前后车辆最优速度差跟驰模型与数值仿真

为了克服经典最优速度（OV）模型仅依据车间距单一因素调整跟驰车最优速度的局限性,在考虑前导车与跟驰车最优速度差信息对交通流稳定性影响的基础上,提出了一种改进的最优速度差（OVD）模型,并通过小振幅摄动法对模型进行了稳定性分析,推导了模型的稳定性条件。最后对改进OVD模型进行了数值仿真。通过线性稳定性分析,发现考虑前后车辆最优速度差后,自由流稳定的临界驾驶员反应灵敏度系数明显减小,稳定区域显著增加。仿真结果表明,与OV跟驰模型相比,在相同初始扰动条件下,改进OVD模型能够明显增强交通流的稳定性。     

一种改进全速度差模型的稳定性分析与仿真

针对传统全速度差（FVD）模型缺乏考虑前车最优速度影响的局限性,提出了一种改进的全速度差（IFVD）模型。在IFVD模型中,除了考虑跟驰车自身的最优速度和前车的速度差外,还进一步分析了多辆前车的最优速度信息对提高交通流稳定性的作用。并利用小振幅扰动法对模型的稳定性进行了分析,推导了模型的临界稳定性条件。最后通过数值模拟方法对FVD和IFVD模型进行了对比研究。研究结果表明,在相同的初始扰动条件下,考虑前车最优速度影响的IFVD模型能更有效地抑制交通流的堵塞,且随着考虑前车数量的增多,自由流稳定的灵敏度系数临界值在减小,稳定区域逐渐增大。     

基于改进最优速度的车辆跟驰模型及数值模拟

为了克服传统最优速度模型(OVM)单一考虑车间距对最优速度影响的不足,在同时考虑跟驰车与前导车之间的车间距和相对速度对后车跟驰行为影响的基础上,提出一种基于改进最优速度的车辆跟驰模型。通过对模型线性稳定性分析,得到改进模型的稳定性条件,并与OV模型进行比较,发现改进模型的稳定区域显著增加。最后对改进模型进行数值仿真,结果表明基于改进最优速度的车辆跟驰模型能明显增强交通流的稳定性。     

基于多前车信息融合的智能网联车辆跟驰模型

为了进一步提高交通流的稳定性,在经典基于驾驶员记忆的最优速度（OVCM）模型的基础上,提出了一种基于多前车最优速度与紧邻加速度（MHOVA）的智能网联车辆跟驰模型。首先,引入k辆前车的最优速度变化量与紧邻前车的加速度改进OVCM模型,并分别以参数γ和ω表示其权重;然后,结合改进模型利用线性稳定性分析获得交通流的临界稳定条件;最后,利用Matlab对车队施加扰动后的速度和车头距等参数进行数值模拟与分析。仿真结果表明：在车队启动和停止过程的仿真中,所提模型比OVCM模型使得车队整体达到稳定状态的时间更短;在环形道路上车队施加扰动的仿真中,所提模型相比于全速度差（FVD）模型、OVCM和多前车最优速度（MHOV）模型,在合理加速度敏感系数ω和前车数k约束下的速度和车头距波动幅度相对较小,尤其当ω为0.3且k为5时车辆速度的向上和向下波动率最小可达0.67%和0.47%,表明改进模型能较好地吸收交通扰动和增强车队行驶稳定性。     

高速公路驾驶员行为模型的建立与仿真

传统的高速公路模型往往忽略了驾驶员微观个性的差别,很难满足控制、诱导等方面的精度要求.本文依据实际数据聚类分析结果将高速公路上行驶车辆的驾驶员分为五类,提出新的换道模型,并对高速公路上的自由流和拥挤流两种交通流的速度一密度关系用模糊推理进行建模,然后用实际数据进行仿真;最后对仿真结果进行了讨论,分析了模型的可行性.     

