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.

Tri-level programming model for combined urban traffic signal control and traffic flow guidance

In order to balance the temporal-spatial distribution of urban traffic flow, a model is established for combined urban traffic signal control and traffic flow guidance. With consideration of the wide use of fixed signal control at intersections, traffic assignment under traffic flow guidance, and dynamic characteristics of urban traffic management, a tri-level programming model is presented. To reflect the impact of intersection delay on traffic assignment, the lower level model is set as a modified user equilibrium model. The middle level model, which contains several definitional constraints for different phase modes, is built for the traffic signal control optimization. To solve the problem of tide lane management, the upper level model is built up based on nonlinear 0-1 integer programming. A heuristic iterative optimization algorithm(HIOA) is set up to solve the tri-level programming model. The lower level model is solved by method of successive averages(MSA), the middle level model is solved by non-dominated sorting genetic algorithm II(NSGA II), and the upper level model is solved by genetic algorithm(GA). A case study is raised to show the efficiency and applicability of the proposed modelling and computing method.

FL-FN-MOGA Based Traffic Signal Control

In this paper, a traffic signal control method based on fuzzy logic (FL), fuzzy-neuro (FN) and multi-objective genetic algorithms (MOGA) for an isolated four-approach intersection with through and left-turning movements is presented. This method has an adaptive signal timing ability, and can make adjustments to signal timing in response to observed changes.The 'urgency degree' term, which can describe the different user's demand for green time is used in decision-making by which strategy of signal timing can be determined. Using a fuzzy logic controller, we can determine whether to extend or terminate the current signal phase and select the sequences of phases. In this paper, a method based on fuzzy-neuro can be used to predict traffic parameters used in fuzzy logic controller. The feasibility of using a multi-objective genetic algorithm ( MOGA) to find a group of optimizing sets of parameters for fuzzy logic controller depending on different objects is also demonstrated. Simulation results show that the proposed methed is effecfive to adjust the signal timing in response to changing traffic conditions on a real-time basis, and the controller can produce lower vehicle delays and percentage of stopped vehicles than a traffic-actuated controller.

NOVEL MULTIMEDIA TRAFFIC MODELING BASED CAC SCHEME FOR CDMA COMMUNICATION SYSTEMS

As the radio spectrum is a very scarce resource,the Call Admission Control (CAC) is one of the most important parts in radio resource management. The Code Division Multiple Access (CDMA) based next generation wireless communications systems will support the transmission of multimedia traffic,such as voice,video and data,thus the CAC,which can support the multimedia traffic and guarantee the Quality of Service (QoS) of different traffic,has gained broad attention. In this paper,a novel multimedia traffic modeling method and a corresponding dynamic QoS based CAC are proposed. The analysis and simulation results show that the proposed CAC scheme can guarantee the QoS to different traffic demand,and improve the system performance significantly.

城市干线信号控制策略综合评估体系

城市干线信号交叉口是路网交通流汇合和分离的节点,是保障城市交通顺畅通行的关键所在。面对信号控制策略评估方法单一的问题,构建了一种面向交通效率、交通安全和交通环境的综合指标体系,并利用组合赋权-模糊综合评价(fuzzy comprehensive evaluation,FCE)模型对城市干线信号控制方案展开综合评估。最后以太原市兴华街干线的三种信控方案为例,借助VISSIM软件和间接安全模型,获取所需的评价指标值,计算综合得分。结果表明,在城市干线中使用信号联控方案最优,能有效提高干线的综合水平,其评估结果与实际结果较为一致,验证了评估体系的有效性。研究结果为城市干线信号系统的规划与管理提供了理论依据与技术支撑。

新型混合交通流场景下交叉口信号控制和轨迹控制协同优化方法

针对人类驾驶车辆(human driven vehicle,HDV)和智能网联车辆(connected and autonomous vehicle,CAV)组成的新型混合交通流场景,现有的交叉口协同控制方法中,集中控制和单车控制分别对中央控制器的算力和车载计算单元的算力要求较高。本文研究了1种将元胞传输模型(cell transmission model,CTM)与双层规划模型相结合的协同优化方法,利用可调整的元胞长度平衡求解信号控制与CAV轨迹优化2个问题所需的算力,从而灵活地根据中央控制器和车载计算单元的算力分配计算资源;通过上层模型预测交通流状态并优化信号控制参数,引入动态调整元胞长度规则,降低中央控制器的计算负担;基于上层的交通状态预测结果,利用下层模型对CAV轨迹进行全局规划,进一步提升交叉口通行效率。同时,为了提升解的最优性和求解的实时性,采用结合随机梯度下降法和遗传算法的迭代优化算法,避免陷入局部最优的同时提升求解效率。最后以无锡市先锋中路与春风南路交叉口数据为例,验证了不同CAV渗透率下优化的效果,结果表明:①相较于基准方案,本文提出的协同优化方案最高可以降低交叉口8.09%的车均行程时间,降低了交叉口拥堵向上游的传播;②当CAV渗透率为30%、60%和90%时,优化比例为2.51%、5.08%和7.88%;③在进口道流量大于3000 pcu/h时,仍能在100s内获得最优信号控制方案,可支持实时优化。该方法可以有效改善城市交通拥堵,提高新型混合交通流场景下交叉口的通行效率。

