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 (QoS). 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 V2V communication scheme incorporates the procedure of density calculation of vehicles in clusters, and V2I 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 QoS criteria.     

基于路口相位方案优化设计的城市道路交通信号迭代学习控制方法研究

城市道路交叉口信号控制是交管工作持续关注的课题,关于协调好有限的道路资源与日益增长的交通需求之间的矛盾,有着至关重要的作用。由于道路自身条件约束,交通流的组成特点复杂,路网交通路呈现非线性动态特征,无法进行精准的数学建模控制。本文提出的迭代学习控制方法,根据交通流的组成和变化特点调整信号控制周期及有效绿灯时长,实现交通信号动态优化控制,保证车辆在路网中能够高效、平稳地通行,是针对非线性动态交通流的一种动态寻优控制算法,能够有效减少路口车辆等待时间、提高通行效率。考虑对不同相位设计方案的适应性,在传统配时优化模型的基础上,构建综合相位设计元素的交通信号迭代学习控制模型,并通过Vissim仿真软件和Python编程语言搭建仿真测试环境,验证了提出模型的有效性。     

基于信号灯状态的燃油最优车速规划与控制

基于车联网（Vehicular ad hoc networks,VANETs）进行车辆和信号灯的协同控制是下一代智能交通系统（Intelligent transportation systems,ITSs）中非常重要的核心技术之一.本文提出了一种预测信号灯信息的车辆低油耗环保驾驶控制系统.首先,根据道路信息和牛顿第二定律建立车辆动态模型,根据系统测量的信号灯状态信息,获得车辆避免刹车情况下通过前方信号灯的参考速度.然后,结合基于油耗模型和速度跟踪的综合优化指标,运用模型预测控制（Model predictive control,MPC）方法计算车辆的最优控制输入,并利用Laguerre函数方法对MPC问题进行求解.仿真表明,该系统可减少路口不必要的停车和刹车操作,节约燃油.     

监督交通量变化的多模型预测自适应交通信号灯控制

因信号设定时间和车流量动态行为引起的交通量变化是现代交通控制系统存在高度不确定性的主要因素.根据交通流量具有高峰期、正常期及突发超流量期的特点,本文提出了一种监督多模型交通流量建模方法,结合模型预测控制技术对交通信号灯进行优化式智能控制,对不同交通模式下交通流量的实时变化作出反应,在优化的模式下对关键主干道交叉路口的信号灯进行自适应调节,达到实现通行次数合理,车辆延误时间以及停车时间都减少的目的.仿真示例说明了该方法的有效性.     

Traffic information interpolation method based on traffic flow emergence using swarm intelligence

Traffic congestion has become one of the most pressing social problems in today’s society, and research into appropriate traffic signal control is actively underway. At present, most traffic signal control methods define traffic signal parameters on the basis of traffic information such as the number of passing vehicles. Installing sensors at a vast number of intersections is necessary for more precise and real-time adaptive control, but this is unrealistic from the viewpoint of cost. As an alternative, we propose a swarm intelligence-based methodology that creates routes with a similar traffic volume using the traffic information from intersections already equipped with sensors and interpolates this information in the intersections without sensors in real time. Our simulation results show that the proposed methodology can effectively create similar traffic routes for main traffic flows with high traffic volumes. The results also show that it has an excellent interpolation performance for heavy traffic flows and can adapt and interpolate to situations where traffic flow changes suddenly. Moreover, the interpolation results are highly accurate at a road link where traffic flows confluence. We also developed an interpolation algorithm that is adaptable to traffic patterns with confluence traffic flows. Experiments were conducted with a simulation of merging traffic flows and the proposed method showed good results.

