Driver choice compared to controlled diversion for a freeway double on-ramp in the framework of three-phase traffic theory

Two diversion schemes that apportion demand between two on-ramps to reduce congestion and improve throughput on a freeway are analyzed. In the first scheme, drivers choose to merge or to divert to a downstream on-ramp based on information about average travel times for the two routes: (1) merge and travel on the freeway or (2) divert and travel on a surface street with merging downstream. The flow, rate of merging at the ramps, and the travel times oscillate strongly, but irregularly, due to delayed feedback. In the second scheme, diversion is controlled by the average mainline velocities just upstream of the on-ramps. Driver choice is not involved. If the average upstream velocity on the mainline drops below a predetermined value (20 m/s) vehicles are diverted to the downstream ramp. When the average mainline velocity downstream becomes too low, diversion is no longer permitted. The resultant oscillations in this scheme are nearly periodic. The period is dominated by the response time of the mainline to interruption of merging rather than delayed feedback, which contributes only a minor component linear in the distance separating the on-ramps. In general the second scheme produces more effective congestion reduction and greater throughput. Also the travel times for on-ramp drivers are less than that obtained by drivers who attempt to minimize their own travel times (first scheme). The simulations are done using the Kerner-Klenov stochastic three-phase theory of traffic [B.S. Kerner, S.L. Klenov, Phys. Rev. E 68 (2003) 036130].     

基于LCEEMD的低信噪比拉曼光谱自适应去噪方法研究

在生物体拉曼光谱快速采集或低功率采集过程中,往往会获得低信噪比拉曼光谱。针对低信噪比光谱数据,提出应用补充总体经验模态方法(CEEMD)分解拉曼光谱,并且依据特征模态分量的归一化排列熵值(NPE)按比例扣除噪声成分的方法,称为局部补充总体均值经验模分解方法(LCEEMD)。LCEEMD方法不仅解决了经验模态(EMD)分解中高频信号与噪声的模态混叠问题,还有效降低了总体经验模态分解法(EEMD)中的残留噪声。仿真数据实验显示,LCEEMD方法在处理10db信噪比模拟光谱时获得了39.615 0 db信噪比,0.001 17标准差和0.999 9相关系数。在人体皮肤拉曼光谱试验中,LCEEMD方法滤波后数据准确呈现出角质层脂质酰胺I带激发拉曼强谱峰以及甘油三酸酯中(CO)酯微弱谱峰。在水稻叶片可溶性糖定量预测模型中,LCEEMD方法取得了0.871 7预测相关系数和0.912 0预测标准误差,优于EMD和EEMD软阈值去噪(0.511 4,1.647 8和0.638 2,1.508 8)。LCEEMD方法实施过程中,根据去噪性能指标反馈调整归一化排列熵阈值,直至获得最佳去噪效果,滤波过程无需参数设置,可以自适应实现。     

混合状态下城市快速路交通流短时预测

建立交通流短时预测状态空间模型, 研究混合状态下城市快速路交通流短时预测. 结合城市快速路自由流状态、拥挤流状态和阻塞流状态下交通流参数的时间和空间分布特性, 基于交通流守恒方程和速度动态模型, 借鉴偏微分方程组求解时空离散的思想, 建立三种状态下交通流短时预测模型; 同时考虑进出口匝道、车道数变更以及道路坡度等因素的影响, 将交通流短时预测模型转化为交通流短时预测状态空间模型, 实现混合状态下交通流短时预测. 研究表明, 该方法能够实现混合状态下道路网内的交通流短时预测, 预测精度可达90.23%. 相同条件下, 经典自回归滑动平均模型的预测精度仅为81%. 关键词： 交通流短时预测 自由流状态 拥挤流状态 阻塞流状态     

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。     

噪声模态单元预判的经验模态分解脉冲星信号消噪

为了改善脉冲星辐射脉冲信号的消噪效果, 提出了一种基于噪声模态单元预判的经验模态分解(EMD) 消噪声方法. 该方法首先利用EMD将含噪辐射脉冲信号分解为一组内蕴模态函数(IMF), 根据IMF系数的统计特性采用局部均方误差准则进行噪声模态单元预判, 并将噪声模态单元置零; 然后对噪声模态单元预判处理后的IMF以模态单元为基本单位进行最优比例萎缩消噪, 从而达到抑制噪声、保留信号的目的. 实验结果表明: 与Sure Shrink小波阈值法、Bayes Shrink小波阈值法和EMD模态单元比例萎缩法相比, 基于噪声模态单元预判的EMD消噪方法可以更有效地去除脉冲辐射信号中的噪声, 同时更好地保留信号突变处的细节信息特征, 在信噪比、 均方误差、峰值相对误差、峰位误差和相位误差等方面都有一定程度的改善. 关键词： 脉冲星信号消噪 经验模态分解 噪声模态单元预判 局部均方误差     

