且两者在地理上相隔较远,彼此没有通讯交流.对此问题,提出用分布式模型预测控制的方法去解决.首先,在风光互补发电系统中存在大量的非线性环节,运用神经网络线性逼近,训练得出各个子系统的神经网络线性化模型.然后,在此基础上,基于风力优先发电、光伏配合、蓄电池必要时输出的原则,设计出满足相...     

无线传感网络中拥塞控制与路由的跨层设计:分布式牛顿法

无线传感网络应用广泛, 其性能与路由选择和拥塞控制密切相关. 致力于拥塞控制与多径路由的跨层优化, 以实现在链路容量受限和节点能量受限情况下的无线传感网络效用最大化. 针对对偶次梯度算法具有收敛速度慢与信息交互量大等缺陷, 设计了具有二阶收敛性能的分布式牛顿算法来实现网络效用最大化. 通过矩阵分裂技术, 实现了只需单跳信息交互的牛顿对偶方向的分布式求解方法. 仿真结果表明, 分布式牛顿算法的收敛性能显著优于对偶次梯度算法.     

分布式预测控制在空气分馏塔中的应用

在大型空分控制系统中,集中式控制由于在线计算量大,对计算机的要求较高。采用分布式预测控制,把复杂系统的控制过程分散到各个子系统中去,降低了计算的复杂度,同时提高了系统的可靠性。建立了分馏塔的数学模型,并拆分成若干个子系统,利用纳什优化原理,获得了全局最优解。仿真结果表明,该算法取得了较好的控制效果。