Effect of acceleration threshold on the phase transition in a cellular automaton traffic flow model

In this paper, we incorporate new parameters into a cellular automaton traffic flow model proposed in our previous paper [Jin et al. 2010 J. Stat. Mech. 2010 P03018]. Through these parameters, we adjust the anticipated velocity and the acceleration threshold separately. It turns out that the flow rate of synchronized flow mainly changes with the anticipated velocity, and the F →S phase transition feature mainly changes with the acceleration threshold. Therefore, we conclude that the acceleration threshold is the major factor affecting the F → S phase transition.

Phase transition in evolution of traffic flow with scale-free property

This paper investigates the behaviour of traffic flow in traffic systems with a new model based on the NaSch model and cluster approximation of mean-field theory. The proposed model aims at constructing a mapping relationship between the microcosmic behaviour and the macroscopic property of traffic flow. Results demonstrate that scale-free phenomenon of the evolution network becomes obvious when the density value of traffic flow reaches at the critical point of phase transition from free flow to traffic congestion, and jamming is limited in this scale-free structure.

Phase-plane analysis of conserved higher-order traffic flow model

The phase-plane analysis is used to study the traveling wave solution of a recently proposed higher-order traffic flow model under the Lagrange coordinate system. The analysis identifies the types and stabilities of the equilibrium solutions, and the overall distribution structure of the nearby solutions is drawn in the phase plane for the further analysis and comparison. The analytical and numerical results are in agreement, and may help to explain the simulated phenomena, such as the stop-and-go wave and oscillation near a bottleneck. The findings demonstrate the model ability to describe the complexity of congested traffic.

Prediction of elevator traffic flow based on SVM and phase space reconstruction

To make elevator group control system better follow the change of elevator traffic flow (ETF) in order to adjust the control strategy,the prediction method of support vector machine (SVM) in combination with phase space reconstruction has been proposed for ETF.Firstly,the phase space reconstruction for elevator traffic flow time series (ETFTS) is processed.Secondly,the small data set method is applied to calculate the largest Lyapunov exponent to judge the chaotic property of ETF.Then prediction model of ETFTS based on SVM is founded.Finally,the method is applied to predict the time series for the incoming and outgoing passenger flow respectively using ETF data collected in some building.Meanwhile,it is compared with RBF neural network model.Simulation results show that the trend of factual traffic flow is better followed by predictive traffic flow.SVM algorithm has much better prediction performance.The fitting and prediction of ETF with better effect are realized.

Cluster-size dependent randomization traffic flow model

In order to exhibit the meta-stable states, several slow-to-start rules have been investigated as modification to Nagel-Schreckenberg （NS） model. These models can reproduce some realistic phenomena which are absent in the original NS model. But in these models, the size of cluster is still not considered as a useful parameter. In real traffic, the slow-to-start motion of a standing vehicle often depends on the degree of congestion which can be measured by the clusters＇ size. According to this idea, we propose a cluster-size dependent slow-to-start model based on the speed- dependent slow-to-start rule （VDR） model. It gives expected results through simulations. Comparing with the VDR model, our new model has a better traffic efficiency and shows richer complex characters.

Traffic flow velocity disturbance characteristics and control strategy at the bottleneck of expressway

In the three-phase traffic flow studies, the traffic flow characteristic at the bottleneck section is a hot spot in the academic field. The controversy about the characteristics of the synchronized flow at bottleneck is also the main contradiction between the three-phase traffic flow theory and the traditional traffic flow theory. Under the framework of three-phase traffic flow theory, this paper takes the on-ramp as an example to discuss the traffic flow characteristics at the bottleneck section.In particular, this paper mainly conducts the micro-analysis to the effect of lane change under the two lane conditions, as well as the effect of the on-ramp on the main line traffic flow. It is found that when the main road flow is low, the greater the on-ramp inflow rate, the higher the average speed of the whole road section. As the probability of vehicles entering from the on-ramp increases, the flow and the average speed of the main road are gradually stabilized, and then the on-ramp inflow vehicles no longer have a significant impact on the traffic flow. In addition, this paper focuses on the velocity disturbance generated at the on-ramp, and proposes the corresponding on-ramp control strategy based on it, and the simulation verified that the control strategy can reasonably control the traffic flow by the on-ramp, which can meet the control strategy requirements to some extent.