考虑后视和多前车信息反馈的车辆跟驰模型

为改进车联网环境下车辆跟驰模型的稳定性,在经典OVCM模型基础上考虑后视效应、多前车速度差和多前车最优速度记忆综合信息对交通流稳定性能的影响,提出一种基于后视和多前车信息反馈的扩展车辆跟驰模型。根据线性稳定性分析法得出模型的中性稳定性判断条件,并进行数值仿真实验与分析。实验结果表明,在扰动初始条件设置一致下,所提模型相比于OV、FVD、OVCM模型,交通流稳定区域增大,速度波动幅度减小,特别是考虑的前车数k、后视敏感系数λi和记忆效应敏感系数γi取值为k=3,λi=［0.2,0.15,0.1］,γi=［0.1,0.08,0.06］时,车辆的平均速度波动率低于0.1%,由此说明,所提模型能有效减少扰动影响,增强交通流的稳态保持。     

一种基于改进优化速度函数的跟驰模型及仿真研究

在全速度差(FVD)模型的基础上,提出一种基于改进优化速度函数的跟驰模型。在改进模型中,优化速度函数除了考虑车间距和速度差的影响外,还进一步利用了前导车的加速度信息以提高交通流的稳定性。利用小振幅扰动法对模型进行线性稳定性分析,得到了模型的稳定性条件。最后与FVD模型进行比较,发现改进模型中自由流稳定的灵敏度系数临界值变小,稳定区域有明显增加。数值模拟结果表明,基于改进优化速度函数的跟驰模型能有效抑制交通流的堵塞。     

基于智能交通信息的预期与适应性元胞传输模型

动态智能交通(ITS)信息在微观层面提升驾驶员的预测能力和适应能力,在宏观层面表现为车队群体的预期性和适应性.本文引入期望密度/速度函数以及预期性和适应性调节系数扩展了元胞传输模型,弥补了原始模型未考虑ITS信息影响的不足.线性稳定性分析和收敛性仿真结果表明预期性和适应性会减少弛豫带来的负效应,增大交通流的线性稳定域,不同的状态调节系数组合刻画了不同程度的预期性和适应性,改变了平衡曲线与收敛速度,智能交通信息将有益于辅助安全驾驶和减少道路拥堵.     

考虑后视和最优速度记忆的跟驰模型及仿真

为提高交通流的稳定性,在考虑后视效应和速度差信息（Backward Looking and Velocity Difference,BLVD）模型的基础上,综合考虑后视和最优速度记忆效应,提出了一个扩展的跟驰模型。采用线性稳定性分析,推导出该模型的交通流稳定判据,发现在模型中引入后视和最优速度记忆效应的共同作用后,交通流的稳定区域有明显增大。通过数值仿真验证了理论分析,仿真结果表明：在初始扰动相同的条件下,与BLVD模型相比,新提出的扩展模型具有更好的交通流致稳性能。最后,使用NGSIM数据对所提出的跟驰模型进行参数标定和评价,证明其能更准确地刻画车流演变规律。     

基于Agent的驾驶员-车辆建模研究与实现

微观交通仿真技术是解决城市交通网络拥塞问题的有效工具.在各种交通元素中,驾驶员-车辆单元是交通流的主体.该文通过对交通流中驾驶员、车辆特性的分析,将驾驶员和车辆视为一个整体,并基于Agent理论对之进行建模.文中详细分析了Agent的生成和路径搜索及选择算法,研究了Agent的跟驰、换道和挤占道等行为.该文最后介绍了基于Agent模型建立的一种微观交通仿真器,并在该仿真器上进行仿真实验.结果表明该系统能够较好的仿真实际交通流状况.     

受天气与交通事故影响的安全路线选择模型

为完善安全为第一因素的路线规划导航系统,突出“以人为本”的理念,基于OD（Origin-Destination）的路段选择,综合考虑贵阳市交通事故数据和天气数据两大影响线路风险系数的因素。建立泊松分布模型和马尔可夫模型,分析区域内交通事故易发点在晴天、阴天、雨天发生交通事故点概率和路线概率,构建风险系数指标。针对模型求得的路线安全系数采用最大决策法选出最优安全路线。结果表明,贵阳市不同天气下发生的交通事故频率有差异,检验每个地点发生交通事故数服从泊松分布;现有导航软件推荐的第一路线并不是最优路线,第二条路线才是安全系数最大的路线。基于泊松分布模型和马尔可夫模型计算交通事故发生概率,为现有导航软件中增添了安全系数为第一参考值的人性化设置,有较好的社会价值和推广性。     