     

平面交叉口交通流动态特征的神经网络模型

平面交叉口的交通控制是交通控制系统理论、方法与实践的重要组成部分，描述平面交叉口实时动态交通需求的模型是实时自适应信号控制的基础依据，本文首先对现有交叉口车流动态特征的研究方法进行描述，在分析车流动态特征产生机理的基础上，指出现有方法的不足，并提出神经网络模型为刻划交叉口动态特征。     

A multiagent systems with Petri Net approach for simulation of urban traffic networks

This paper presents a novel model framework for complex urban traffic systems based on the interconnection of a dynamical multi-agent system in a macroscopic level. The agents describe all the types of street segments, intersections, sources and sinks of cars, modelling the behavior of the flow of vehicles through them as simple differential equations. These agents include the phenomena of changes in the flow rate due to congestions, traffic signals and the density of the vehicles. Traffic signal changes are obtained by the evolution of Petri Nets, in order to represent a more real behavior. Therefore, a complex network can be constructed by the interconnection of the agents, in continuous time, and the Petri Nets, in a discrete-event behavior, becoming a hybrid and scalable system. In order to analyze the performance of the approach, a real set of streets and intersections in Montevideo City is studied. Also, the approach is compared with a simulation realized in the software TSIS-CORSIM, which contains real data of density of vehicles. The multi-agent system achieves comparable results, taking into account the differences in the level of details respect to TSIS-CORSIM. Thus, the results can represent the most important issues of vehicular traffic with less computational resources.     

交通灯运行时间自适应算法及其控制系统

城市交通拥堵具有严重的危害性, 直接导致时间延误、能源浪费和废弃物排放增加, 降低居民生活水平. 现阶段, 基于平面交叉路口交通灯切换时间相对固定, 恶劣天气或发生交通事故时路口经常发生交通堵塞的实际情况, 本文提出了一种平面交叉口交通拥堵多方向交通灯运行时间自适应算法, 采取视频图像处理算法判断道路交通拥堵情况, 根据路况设置交通灯的工作时间, 并设计了相应的控制系统. 仿真结果表明, 在高峰期时段, 此自适应算法的车辆通行效率高于传统的交通灯运行时间控制方法.     

A co-simulation,optimization, control approach for traffic light control with truck priority

Under certain conditions giving priority to trucks at signalized intersections will benefit all vehicles because of elimination of extra delays associated with stop and go trucks due to their size and slow deceleration and acceleration rates. In this paper, we develop, analyse and evaluate a traffic light control system for signalized intersections that takes into account the differences in dynamics and characteristics between trucks and passenger vehicles. The proposed traffic light control system combines simulation-based optimization techniques timing the baseline traffic signals and model-based active strategy giving priority to trucks when it is to the benefit of all vehicles involved. In order to overcome the computational constraints of the simulation-based approach, a multiple agent-based solution utilizing multiple simulators has been proposed for large scale road network applications. The evaluation results show consistent improvements in reducing the truck traffic delays (5% to 10%) and the number of truck stops without delaying passenger vehicles whose travel time and number of stops have also been reduced. The reductions of vehicle delays and number of stops lead to reduction in emission levels and fuel consumption for both trucks and passenger vehicles.     

基于分布估计算法的单路口交通信号控制

针对遗传算法求解城市道路交叉口信号控制存在的主要问题,以四相位交叉路口为研究对象,建立了以信号周期内车辆延误总时间最短为目标函数,以各相位有效绿灯时间为控制变量的单路口交通信号优化模型.并分别以整数编码的PBIL算法和实数编码的EMNA算法两种典型分布估计算法求解单路口交通信号优化问题.仿真结果表明,与传统遗传算法相比,两种分布估计算法均可用更小的种群规模快速高效地求得最优解.     

交叉口混合交通流微观控制模型

为构建智能网联汽车(CAV)和有人驾驶汽车(HDV)混合通行情况下的交叉口通行机制与控制方法, 本文提出CAV专用道条件下交叉口协同通行模型. 首先, 设计CAV专用道条件下的交叉口布置, 对交叉口进行网格化处理,将CAV通行时隙和HDV绿灯相位对交叉口某部分网格某时段的占用统一到交叉口时空资源描述框架下; 其次, 建立兼顾CAV与HDV的交叉口时空网格资源分配模型, 构建自适应信号灯控制算法和CAV轨迹规划算法; 再次, 以车辆最小延误为目标进行自适应信号灯配时优化和CAV轨迹优化; 最后, 选取广州某典型交叉口建立仿真实验对所提方法的有效性进行了验证.     