Analytical investigation of the boundary-triggered phase transition dynamics in a cellular automata model with a slow-to-start rule

Previous studies suggest that there are three different jam phases in the cellular automata automaton model with a slow-to-start rule under open boundaries.In the present paper,the dynamics of each free-flow-jam phase transition is studied.By analysing the microscopic behaviour of the traffic flow,we obtain analytical results on the phase transition dynamics.Our results can describe the detailed time evolution of the system during phase transition,while they provide good approximation for the numerical simulation data.These findings can perfectly explain the microscopic mechanism and details of the boundary-triggered phase transition dynamics.

A cellular automaton model for a bridge traffic bottleneck

A cellular automaton （CA） model is proposed in this paper to analyze a bridge traffic bottleneck. The simulation results with this model show that there are several phase transitions in the traffic average density, velocity and flow for each lane under a periodic boundary condition. An unstable phase in the traffic average density and velocity for the upstream and downstream lanes of the bridge is shown in a range of initial traffic densities. The critical points of the phase transitions and the phenomenon of the unstable phase found in the simulation are also explained with the mean-field theory.

On the modeling of synchronized flow in cellular automaton models

In this paper, we further analyze our cellular automaton （CA） traffic flow model. By changing some parameters, the characteristics of our model can be significantly varied, ranging from the features of phase transitions to the number of traffic phases. We also review the other CA models based on Kerner＇s three-phase traffic theory. By comparisons, we find that the core concepts for modeling the synchronized flow in these models are similar. Our model can be a good candidate for modeling the synchronized flow, since there is enough flexibility in our framework.

融入智能网联汽车的混行交通流混沌特性

为了研究混行交通流混沌特性、辨析影响混行车队混沌程度的因素,在传统交通流理论基础上,利用Cao方法和改进的Cao方法确定混行交通流延迟时间和嵌入维数,对混行交通流序列进行相空间重构并通过计算最大Lyapunov指数判定其混沌特性.对混行交通流中智能网联汽车(intelligent connected vehicle,ICV)协同自适应巡航(cooperative adaptive cruise control,CACC)车辆比例及延迟时间关键参数进行影响分析.结果表明:在跟驰过程中车头间距序列的最大Lyapunov指数小于0时,混行交通流存在混沌;CACC车辆比例增加能够减弱混沌的时间区域,比如当CACC车辆比例达到0.6时,跟驰系统趋于稳定;CACC车辆的延迟时间对混沌的影响显著,保持低通信延迟才能发挥CACC车辆的作用,从而有效抑制混沌.

高速公路实时交通流模糊逻辑优化控制系统设计

针对高速公路交通流特征复杂多变,并受到天气、道路状况和车辆类型等多种因素影响的问题,设计了一种新的高速公路实时交通流模糊逻辑优化控制系统,包括硬件和软件两部分;在硬件方面,设计了车流控制模块、无线传输车载终端、实时交通流逻辑调度模块和模糊控制器模块,并确定了它们之间的实时连接关系;在软件方面,求解了交通流逻辑控制条件,联合了高速公路实时通行相位条件,调节了模糊控制器,并根据高速公路交通路网模型推导了交通流优化控制条件的求解表达式;实验结果表明,该系统可以解决逻辑协调错误的问题,并有效控制高速公路车流量。

道路施工作业区交通流相位分类及应用研究

为确定施工区交通流相位划分的最优聚类算法,本研究基于三相交通流理论,应用激光雷达、航空摄像等手段采集的施工区交通流运行数据,对PTV-Vissim仿真平台进行标定,搭建6种类型的施工区场景,通过仿真实验得到多样本的施工区交通流数据。论文选取层次聚类、高斯混合聚类、光谱聚类、DBSCAN聚类和K-Means聚类5种机器学习算法对施工区的交通流相位进行聚类,建立以轮廓系数、方差比准则和DB指数为基础的综合评分体系,并结合形态学评价方法判断各种聚类算法的优劣,选定层次聚类作为判定施工区交通流相位及其边界划分的最优方法,而后对交通流的相位形态作进一步分析。

基于相位相序的交叉口信号配时优化研究

交叉口是城市交通网络的瓶颈地带,交叉口信号配时优化是提高交叉口通行能力,改善道路运行状况的重要方法。分析相位相序对信号配时的影响和非对称相位的适用条件,针对部分交叉口非对称交通流的特征,改变通常情况下使用的对称相位设置方法,设置非对称相位,通过对进口车道渠化、相位相序的优化来提高绿灯时间的使用效率,提高交叉口的服务水平。使用SYNCHRO软件对交叉口对称相位配时方案和优化方案进行仿真,结果表明,非对称相位对减少非对称交通流的交叉口的延误和提高服务水平有着显著效果。