k最近邻流序列算法对异常流检测的优化研究

通过时空异常流检测技术可以发现城市交通数据中的异常交通特征。与时间序列中单个异常流检测采用的方法不同,提出了从流序列中检测异常流分布的k最近邻流序列算法（kNNFS）。算法首先为每个位置测定每个时间区间内的单个流观测值;随后计算单个流的观测频率来构建每个位置处每个时间区间的流分布概率库;最后由阈值判定使用KL散度计算的新的流分布概率与其k最近邻之间的距离是否为异常值,距离值小于阈值则更新入流分布概率库,否则为异常的流分布。仿真分析表明,对比DPMM算法和SETMADA算法,kNNFS算法在检测精度和算法运行时间方面均有优化提升。     

Research on the comprehensive traffic state evaluation model linked with drivers’ perception under the vehicle networking

Unified judgment standards and methods are often adopted to identify traffic state in certain road network based on traffic flow parameters. However, drivers often have different perceptions about the traffic state on different road sections, since their expectations on traffic state vary more or less from each other on different road sections. In particular, under the vehicle networking, out of considerations for safety and other relevant factors, requirements for the correlation and coordination of running vehicles have also raised significantly. Therefore, it is necessary to take driver’s perception about the driving conditions of certain road sections into consideration to adjust the release of traffic state. This paper has provided a comprehensive traffic state evaluation model linked with driver’s perception under the vehicle networking. The authors first establish an ANFIS model based on the T–S model and then conduct statistical analysis on drivers’ perceptions about certain traffic state. At last, the authors use the results of statistical analysis as regulatory factors to amend the parameters input through ANFIS. Through simulation, this paper has demonstrated that the model established has a high rate of convergence, a high identification precision and the generalization ability to conduct researches on the identification of traffic state.     

The influences of tunnel lighting environment on drivers’ peripheral visual performance during transient adaptation

Highway tunnel lighting environments include sidewall, pavement, ceiling, etc., their surface luminance were affected by the distribution of luminous flux of the lamps. While driving through a highway tunnel, the driver needs to complete a series of visual tasks. The spatial distribution of the luminance and color on the road surface and sidewall, as well as the correlated color temperature, influence the driver’s visual performance. The process of driving through a highway tunnel lighting environment was simulated in a laboratory where the luminance of the non-uniform visual environment was gradually decreased. The effects of the spatial distribution of the lighting environment parameters on a driver’s visual performance was studied by testing each subject’s reaction times and missed target rates. The tests showed that the spatial distribution of the lighting environment parameters significantly influences a driver’s visual performance, and the effects on test subjects’ peripheral visual performance are different in threshold, transition, and interior zones. The optimization of the spatial distribution of the lighting environment parameters can enhance traffic safety and energy saving of highway tunnel lighting.     

A game–theoretic analysis of the implications of overlay network traffic on ISP peering

Inter-ISP traffic flow determines the settlement between ISPs and affects the perceived performance of ISP services. In today’s Internet, the inter-ISP traffic flow patterns are controlled not only by ISPs’ policy-based routing configuration and traffic engineering, but also by application layer routing. The goal of this paper is to study the economic implications of this shift in Internet traffic control assuming rational ISPs and subscribers. For this purpose, we build a general traffic model that predicts traffic patterns based on subscriber distribution and abstract traffic controls such as caching functions and performance sensitivity functions. We also build a game–theoretic model of subscribers picking ISPs, and ISPs making provisioning and peering decisions. In particular, we apply this to a local market where two ISPs compete for market share of subscribers under two traffic patterns: “Web” and “P2P overlay”, that typifies the transition the current Internet is going through. Our methodology can be used to quantitatively demonstrate that (1) while economy of scale is the predominant property of the competitive ISP market, P2P traffic may introduce unfair distribution of peering benefit (i.e. free-riding); (2) the large ISP can restore more fairness by reducing its private capacity (bandwidth throttling), which has the drawback of hurting business growth; and (3) ISPs can reduce the level of peering (e.g. by reducing peering bandwidth) to restore more fairness, but this has the side-effect of also reducing the ISPs’ collective bargaining power towards subscribers.     