基于无线传感器网络的自适应交通灯控制系统

分析了现有信号灯控制系统的优缺点,提出了一种基于无线传感器网络的交通灯控制系统设计方案,利用敷设在路面上的携带超声波收发模块的传感器节点探测各方向车道上车流量,并根据车流量实时改变相应车道车辆放行时间,以提高道路利用率,减少拥堵现象。本系统可准确地获得车流量统计信息。对信号灯调度过程进行了建模分析,提出了自适应的调度算法,仿真实验结果表明,该算法能降低车辆平均等待时间,提高通行效率。     

Multi-criteria evolutionary optimization of a traffic light using genetics algorithm and teaching-learning based optimization

Today, the development of urbanization and increasing the number of vehicles has resulted in displeased consequences like traffic congestion and vehicle queuing. The vast majority of countries in the world encounter the challenge of the explosive rise in traffic demand. In this regard, it is necessary to meet traffic demand in transport networks, especially in metropolitans. In traffic management and shortening the trip duration, traffic lights on the signalized intersections play an essential role in urban pathways. This work provides a multi-criteria decision-making method for optimum traffic light control in an isolated corner. The main idea involves establishing a set of sub-optimal solutions for traffic light timing and selecting the best one among the diverse solutions. We have mathematically modelled the problem as an optimization problem to achieve an optimal solution with less waiting time for vehicles in intersections and the lowest cost. Genetic algorithm (GA) and Teaching-Learning-based Optimization (TLBO) are utilized for each phase to create a set of suitable timing scenarios. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used to identify the best scenario, considering both waiting vehicles and traffic capacity as decision criteria. Its efficiency has been demonstrated over three different traffic volumes. Also, in a real-world implementation, its practical capability has been approved at a crossroads in Mashhad, Iran. The simulations indicate the improvement in the number of vehicles waiting behind the crossroad and the traffic capacity by 10% and 6.76% compared to the existing signal timing of the studied intersection, respectively.     

城市交通信号局部博弈交互下的学习协调控制

针对传统分布式自适应交通信号控制协调效率受限,并且存在维数灾难问题,建立了城市区域交通信号控制系统模型,将其优化问题建模为局部交叉口交通信号博弈协调控制,提出基于交叉口交通信号控制agent局部信息博弈交互的学习算法。在学习过程中交叉口交通信号控制agent进行局部信息博弈交互,自主调整交通信号控制策略使其逐步学习到最优策略。通过设计不同的交通需求情景,对路网平均延误和平均停车次数进行加权构建性能评价指标,相对于遗传算法和感应控制方法,博弈学习取得更好的交通信号控制效果,其能收敛到最优性能评价指标,其具有更好的交通需求管控能力。     

时变论域下红绿灯配时的模糊控制

针对交叉口日益拥堵,交通信号灯配时设置的不合理问题,提出一种基于时变论域的模糊控制方法,给出了交通信号灯的实时配时方案。该配时方法通过动态地调整信号周期和绿灯时长,以匹配多变的交通流状况,实现了实时控制。以长沙市某十字路口为例验证了该方案的有效性,结果表明：与定时信号配时方法相比,该配时方法更有效的缓解了交叉口交通拥堵状况,增强了路口通行能力,减少了车辆延误时间。     