基于模型偏差学习的交通信号自适应优化方法

城市交通信号配时优化是保证交通系统整体运行效率的前提条件。传统基于模型的信号控制方法往往基于历史统计数据,并且底层交通流模型不可避免地存在模型偏差,影响控制方案的科学性及其实际应用效果。考虑模型与数据融合驱动,提出一种基于模型偏差学习的交通信号控制自适应优化方法,以最小化路网行程时间为目标,建立信号控制最优化模型。首先,针对交通流预测模型的模型偏差,引入偏差函数表示预测模型与实际交通流状态间的误差;其次,建立基于径向基函数(RBF)神经网络的偏差函数,结合实际交通流数据对模型偏差进行学习,提高偏差函数的拟合效果;在此基础上,提出考虑模型偏差信息的自适应优化方法,以提高信号控制方法的控制性能。以小型测试路网与实际路网为例进行算例分析,基于SUMO仿真对本文提出的模型偏差学习方法进行验证,考虑不同交通流量条件,与固定配时、模型预测控制进行比较,分析其控制性能指标。结果表明,模型与数据融合驱动的信号控制自适应优化方法能有效提高预测模型的准确性,降低路网行程时间,与基于模型的控制和模型预测控制方法相比路网累积行程时间平均减少了38.3%和25.6%,提升了路网的实际运行效果。最后在宣城实际路网的仿真验证了方法的有效性。

SignalML结构模型和应用

交通信号控制硬件和软件技术的发展,产生了多种交通信号控制器之间的不兼容问题。为了实现交通信号控制器之间以及交通信号控制系统之间的信息交换,本文结合XML的发展,在NTCIP应用层上提出了SignalML信号控制标记语言,用来统一描述、包装、存储以及传递信号控制数据的格式,便于交通信号控制系统内部以及控制系统与其他外部交通系统之间可以基于这一标准方便地进行信号控制信息的交换和共享。本文尝试建立了SignalML的结构模型,最后运用该模型在北京市交通信号灯试验平台中进行了应用。

Model predictive control for hybrid vehicle ecological driving using traffic signal and road slope information

This paper presents development of a control system for ecological driving of a hybrid vehicle. Prediction using traffic signal and road slope information is considered to improve the fuel economy. It is assumed that the automobile receives traffic signal information from intelligent transportation systems （ITS）. Model predictive control is used to calculate optimal vehicle control inputs using traffic signal and road slope information. The performance of the proposed method was analyzed through computer simulation results. Both the fuel economy and the driving profile are optimized using the proposed approach. It was observed that fuel economy was improved compared with driving of a typical human driving model.

Intersection signal control multi-objective optimization based on genetic algorithm

A signal control intersection increases not only vehicle delay, but also vehicle emissions and fuel consumption in that area. Because more and more fuel and air pollution problems arise recently, an intersection signal control optimization method which aims at reducing vehicle emissions, fuel consumption and vehicle delay is required heavily. This paper proposed a signal control multi-object optimization method to reduce vehicle emissions, fuel consumption and vehicle delay simultaneously at an intersection. The optimization method combined the Paramics microscopic traffic simulation software, Comprehensive Modal Emissions Model （CMEM）, and genetic algorithm. An intersection in Haizhu District, Guangzhou, was taken for a case study. The result of the case study shows the optimal timing scheme obtained from this method is better than the Webster timing scheme.

Image-based traffic signal control via world models

Traffic signal control is shifting from passive control to proactive control, which enables the controller to direct current traffic flow to reach its expected destinations. To this end, an effective prediction model is needed for signal controllers. What to predict, how to predict, and how to leverage the prediction for control policy optimization are critical problems for proactive traffic signal control. In this paper, we use an image that contains vehicle positions to describe intersection traffic states. Then, inspired by a model-based reinforcement learning method, DreamerV2,we introduce a novel learning-based traffic world model. The traffic world model that describes traffic dynamics in image form is used as an abstract alternative to the traffic environment to generate multi-step planning data for control policy optimization. In the execution phase, the optimized traffic controller directly outputs actions in real time based on abstract representations of traffic states, and the world model can also predict the impact of different control behaviors on future traffic conditions. Experimental results indicate that the traffic world model enables the optimized real-time control policy to outperform common baselines, and the model achieves accurate image-based prediction, showing promising applications in futuristic traffic signal control.