一种平稳化短时交通流预测方法

支持向量机回归模型是以预测噪声具有对称性概率分布为假设条件,而实际的短时交通流数据序列具有非平稳特征,这就使得在采用支持向量机回归模型进行短时交通流预测时,难以保证预测噪声的对称性概率分布,从而会影响到预测精度.针对上述问题,在证明支持向量机回归模型对平稳时间序列的预测噪声具有对称性概率分布的基础上,分别针对平稳化和未平稳化的短时交通流观测序列进行了仿真预测,并对预测结果进行了比对分析.分析结果表明,采用平稳化短时交通流预测方法可将预测的均方根误差降低约21.6％,绝对值误差降低约21.3％,相对误差降低约17.3％,仿真结果验证了所提方法的有效性.     

Development and application of an integrated traffic simulation and multi-driving simulators

Professional virtual reality experiment tools, including driving simulators and traffic simulators, have their strengths and weaknesses. The integration of the two simulators will enhance the ability of both traffic modeling and driving simulation and present a new area of applications. This paper develops, implements, and validates an experimental platform that integrated a traffic simulator with multiple driving simulators (TSMDS). As a connected multi-user framework that allows multiple drivers who are simultaneously handling many driving simulators, it not only allows driver behavior experiments to be more accurate, controlled, and versatile but also simulates special driving behavior or multi-vehicle interactions under more realistic traffic flow environments. To validate the performance of TSMDS, 27 drivers were recruited to attend the lane changing experiments at a recurring on-ramp bottleneck and left-turn experiments at a two-phase signalized intersection in Shanghai. Both experiments required several drivers to drive the TSMDS and fulfill several complicated lane changing/crossing behaviors through their interaction. The results show that both the participants’ response and lane changing/crossing data that were obtained from the experiment are consistent with the field observation, which confirms the validity of the integrated platform.     

Global sensitivity analysis based on Gini’s mean difference

Global sensitivity analysis has been widely used to detect the relative contributions of input variables to the uncertainty of model output, and then more resources can be assigned to the important input variables to reduce the uncertainty of model output more efficiently. In this paper, a new kind of global sensitivity index based on Gini’s mean difference is proposed. The proposed sensitivity index is more robust than the variance-based first order sensitivity index for the cases with non-normal distributions. Through the decomposition of Gini’s mean difference, it shows that the proposed sensitivity index can be represented by the energy distance, which measures the difference between probability distributions. Therefore, the proposed sensitivity index also takes the probability distribution of model output into consideration. In order to estimate the proposed sensitivity index efficiently, an efficient Monte Carlo simulation method is also proposed, which avoids the nested sampling procedure. The test examples show that the proposed sensitivity index is more robust than the variance-based first order sensitivity index for the cases with non-normal distributions.     

Car-following model with explicit reaction-time delay: linear stability analysis of a uniform solution on a ring

A microscopic car-following model with an explicit reaction-time delay is considered. The acceleration of every vehicle depends on its actual speed, the predecessor speed, and the distance between them. The acceleration function explicitly includes the driver’s reaction-time delay. On the one hand, this is an undoubted advantage of the model, and on the other hand, it significantly complicates the mathematical analysis. The subject of the study is the stability analysis of the uniform solutions on a ring. Using the linear stability analysis, we obtained the conditions on the model parameters including the driver’s reaction time, for which perturbations around the uniform solution decay. The calculations show that there are values for the model parameters, which satisfy the obtained conditions and at the same time provide a realistic dynamic of the vehicles. An important result is the fact that this model is able to reproduce such phenomenon as propagation of stop-and-go waves, which are widely known in traffic flow theory and are observed in practice. The latter is another advantage of the proposed car-following model.     

Analysis of a cellular automaton model for car traffic with a slow-to-stop rule

We propose a modification of the widely known Benjamin–Johnson–Hui (BJH) cellular automaton model for single-lane traffic simulation. In particular, our model includes a ‘slow-to-stop’ rule that exhibits more realistic microscopic driver behaviour than the BJH model. We present some statistics related to fuel economy and pollution generation and show that our model differs greatly in these measures. We give concise results based on extensive simulations using our system.