Effects of anticipatory driving in a traffic flow model

Anticipation in traffic means that drivers estimate their leaders' velocities for future timesteps. In the article a specific stochastic car-following model with non-unique flow-density relation is investigated with respect to anticipatory driving. It is realized by next-nearest-neighbour interaction which leads to large flows and short temporal headways. The underlying mechanism that causes these effects is explained by the headways of the cars which organize in an alternating structure with a short headway following a long one, thereby producing a strong anti-correlation in the gaps or in the headways of subsequent cars. For the investigated model the corresponding time headway distributions display the short headways observed in reality. Even though these effects are discussed for a specific model, the mechanism described is in general present in any traffic flow models that work with anticipation.     

混有协同自适应巡航控制车辆的异质交通流稳定性解析与基本图模型

针对传统车辆和协同自适应巡航控制(cooperative adaptive cruise control,CACC)车辆构成的异质交通流,研究其稳定性与基本图模型.应用实车测试验证的CACC模型和智能驾驶员模型(intelligent driver model)分别作为CACC车辆和传统车辆的跟驰模型,建立异质流稳定性解析框架,研究不同平衡态速度、不同CACC车辆比例时的异质流稳定性.推导异质流基本图模型,并进行数值仿真实验.研究结果表明,在传统车辆稳定的速度范围,异质流处于稳定状态.在传统车辆不稳定的速度范围,CACC车辆比例增加以及平衡态速度远离9.6—18.6 m/s速度范围,均能够改善异质流的不稳定性.通行能力随着CACC车辆比例的增加而提高.此外,CACC模型的期望车间时距越大,异质流稳定域越大,但通行能力降低.因此,恒定车间时距CACC控制策略下的期望车间时距取值应权衡异质流稳定域和通行能力两个方面的影响.     

瓶颈处车辆横纵向行为建模与分析

车辆的横向偏移现象在现实的交通流中广泛存在, 交通瓶颈处的横向偏移现象往往更加显著. 车辆间横纵向的运动相互干扰, 使得瓶颈交通流组织十分混乱, 通行能力受到显著影响. 为了研究瓶颈处车辆横纵向行为规律及其对交通流的影响, 提出一个考虑横向偏移特征的车辆行为模型: 通过引入目标转向角概念,并结合经典优化速度模型, 给出了用于描述车辆的横纵向运动规律的运动方程, 同时通过分析车辆横向偏移特征, 制定了基于车辆行驶状态划分的目标转向角确定规则集. 数值模拟结果表明: 车辆的横向偏移会对交通流的运行产生影响, 在一定的横向偏移反应阈值下, 瓶颈处横向干扰于交通流的影响随着密度的增加而增加; 同时观察到了实际城市交通瓶颈的宏观及微观现象, 验证了模型的有效性. 关键词： 交通流 瓶颈 车辆行为 横向偏移特征     

考虑横向分离与超车期望的车辆跟驰模型

为了更加客观地描述实际的车辆跟驰行为, 在优化速度模型的基础上, 通过引入横向分离参数并提出超车期望和虚拟前车的概念, 建立了考虑横向分离与超车期望的车辆跟驰模型.对模型进行线性稳定性分析, 得到了模型稳定性条件, 发现车辆横向分离、超车期望和虚拟前车的位置的增加, 在车流密度较小、车速较快的情况下, 使得交通流稳定区域增大, 但在车流密度较大、车速较慢的情况下, 反而使得交通流稳定区域减小.数值模拟结果验证了模型稳定性分析的结果, 表明在交通瓶颈处等交通流密度较大、运行缓慢的区域, 为抑制交通拥堵, 应该限制车辆的横向偏移和超车行为的发生. 关键词： 交通流 车辆跟驰模型 横向分离 超车期望     

New insights into traffic dynamics: a weighted probabilistic cellular automaton model

From the macroscopic viewpoint for describing the acceleration behaviour of drivers, a weighted probabilistic cellular automaton model （the WP model, for short） is proposed by introducing a kind of random acceleration probabilistic distribution function. The fundamental diagrams, the spatiotemporal patterns, are analysed in detail. It is shown that the presented model leads to the results consistent with the empiricaZ data rather well, nonlinear flow-density relationship existing in lower density regions, and a new kind of traific phenomenon called neo-synchronized flow. Furthermore, we give the criterion for distinguishing the high-speed and low-speed neo-synchronized flows and clarify the mechanism of this kind of traffic phenomenon. In addition, the result that the time evolution of distribution of headways is displayed as a normal distribution further validates the reasonability of the neo-synchronized flow. These findings suggest that the diversity and the randomicity of drivers and vehicles have indeed a remarkable effect on traffic dynamics.     

Effect of looking backward on traffic flow in a cooperative driving car following model

An extended car following model is proposed by incorporating intelligent transportation system and the backward looking effect under certain condition in traffic flow. The neutral stability condition of this model is obtained by using the linear stability theory. The results show that anticipating the behavior of vehicles preceding and following one vehicle could lead to appreciable stabilization of traffic system. From the simulation of space-time evolution of the vehicle headways, it is shown that the traffic jam could be suppressed efficiently via taking into account the information about the motion of two preceding vehicles and one following vehicle, and the analytical result is consistent with the simulation one.     