设置移位左转车道的不对称交通流信号交叉口设计及优化

常规的交通组织与信号控制处理不对称车流的效果不理想,会造成时间、道路资源的浪费。针对不对称交通流的特性,提出设置移位左转车道的不对称交通流交叉口信号控制优化方案。以直行车辆与左转车辆车均延误最小为目标,建立移位左转交叉口时空资源优化模型与车辆延误计算模型。通过研究交通流不对称程度与移位左转车道长度对左转车辆延误的影响,分析不对称交通流移位左转车道的适应性,并运用VISSIM软件对优化方案进行仿真。仿真结果表明:与交叉口现状相比,方案2中车辆的平均延误降低27.2%,平均排队长度降低29.7%,说明移位左转车道信号控制方案能极大改善不对称交通流交叉口车辆的行驶情况,减少车均延误时间,使车流运行更加顺畅。

大型多环道涡轮环交车道级信号控制方法

针对大型多环道环形交叉口车辆频繁换道和进出口处不同流向交通流冲突的问题,本文结合涡轮式环形交叉口在控制车辆换道方面的独特优势,提出车道级左转两步信号控制方法。首先,通过对第1和第2停车线的车辆到达-驶离曲线分析,建立以车辆平均延误最小为目标,信号周期、饱和度及绿灯时长等为约束的信号配时模型。其次,通过建模分析不同控制方法、不同车道数和中心岛半径对车辆平均延误的影响。结果表明:车道级左转两步信号控制方法相较于传统左转两步信号控制方法,延误水平更低,平均可降低6.13 s;随着车道数和交通量的增加,本文提出方法的车辆平均延误增长幅度和速度均较低;中心岛半径越大,车辆平均延误越大,但增加幅度较小。最后,以镇江市梦溪广场无控制大型多环道环形交叉口为例,VISSIM仿真表明:本文所提出的信号控制方法相较于传统左转两步信号控制方法,车辆平均延误降低8.91%,停车率降低6.85%。该信号控制方法结合涡轮式环形交叉口可有效改善大型多环道环形交叉口交通安全及通行效率。

考虑实时载客量的公交信号优先策略改进控制模型

传统的公交信号优先策略往往依据公交车到达交叉口时刻或公交车固定载客量,无法有效地反映公交车实际载客量影响。针对该问题,考虑公交车实时载客量,以通过交叉口所有乘客的人均延误最小为优化目标,建立了交叉口公交信号优先控制模型;将公交IC卡和GPS数据进行匹配,比对公交站点乘客上车数,推算公交站点乘客下车数,分析了公交车通过交叉口时的实时载客量;提出了公交信号优先改进策略,分析该策略下各相位延误,引入公交优先强度系数;构建了交叉口人均延误模型和公交信号优先控制模型,采用启发式搜索算法求解该模型。研究结果表明:与传统的公交信号优先策略相比,考虑实时载客量的改进绿灯延长、改进绿灯早启公交信号优先策略的周期人均延误分别降低了20.98%、18.11%。

平行流交叉口延误计算及信号配时鲁棒优化模型

为解决平行流交叉口在实际应用中因交通波动导致主预信号协调效果不佳、移位左转车道上车辆排队溢出等问题,研究了基于情景的鲁棒优化控制方法。通过解析平行流交叉口交通运行机理,确定了交通需求、饱和流率和运行车速的随机波动会影响其运行稳定性,以此关联时变交通供给与平行流交叉口控制的耦合特征。进而构建了车均延误平均值-标准差目标函数,利用权重系数直观反映决策者对通行效率和稳定性偏好程度。在此基础上,考虑主预信号协调控制、车道功能划分、车道清空等约束条件,建立了平行流交叉口鲁棒优化模型。结合移位左转车道上车辆运行规则,基于车辆到达-驶离图式推导给出延误计算模型。研究结果表明:延误模型仿真验证中,左转和直行车均延误相对误差绝对值的平均值不超过3%,且最大值不超过6%,拟合效果较好。案例分析中,鲁棒优化相对于确定性优化,在延误均值仅增加2.24%的情况下,延误标准差降低了21.23%,说明鲁棒优化在几乎不损失交叉口通行效率的前提下,提高了平行流交叉口运行稳定性,使信号控制更符合实际交通运行需要。敏感性分析中,目标函数值随移位左转车道长度、设计速度的增加呈先减后增的变化趋势,故设计阶段移位左转车道长度的取值应与交通需求相匹配,而主预信号直行相位差计算过程中设计速度的取值应比现场调查的实际运行速度平均值略大。