基于行人安全的交通信号灯智能控制算法研究

提出了一种基于深度确定性策略梯度(DDPG, deep deterministic policy gradient)的行人安全智能交通信号控制算法;通过对交叉口数据的实时观测,综合考虑行人安全与车辆通行效率,智能地调控交通信号周期时长,相位顺序以及相位持续时间,实现交叉路口安全高效的智能控制;同时,采用优先经验回放提高采样效率,加速了算法收敛;由于行人安全与车辆通行效率存在相互矛盾,研究中通过精确地设计强化学习的奖励函数,折中考虑行人违规引起的与车辆的冲突量和车辆通行的速度,引导交通信号灯学习路口行人的行为,学习最佳的配时方案;仿真结果表明在动态环境下,该算法在行人与车辆冲突量,车辆的平均速度、等待时间和队列长度均优于现有的固定配时方案和其他的智能配时方案。     

基于公交GPS数据的交叉口信号配时参数估计

为了利用公交GPS数据估计交叉口信号配时参数,提出一种改进插值法来估计交叉口单车行程时间;通过先聚类再分类划分信号周期,给出采用停车线附近的GPS数据序列估计周期边界及周期时长的方法;结合周期边界估计结果,提出一种新的红灯时长估计方法.在不同交通流量下对估计方法进行模拟及现场测试,结果表明,基于公交GPS数据的信号配时参数估计方法适用于不同交通流量,且估计效果明显优于现有方法.     

基于机器视觉的交叉口特种车辆快速通行技术研究

交叉口是道路网络中重要的交通节点,容易产生交通堵塞问题,为了在保证通行安全的情况下提高特种车辆的通行效率,研究基于机器视觉的交叉口特种车辆快速通行技术。优化通行基础采用图像采集及预处理、检测识别和通行控制作为技术框架结构,利用机器视觉技术采集交叉口实时交通图像,通过图像滤波、图像增强等步骤,实现初始图像的预处理。利用Car-YOLO网络识别交叉口通行能力,规划快速通行路线,考虑前车行驶状态,求解特种车辆通行速度,针对车辆所占车道,通过绿灯早启、绿灯周期时间延长等方式控制交叉口信号灯,实现交叉口特种车辆快速通行。实验结果表明:在拥堵和正常通行场景下,优化设计技术的特种车辆通过时间的平均值分别为18.2s、10.1s,事故发生概率分别低于2%、1.4%,具有较好的应用效果。     

Type-2 fuzzy logic based urban traffic management

This paper presents a multi-agent system based on type-2 fuzzy decision module for traffic signal control in a complex urban road network. The distributed agent architecture using type-2 fuzzy set based controller was designed for optimizing green time in a traffic signal to reduce the total delay experienced by vehicles. A section of the Central Business District of Singapore simulated using PARAMICS software was used as a test bed for validating the proposed agent architecture for the signal control. The performance of the proposed multi-agent controller was compared with a hybrid neural network based hierarchical multi-agent system (HMS) controller and real-time adaptive traffic controller (GLIDE) currently used in Singapore. The performance metrics used for evaluation were total mean delay experienced by the vehicles to travel from source to destination and the current mean speed of vehicles inside the road network. The proposed multi-agent signal control was found to produce a significant improvement in the traffic conditions of the road network reducing the total travel time experienced by vehicles simulated under dual and multiple peak traffic scenarios.     

一种数据丢包情况下的交叉口排队长度均衡控制方法

针对交通数据在传输过程中随机丢包造成交通拥堵的问题,提出一种新的交叉口排队长度均衡控制方法。考虑到交叉口交通控制的重复特性和强非线性,将无模型自适应迭代学习控制方案应用于交叉口排队长度控制中,通过实时调整各交叉口的信号配时方案来调节路口车辆的排队长度,实现各交叉口排队长度的均衡。针对道路交通网络控制中排队长度差值数据在传输过程中存在的丢包现象,将数据丢失现象描述为概率已知的伯努利序列,提出数据丢失情况下的补偿算法,即利用上次迭代的输出数据、伪梯度的估计值和控制输入差值对丢失数据进行补偿,解决存在数据丢包情况下多交叉口排队长度均衡控制问题。仿真结果表明,该方法在数据丢包的情况下迭代100次左右能够收敛于期望值并达到期望控制效果,验证了补偿算法的有效性。