Staggered car-following induced by lateral separation effects in traffic flow

This Letter develops a new staggered car-following model taking into consideration lateral separation effects. Time-to-collision, calculated using visual angle variables, is introduced to describe the lateral separation distance and improve the optimal velocity model. The analytical and numerical results show that the stability of traffic flow can gradually be enhanced with the increase of lateral separation effects. The asymmetry property of traffic flow is also investigated using the new model. The results imply that incorporating lateral separation effects into the car-following model leads to the suppression of traffic jams and greatly enhances the realism of the model.     

交通流问题的有限元分析与模拟(Ⅲ)

将交通流Zhang H M模型和吴正模型改进为包括车道数变化的情况,指出它们与传统的L-W理论模型方程的联系和区别,并将Runge-Kutta间断Galerkin有限元方法推广应用到这些模型的数值求解.数值模拟包括红绿灯、车道数改变和非平衡状态时的超车行驶等交通流现象.     

雨天高速公路车辆换道模型研究

降雨使得路面附着系数和驾驶员视距降低, 容易造成交通事故, 影响道路通行效率. 为了研究雨天天气下车辆的换道行为, 引入反映降雨对换道行为影响的路面附着系数参数和驾驶员反应时间参数, 并进行量化分析, 由此使得安全距离随降雨强度和车辆速度的变化而变化, 在考虑前车和后车速度差对换道行为影响的基础上, 建立了雨天高速公路车辆换道模型. 仿真分析表明, 在中密度区雨天换道率与晴天相比有明显下降, 最大降幅约为25%; 且改进模型再现了自由流、自由流在无外因影响下形成动态拥堵流以及阻塞流下车辆时走时停的现象; 在中密度和高密度交通流中, 雨天更易引起交通拥堵, 其道路时空图中拥堵出现的频率和持续时间均相应增大, 且车辆以低速度行驶的时间较晴天天气下高许多.     

Non-lane-based full velocity difference car following model

In order to describe car following behavior in real world, this paper presents a non-lane-based car following model by incorporating the effects of the lane width in traffic. The stability condition of the model is obtained by using the linear stability theory. And numerical simulation is carried out to validate the analytic results. The property of the model is investigated, and it is found that the proposed model can describe the phase transition of traffic flow and estimate the evolution of traffic congestion. The results implied that incorporating the lane width effects in car following model not only stabilize traffic flow and suppress the traffic jam, but also lower critical headway and increase capacity. Thus, the lateral separation effects greatly enhance the realism of car following models.     

Effect of driver over-acceleration on traffic breakdown in three-phase cellular automaton traffic flow models

Based on simulations with cellular automaton (CA) traffic flow models, a generic physical feature of the three-phase models studied in the paper is disclosed. The generic feature is a discontinuous character of driver over-acceleration caused by a combination of two qualitatively different mechanisms of over-acceleration: (i) Over-acceleration through lane changing to a faster lane, (ii) over-acceleration occurring in car-following without lane changing. Based on this generic feature a new three-phase CA traffic flow model is developed. This CA model explains the set of the fundamental empirical features of traffic breakdown in real heterogeneous traffic flow consisting of passenger vehicles and trucks. The model simulates also quantitative traffic pattern characteristics as measured in real heterogeneous flow.     

A control method applied to mixed traffic flow for the coupled-map car-following model

In light of previous work [Phys. Rev. E 60 4000 （1999）], a modified coupled-map car-following model is proposed by considering the headways of two successive vehicles in front of a considered vehicle described by the optimal velocity function. The non-jam conditions are given on the basis of control theory. Through simulation, we find that our model can exhibit a better effect as p = 0.65, which is a parameter in the optimal velocity function. The control scheme, which was proposed by Zhao and Gao, is introduced into the modified model and the feedback gain range is determined. In addition, a modified control method is applied to a mixed traffic system that consists of two types of vehicle. The range of gains is also obtained by theoretical analysis. Comparisons between our method and that of Zhao and Gao are carried out, and the corresponding numerical simulation results demonstrate that the temporal behavior of traffic flow obtained using our method is better than that proposed by Zhao and Gao in mixed traffic systems.     

A new dynamic model for heterogeneous traffic flow

Based on the property of heterogeneous traffic flow, we in this Letter present a new car-following model. Applying the relationship between the micro and macro variables, a new dynamic model for heterogeneous traffic flow is obtained. The fundamental diagram and the jam density of the heterogeneous traffic flow consisting of bus and car are studied under three different conditions: (1) without any restrictions, (2) under the action of the traffic control policy that restrains some private cars and (3) using bus to replace the private cars restrained by the traffic control policy. The numerical results show that our model can describe some qualitative properties of the heterogeneous traffic flow consisting of bus and car, which verifies that our model is reasonable.     

