Adaptive signal control and coordination for urban traffic control in a connected vehicle environment: A review

Existing signal control systems for urban traffic are usually based on traffic flow data from fixed location detectors.Because of rapid advances in emerging vehicular communication,connected vehicle(CV)-based signal control demonstrates significant improvements over existing conventional signal control systems.Though various CV-based signal control systems have been investigated in the past decades,these approaches still have many issues and drawbacks to overcome.We summarize typical components and structures of these existing CV-based urban traffic signal control systems and digest several important issues from the summarized vital concepts.Last,future research directions are discussed with some suggestions.We hope this survey can facilitate the connected and automated vehicle and transportation research community to efficiently approach next-generation urban traffic signal control methods and systems.

Real-Time Discrete Adaptive Control of Robot Arm Based on Digital Signal Processing

A discrete model reference adaptive controller of robot arm is obtained by integrating the reduced dynamic model of robot, model reference adaptive control （MRAC） and digital signal processing （DSP） computer system into an electromechanical system. With the DSP computer system, the control signal of each joint of the robot arm can be processed in real time and independently. The simulation and experiment results show that with the control strategy, the robot achieved a good trajectory following precision, a good decoupling performance and a high real-time adaptivity.

Performance Evaluation of Intelligent Adaptive Traffic Control Systems: A Case Study

Mashhad, the second largest city in Iran, like many other big cities, is faced with increasing traffic congestion owing to rapidly increasing population and annual pilgrimage. In recent years, Mashhad traffic and transportation authorities have been challenged with how to manage the increasing congestion with limited budgets for major roadway construction projects. Mashhad has recognized the need to improve the existing system capacity to get the most out of their cur- rent transportation system infrastructures. Since most of the delay times occur at signalized intersections, using an intelligent control system with proper capabilities to overcome the growing traffic requirements is recommended. Following comprehensive studies carried out with the aim of developing the Mashhad traffic control center, the SCATS adaptive traffic control system was introduced as the selected intelligent control system for integrating signalized intersections. The first intersection was equipped with this system in 2005. This paper describes the results of a field evaluation in which fixed actuated-coordinated signal timings are compared with those dynamically computed by SCATS. The ef- fects of this system on optimizing fuel consumption as well as reducing air pollutants are fully discussed. It is found that SCATS consistently reduced travel times and the average delay per stopped or approaching vehicle. The positive impact of adaptive traffic control systems on fuel consumption and air pollution are also highlighted.

Traffic Signals Control with Adaptive Fuzzy Controller in Urban Road Network

An adaptive fuzzy logic controller (AFC) is presented for the signal control of the urban traffic network. The AFC is composed of the signal control system-oriented control level and the signal controller-oriented fuzzy rules regulation level. The control level decides the signal timings in an intersection with a fuzzy logic controller. The regulation level optimizes the fuzzy rules by the Adaptive Rule Module in AFC according to both the system performance index in current control period and the traffic flows in the last one. Consequently the system performances are improved. A weight coefficient controller (WCC) is also developed to describe the interactions of traffic flow among the adjacent intersections. So the AFC combined with the WCC can be applied in a road network for signal timings. Simulations of the AFC on a real traffic scenario have been conducted. Simulation results indicate that the adaptive controller for traffic control shows better performance than the actuated one.

Connect & Drive:design and evaluation of cooperative adaptive cruise control for congestion reduction

Road throughput can be increased by driving at small inter-vehicle time gaps. The amplification of velocity disturbances in upstream direction, however, poses limitations to the minimum feasible time gap. This effect is covered by the notion of string stability. String-stable behavior is thus considered an essential requirement for the design of automatic distance control systems, which are needed to allow for safe driving at time gaps well below 1 s. Using wireless inter-vehicle communications to provide real-time information of the preceding vehicle, in addition to the information obtained by common Adaptive Cruise Control （ACC） sensors, appears to significantly decrease the feasible time gap, which is shown by practical experiments with a test fleet consisting of six passenger vehicles. The large-scale deployment of this system, known as Cooperative ACC （CACC）, however, poses challenges with respect to the reliability of the wireless communication system. A solution for this scalability problem can be found in decreasing the transmission power and/or beaconing rate, or adapting the communications protocol. Although the main CACC objective is to increase road throughput, the first commercial application of CACC is foreseen to be in truck platooning, since short distance following is expected to yield significant fuel savings in this case.