融合核极限学习机与PSR的混沌交通流预测

传统短时交通流预测精度低、稳定性差,提出一种结合改进蝴蝶算法优化核极限学习机与相空间重构的短时交通流预测模型。结合量子自适应种群初始化、邻域扰动和惯性权重对蝴蝶算法改进,利用改进蝴蝶算法对核极限学习机超参寻优。利用混沌理论确定样本时序最佳延迟时间和嵌入维数,利用PSR对样本重构,利用优化核极限学习机建立短时混沌交通流预测模型。采用郑州市某主干路口车流实测数据进行实证分析,其结果表明,改进模型能够有效降低预测误差,实现混沌交通流实时准确预测。

基于小波神经网络的流量混沌时间序列预测

在Takens提出的相空间重构模型基础上,应用小波变换对其进行改进,充分考虑噪声对重构结果的影响。将小波神经网络混沌时间序列预测方法引入网络流量预测中,介绍小波神经网络的基本构造和学习方法。实验表明,与RBF神经预测方法相比,小波神经网络预测方法的逼近效果更好、误差更小。

车辆长度和速度对单车道混合交通流的影响

以NaSch模型为基础,采用开放性边界条件,建立由两种长度不一样、最大行驶速度不同的车辆构成的单车道混合交通流模型。通过计算机模拟,研究了车辆的长度和速度对混合交通流的影响。

基于粗集理论和支持向量机的道路网短时交通流量预测

为了解决城市交通流量预测问题,研究道路网中多断面同时作用的情况,先将各断面交通流时间序列在相空间中重构,以充分提取交通流中的相关信息,然后应用粗集理论的强定性分析能力对输入信息进行约简,消除了样本中的噪声和冗余。在此基础上,再利用支持向量机对约简信息进行预测。为了获得最优的预测精度,该方法还利用遗传算法对预测进行了优化。实例研究表明,该方法的预测效果令人满意,在交通控制领域具有较大的应用潜力。

元胞自动机交通流NS模型相图研究

基于开放边界条件下元胞自动机交通流NS模型,模拟入口概率α、出口概率β、车辆最大速度Vmax以及随机慢化概率ρ对系统流量J的影响,根据不同流相中流量J的变化规律,建立NS模型的相图。结果表明,车辆最大速度在一定范围内的增加能提高系统流量,车辆的随机慢化概率在小于临界随机慢化概率ρc时,不会对系统流量造成影响,而超过ρc时,随机慢化概率越大,对系统流量的反作用越明显。Vmax和ρ决定系统相图。当Vmax=5,ρ<ρc时,系统相图只由自由流相和堵塞流相构成,自由流相和堵塞流相被一条曲线分开;ρ>ρc时,最大流相出现,最大流相区域随着ρ增大而增加。

基于统计特性的城市快速路交通流状态划分

目前交通流理论研究的主要对象为高速公路,与其相比,城市快速路交通流则具有明显不同的特点.交通科学是一门说证科学,在实证数据的基础上,首先对城市快速路交通流速度概率分布进行了研究,分析了在不同密度条件下速度概率分布的特性.在此基础上,在流—密平面中将交通流划分为自由流、谐动流、同步流和堵塞4个稳态相位,并具体讨论了各个相位的性质,定量标定了每个相位的区域范围.

交通流量的混沌特性分析及预测模型研究

基于混沌动力系统的相空间重构和非线性系统的Volterra级数,分析交通流量的混沌特性,研究了一种交通流量的自适应预测模型。在合理选取嵌入维数和延滞时间实现交通流量时间序列相空间重构的基础上,应用小数据量法计算重构交通流量时间序列的最大Lyapunov指数,根据该指数值对交通流量的混沌特性进行分析,并采用庞卡莱截面法对分析结果进行验证;构建交通流量的Volterra预测模型,并采用LMS自适应算法对模型系数进行调整。通过对实际采集的高速公路交通流量数据的仿真研究表明,小数据量法能对交通流混沌特性进行准确判别,构建的二阶Volterra自适应预测模型能够有效地预测交通流量的变化。因此,在判定交通流量存在混沌特性时,可以应用论文构建的二阶Volterra自适应预测模型对其进行准确的预测。