含扰动迭代学习补偿的城市交通信号预测控制方法

城市交通流具有随机性,导致存在诸多未知干扰,在一定程度上影响了交通流模型的质量,使得基于交通流模型的城市交通信号预测控制效果受到限制。已有城市交通信号预测控制方法大多是对控制目标和控制方法进行改进,忽略了模型建立过程中由于城市交通流随机性而带来的扰动。针对该问题,在宏观交通流模型的基础上建立路网路段模型,通过模型预测控制对交叉口的排队长度进行控制,同时利用城市路网交通流的周期性特征,通过迭代学习对交通流预测模型中的未知重复扰动进行补偿,以减少扰动对所建立路网路段模型的影响。在此基础上,提出一种含扰动迭代学习补偿的城市交通信号预测控制方法,有效结合迭代学习和模型预测控制的优势,通过改变路口信号时长使路网内的车辆分布更加均匀,提高路网最大通行能力。数学分析结果验证了该方法的收敛性。仿真结果表明,相比固定配时和不含迭代补偿的模型预测控制2种方案,在该方法下路网中车辆的平均停车次数分别减少23%和10%,车辆平均延误时间分别缩短16%和8%,车辆平均速度分别提高14%和5%。

城市路网交通信号分层分布式控制优化方法

合理的路网信号控制结构对提高城市交通系统运行效率具有重要意义。本文综合考虑控制性能和计算效率,提出路网交通信号分层分布式控制策略。在控制结构的上层,引入Webster方法实时更新周期时长;在下层,采用模型预测控制方法对交叉口信号控制问题进行建模,以最小化路网总行程时间为目标,优化信号绿信比,并利用Benders分解思路将原问题分解为独立求解单个交叉口信号配时方案的Primal问题和协调优化相邻交叉口间交通流交互作用的Master问题,提出一种基于Benders分解的双层分布式信号协调控制优化算法。通过两个实际路网算例,验证分层分布式控制方法的有效性,并针对上下层的控制方法设置对比实验。结果表明:基于Benders分解的分布式模型预测控制方法能求得接近集中式控制的整体优化解(路网总行程时间差在3.26%以下),在兼顾控制性能的同时大幅提升优化求解的计算效率,相较于集中式控制,计算时间减少的幅度可达42.24%;在不同实验场景下,分布式控制方法的控制效果均优于定时控制方法,路网总行程时间约减少9.40%~20.57%。此外,在上层加入周期优化层后,分层控制方法能根据实时交通状态调整周期时长,进一步提高交通系统的运行效率。

基于非线性模型预测控制的城市路网交通流优化

为提高城市路网设计效率,文中提出一种基于非线性模型预测控制的城市路网交通流优化方案。采用预测控制方法,利用模拟试验验证所提算法在道路网络中的应用,从而最大限度地提高路口的通行能力;同时,验证迭代式识别在车辆流量模型的参数识别中的正确性。研究结果表明:采用时变参数模式预测时,每个路口的队长度差异都是比较合理的,模型预测结果与仿真结果接近;采用该模型的预测控制算法可以对网络的实时状况进行实时修正,从而改善道路的运行性能,8:00时段道路阻塞状况的模拟结果证明该方法具有较好的性能。相比之下,当系统中产生随机扰动时,自适应迭代学习辨识算法可以实现对系统期望输出的快速跟踪,具有较快的收敛速度和良好的效果。

信号控制交叉口人均延误的影响因素研究

在利用人均延误进行信号控制交叉口优化时,研究者为了简化模型通常使用不同车型的车辆平均载客量和车均延误来估计人均延误。为揭示这一简化方法忽视的人均延误的影响因素,通过对传统Webster模型进行改进,从理论上证明了人均延误受到不同车辆载客量和车辆到达顺序的显著影响。进而提出一种融合改进的Webster模型和高斯混合模型的人均延误估计方法。利用VISSIM仿真软件在不同交通量、车辆到达顺序、车辆类型比例和车辆载客量分布组合的场景下测试所提出的估计方法。结果表明,信号控制交叉口人均延误受不同载客量车辆的到达顺序、车辆类型比例的影响,证明了所提出的估计方法通过反映这些影响因素能够对处于非饱和交通流条件下的人均延误及其极值进行更加准确的估计。

考虑停靠站影响的公交优先信号配时优化

为了解决城市道路交叉口及其公交停靠站附近交通拥堵频发现象,以城市道路交叉口及其下游公交停靠站为公交优先信号配时优化控制单元,以控制单元范围内的乘客出行总延误最小为控制目标,构建考虑下游公交停靠站影响的交叉口公交优先绿灯延长时间优化模型;结合南京市汉中门大街—北圩路交叉口及其下游汉中门大街—嫩江路公交停靠站晚高峰时段的乘客出行延误数据进行实例分析,结果显示优化后的公交优先信号配时方案的乘客出行总延误比优化前降低了8.38%。