Multi-objective Optimization of Multi-Agent Elevator Group Control System Based on Real-time Particle Swarm Optimization Algorithm

In order to get a globally optimized solution for the Elevator Group Control System (EGCS) scheduling problem, an algorithm with an overall optimization function is needed. In this study, Real-time Particle Swarm Optimization (RPSO) is proposed to find an optimal solution to the EGCS scheduling problem. Different traffic patterns and controller mechanisms for EGCS are analyzed. This study focuses on up-peak traffic because of its critical importance to modern office buildings. Simulation results show that EGCS based on Multi-Agent Systems (MAS) using RPSO gives good results for up-peak EGCS scheduling problem. Besides, the elevator real-time scheduling and reallocation functions are realized based on RPSO in case new information is available or the elevator becomes busy because it is unavailable or full. This study contributes a new scheduling algorithm for EGCS, and expands the application of PSO.

Using Adaptive Discrete-Time Gas Supply Control for Long Pulse Arc Discharge of Ion Source on NBI

A control model of gas supply system is introduced for ion source and an adaptive discrete-time control algorithm to regulate the hydrogen injection. A real-time feedback control system （RFCS） is designed to control the gas supply for ion source based on the control model and the discrete-time control algorithm. The experimental results have proved that RFCS could regulate the gas supply smoothly, suppress the arc＇s abrupt over-current at the end of the ion source discharging, prolong the discharge pulse and stabilize the ion concentration. With RFCS, the ion source for neutral beam injection has reached its longest pulse with a length of 4.5 seconds in a stable status.

Real-Time Traffic Signal Timing for Urban Road Multi-Intersection

this paper develops a real-time traffic signal timing model which is to be integrated into a single intersection for urban road, thereby solving the problem of traffic congestion. We analyze the current situation of the traffic flow with release matrix firstly, and then put forward the basic models to minimize total delay time of vehicles at the intersection. The optimal real-time signal timing model (non-fixed cycle and non-fixed split) is built with the Webster split optimal model. At last, the simulated results, which are compared with conventional model, manifest the promising properties of proposed model.

Stability analysis of traffic flow with extended CACC control models

To further investigate car-following behaviors in the cooperative adaptive cruise control（CACC） strategy,a comprehensive control system which can handle three traffic conditions to guarantee driving efficiency and safety is designed by using three CACC models.In this control system,some vital comprehensive information,such as multiple preceding cars’ speed differences and headway,variable safety distance（VSD） and time-delay effect on the traffic current and the jamming transition have been investigated via analytical or numerical methods.Local and string stability criterion for the velocity control（VC） model and gap control（GC） model are derived via linear stability theory.Numerical simulations are conducted to study the performance of the simulated traffic flow.The simulation results show that the VC model and GC model can improve driving efficiency and suppress traffic congestion.

A multi process value-based reinforcement learning environment framework for adaptive traffic signal control

Realising adaptive traffic signal control(ATSC)through reinforcement learning(RL)is an important means to easetraffic congestion.This paper finds the computing power of the central processing unit(CPU)cannot fully usedwhen Simulation of Urban MObility(SUMO)is used as an environment simulator for RL.We propose a multi-process framework under value-basedRL.First,we propose a shared memory mechanism to improve exploration efficiency.Second,we use the weight sharing mechanism to solve the problem of asynchronous multi-process agents.We also explained the reason shared memory in ATSC does not lead to early local optima of the agent.Wehave verified in experiments the sampling efficiency of the 10-process method is 8.259 times that of the single process.The sampling efficiency of the 20-process method is 13.409 times that of the single process.Moreover,the agent can also converge to the optimal solution.