Modeling the heterogeneous traffic flow considering the effect of self-stabilizing and autonomous vehicles

With the increasing maturity of automatic driving technology,the homogeneous traffic flow will gradually evolve into the heterogeneous traffic flow,which consists of human-driving and autonomous vehicles.To better study the characteristics of the heterogeneous traffic system,this paper proposes a new car-following model for autonomous vehicles and heterogeneous traffic flow,which considers the self-stabilizing effect of vehicles.Through linear and nonlinear methods,this paper deduces and analyzes the stability of such a car-following model with the self-stabilizing effect.Finally,the model is verified by numerical simulation.Numerical results show that the self-stabilizing effect can make the heterogeneous traffic flow more stable,and that increasing the self-stabilizing coefficient or historical time length can strengthen the stability of heterogeneous traffic flow and alleviate traffic congestion effectively.In addition,the heterogeneous traffic flow can also be stabilized with a higher proportion of autonomous vehicles.     

多速混合车辆单车道元胞自动机交通流模型的研究

在交通流NS模型的基础上，建立了多速混合车辆单车道元胞自动机交通流模型， 通过计算机数值模拟，得到了混合车辆在不同参数下交通流模型的基本图，并对混合交通的 特性进行了分析和讨论. 关键词： 元胞自动机 混合交通流模型 计算机模拟     

基于改进时空Moran's I指数的道路交通状态特征分析

定义时空对象代替空间对象作为基本研究单元, 改进传统Moran's I指数为时空Moran's I指数, 以便适于分析道路交通的时空特性; 通过Moran散点图给出时空自相关意义下的交通流时空状态特征分类: 拥堵聚集、畅通聚集、拥堵异质和畅通异质.为说明改进时空Moran's I指数分析法的实际应用价值, 以北京市二环快速路外环方向的车流速度数据为例, 进行时空自相关指标计算, 并从全局与局部自相关两个方面分析时 空自相关指标所反映的交通状态时空分布与演化规律. 关键词： I指数')" href="#">改进Moran's I指数 交通状态 时空自相关 时空分布     

A new cellular automaton model accounting for stochasticity in traffic flow induced by heterogeneity in driving behavior

A new reliable cellular automaon(CA) model designed to account for stochasticity in traffic flow induced by heterogeneity in driving behavior is presented.The proposed model differs from most existing CA models in that this new model focuses on describing traffic phenomena by coding into its rules the key idea that a vehicle's moving state is directly determined by a driver stepping on the accelerator or on the brake(the vehicle's acceleration).Acceleration obeys a deformed continuous distribution function when considering the heterogeneity in driving behavior and the safe distance, rather than equaling a fixed acceleration value with a probability, as is the rule in many existing CA models.Simulation results show that the new proposed model is capable of reproducing empirical findings in real traffic system.Moreover, this new model makes it possible to implement in-depth analysis of correlations between a vehicle's state parameters.     

基于模糊神经网络的短时交通流预测方法研究

为满足交通控制和诱导系统的实时性需求,减少交通拥挤状况,降低交通事故突发频率,需要对短时交通流进行预测。当前的短时交通流预测方法是采用K-近邻的非参数回归对其进行预测,预测过程中没有将预测模型中关键因素对交通流的影响进行详细的说明,导致预测结果不准确,存在短时交通流预测误差较大的问题。为此,提出一种基于模糊神经网络的短时交通流预测方法。该方法首先以历史短时交通流数据样本序列为基础,将提取的关联维数作为短时交通流的混沌特征量,然后以该特征量为依据,对短时交通流数据进行聚类,使相同的短时交通流聚合类样本比不同的交通流聚合类样本更为贴近,采用高斯过程回归对短时交通流预测模型进行建设,建设过程中利用差分方法对短时交通流预测序列进行平稳化操作之后,对短时交通流预测模型进行训练,将GPR模型引入至短时交通流预测过程中,得到交通流预测方差估计值,并确定交通流预测值置信区间,由此实现短时交通流的预测。由此实现短时交通流的预测。实验结果证明,所提方法可以准确地预测交通运输系统的实时状况,为车辆行驶的最佳路线进行了有效引导,减少了自然影响方面和人为因素对短时交通流预测结果的干扰,为交通部门对交通路况的控制管理提供了依据。     

Heterogeneous traffic flow modeling with drivers' timid and aggressive characteristics

The driver's characteristics(e.g., timid and aggressive) has been proven to greatly affect the traffic flow performance,whereas the underlying assumption in most of the existing studies is that all drivers are homogeneous. In the real traffic environment, the drivers are distinct due to a variety of factors such as personality characteristics. To better reflect the reality,we introduce the penetration rate to describe the degree of drivers' heterogeneity(i.e., timid and aggressive), and proceed to propose a generalized heterogeneous car-following model with different driver's characteristics. Through the linear stability analysis, the stability conditions of the proposed heterogeneous traffic flow model are obtained based on the perturbation method. The impacts of the penetration rate of drivers with low intensity, the average value and standard deviation of intensity parameters characterizing two types of drivers on the stability of traffic flow are analyzed by simulation. Results show that higher penetration of aggressive drivers contributes to traffic flow stability. The average value has a great impact on the stability of traffic flow, whereas the impact of the standard deviation is trivial.