Real-time optimization of energy consumption under adaptive cruise control for connected HEVs

This paper presents a real-time energy optimization algorithm for a hybrid electric vehicle(HEV)that operates with adaptive cruise control(ACC).Real-time energy optimization is an essential ssue such that the HEV powertrain system is as efficient as possible.With connected vehice technique,ACC system shows considerable potential of high energy eficiency.Combining a classical ACC algorithm,a two-level cooperative control scheme is constructed to realize real-time power distribution for the host HEV that operates in a vehicle platoon.The proposed control strategy actually provides a solution for an optimal control problem with multi objectives in terms of string stable of vehicle platoon and energy consumption minimization of the individual following vehicle.The string stability and the real-time optimization performance of the cooperative control system are confirmed by simulations with respect to several operating scenarios.

基于双流自适应图卷积网络的管制员睡岗行为识别

为识别空中交通管制员的睡岗行为,减少管制差错,保障航空器飞行安全,提出了一种基于双流自适应图卷积网络的管制员睡岗行为识别方法。该方法设计双流网络分别处理管制员骨架的一阶信息和二阶信息,实现对骨架数据的充分提取;通过自适应学习的骨骼拓扑连接矩阵,挖掘管制员不同关节之间的功能连接关系;同时在卷积层引入时空通道注意力机制,增强管制员睡岗行为识别模型在时间、空间、通道3个方向提取重要信息的能力。仿真结果表明,该方法能有效识别管制员3种睡岗行为,相较于传统的时空图卷积网络,识别准确率提高了3.08百分点,达到95.03%,可以提高民航运行安全管理水平。

基于物联网的交通信号灯控制系统

随着城市化进程的不断加速,交通拥堵问题成为制约城市可持续发展的一大挑战。在智能交通系统中,交通信号灯作为关键组成部分,对道路交通的管理和优化发挥着至关重要的作用。为提高交通效率、减少交通拥堵,文章探讨了基于物联网的智能交通信号灯控制系统。首先,介绍了交通信号灯的系统组成。其次,针对交通信号灯控制的优化需求,提出一种基于物联网的自适应控制算法。最后,构建了交通信号灯仿真界面,并设计了交互控制按钮。实验结果表明,所提出的自适应控制算法能够有效提升道路通行效率,降低交通拥堵程度,为智能交通系统的进一步优化与发展提供了重要参考。

A model reference adaptive control based method for actuator delay estimation in real-time testing

Real-time testing provides a viable experimental technique to evaluate the performance of structural systems subjected to dynamic loading.Servo-hydraulic actuators are often utilized to apply calculated displacements from an integration algorithm to the experimental structures in a real-time manner.The compensation of actuator delay is therefore critical to achieve stable and reliable experimental results.The advances in compensation methods based on adaptive control theory enable researchers to accommodate variable actuator delay and achieve good actuator control for real-time tests.However,these adaptive methods all require time duration for actuator delay adaptation.Experiments show that a good actuator delay estimate can help optimize the performance of the adaptive compensation methods.The rate of adaptation also requires that a good actuator delay estimate be acquired especially for the tests where the peak structural response might occur at the beginning of the tests.This paper presents a model reference adaptive control based method to identify the parameter of a simplified discrete model for servo-hydraulic dynamics and the resulting compensation method.Simulations are conducted using both numerical analysis and experimental results to evaluate the effectiveness of the proposed estimation method.

基于电压偏差和SOC的电氢耦合系统多源协调控制策略

在公路直流微电网中,电动汽车充电行为的时间特性和充电方式特性会对直流母线电压造成不同程度的冲击,高渗透率的电动汽车负荷对“源-荷-储”协调控制提出新的挑战。通过分析公路直流微电网的交通负荷特性和微源特点,提出了一种基于直流母线电压偏差的电氢耦合系统多源协调控制策略。此控制策略以蓄电池作为调压核心元件,通过检测变流器端口直流母线电压波动来实现协调自洽运行,无需通信即可实现系统的功率平衡。为避免蓄电池因荷电状态(SOC)过界而退出运行,设计了一种多源自适应下垂控制器,使氢能单元主动响应SOC变化,减少蓄电池SOC管理造成的不平衡功率,抑制SOC向边界值的趋近速度,实现SOC的动态平衡。最后,在Matlab/Simulink中验证了所提控制策略的有效性。

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。

基于改进遗传算法的离场航班时刻优化

为提高机场航班放行正常率,对离场航班时刻进行了智能优化方法研究。考虑进场航班时刻不变、离场容量和航班时刻调整范围受限等约束,构建了以全局时间调整偏差总量最小为目标的离场航班时刻优化模型;为提高优化效率,将遗传算法的交叉概率改进为自适应交叉概率;设计了一种基于改进遗传算法的离场航班时刻优化方法。以兰州中川国际机场全天运行455起降架次为例,对航班时刻进行优化和仿真验证。结果表明,优化的航班时刻相较于原航班时刻,航班平均延误时间降低12.8%;离场航班平均延误时间降低22.3%;离场航班延误架次减少了42.8%;航班放行正常率提高了12%。采用基于自适应交叉概率的遗传算法可有效降低了航班延误和提高航班放行正常率。

SDN在数据中心传输中的自适应流量管理研究

探讨软件定义网络(Software Defined Network,SDN)在数据中心传输中的自适应流量管理。SDN架构的关键组成要素包括控制器、应用层和南向接口,通过控制平面和数据平面的分离,实现了网络的灵活性和可编程性。OpenFlow协议作为关键通信协议在SDN中得到广泛应用,为控制器提供了调整数据平面行为的标准化手段。在自适应流量管理方面,控制器通过实时监测和智能调整网络状态,识别瓶颈和拥塞点,并根据不同应用的性能需求进行精确的流量管理决策。基于流和基于应用的自适应管理算法使网络能够灵活适应不同流量负载,提高资源利用效率。流量监测工具如NetFlow和sFlow以及反馈机制在实现自适应流量管理中发挥关键作用,实时感知和调整网络状态,使SDN网络更加智能、适应性更强,并提供了优越的应用体验。未来的研究方向将关注SDN中自适应流量管理的创新策略,推动网络技术不断进步。

Adaptive green traffic signal controlling using vehicular communication

The importance of using adaptive traffic signal control for figuring out the unpredictable traffic congestion in today's metropolitan life cannot be overemphasized. The vehicular ad hoc network(VANET), as an integral component of intelligent transportation systems(ITSs), is a new potent technology that has recently gained the attention of academics to replace traditional instruments for providing information for adaptive traffic signal controlling systems(TSCSs). Meanwhile, the suggestions of VANET-based TSCS approaches have some weaknesses:(1) imperfect compatibility of signal timing algorithms with the obtained VANET-based data types, and(2) inefficient process of gathering and transmitting vehicle density information from the perspective of network quality of service(Qo S). This paper proposes an approach that reduces the aforementioned problems and improves the performance of TSCS by decreasing the vehicle waiting time, and subsequently their pollutant emissions at intersections. To achieve these goals, a combination of vehicle-to-vehicle(V2V) and vehicle-to-infrastructure(V2I) communications is used. The V2 V communication scheme incorporates the procedure of density calculation of vehicles in clusters, and V2 I communication is employed to transfer the computed density information and prioritized movements information to the road side traffic controller. The main traffic input for applying traffic assessment in this approach is the queue length of vehicle clusters at the intersections. The proposed approach is compared with one of the popular VANET-based related approaches called MC-DRIVE in addition to the traditional simple adaptive TSCS that uses the Webster method. The evaluation results show the superiority of the proposed approach based on both traffic and network Qo S criteria.