Relevant modes selection method based on Spearman correlation coefficient for laser signal denoising using empirical mode decomposition

Empirical mode decomposition (EMD) is a recently proposed nonlinear and nonstationary laser signal denoising method. A noisy signal is broken down using EMD into oscillatory components that are called intrinsic mode functions (IMFs). Thresholding-based denoising and correlation-based partial reconstruction of IMFs are the two main research directions for EMD-based denoising. Similar to other decomposition-based denoising approaches, EMD-based denoising methods require a reliable threshold to determine which IMFs are noise components and which IMFs are noise-free components. In this work, we propose a new approach in which each IMF is first denoised using EMD interval thresholding (EMD-IT), and then a robust thresholding process based on Spearman correlation coefficient is used for relevant modes selection. The proposed method tackles the problem using a thresholding-based denoising approach coupled with partial reconstruction of the relevant IMFs. Other traditional denoising methods, including correlation-based EMD partial reconstruction (EMD-Correlation), discrete Fourier transform and wavelet-based methods, are investigated to provide a comparison with the proposed technique. Simulation and test results demonstrate the superior performance of the proposed method when compared with the other methods.     

Stochastic modeling of the dynamics of incident-induced lane traffic states for incident-responsive local ramp control

Incident-induced traffic congestion has been recognized as a critical issue to solve in the development of advanced freeway incident management systems. This paper investigates the applicability of a stochastic optimal control approach to real-time incident-responsive local ramp control on freeways. The architecture of the proposed ramp control system embeds two primary functions including (1) real-time estimation of incident-induced lane traffic states and (2) dynamic prediction of ramp-metering rates in response to the changes of incident impacts. To accomplish the above two goals, a discrete-time nonlinear stochastic optimal control model is proposed, followed by the development of a recursive prediction algorithm. Based on the simulation data, the numerical results of model tests indicate that the proposed method permits relieving incident impacts particularly under low-volume and medium-volume conditions, relative to high-volume lane-blocking conditions. Particularly, the incident-induced queue lengths can be improved by 50.1% and 67.9%, compared to the existing ramp control and control-free strategies, respectively.     

EEMD自适应去噪在拉曼光谱中的应用

二代小波是公认较好的降噪手段,但是降噪效果依赖于基函数、分解层数和阈值等参数设置。经验模态分解(empirical mode decomposition, EMD)无需参数设定,按照频率特性将信号分解成本征模函数(intrinsic mode function, IMF),对IMF滤波,实现了信号自适应去噪。拉曼光谱中信号和噪声交叠集中在极高频段,EMD产生模态混叠问题,影响去噪效果。应用总体平均经验模态分解(ensemble empirical mode decomposition,EEMD)拉曼光谱克服了模态混叠,有效区分出高频信号和噪声,获得了与小波函数相似去噪效果。文中首先对一段非线性非平稳豆油脂拉曼光谱EMD分解,可见模态混叠,EEMD分解出清晰模态的特征分量。然后分别用快速傅里叶变换(fast Fourier transform,FFT)、小波变换(Wavelet)、EMD和EEMD处理含噪光谱,信噪比、均方根误差、相关系数三个方面指标表明FFT高频去噪效果最差,其次是EMD,恰当的Wavelet同EEMD效果相当,EEMD的优势是降噪过程的自适应。最后提出光谱时频分析方法和IMF噪声属性判别准则研究趋势。     

A method for extracting human gait series from accelerometer signals based on the ensemble empirical mode decomposition

In this paper,the ensemble empirical mode decomposition(EEMD) is applied to analyse accelerometer signals collected during normal human walking.First,the self-adaptive feature of EEMD is utilised to decompose the accelerometer signals,thus sifting out several intrinsic mode functions(IMFs) at disparate scales.Then,gait series can be extracted through peak detection from the eigen IMF that best represents gait rhythmicity.Compared with the method based on the empirical mode decomposition(EMD),the EEMD-based method has the following advantages:it remarkably improves the detection rate of peak values hidden in the original accelerometer signal,even when the signal is severely contaminated by the intermittent noises;this method effectively prevents the phenomenon of mode mixing found in the process of EMD.And a reasonable selection of parameters for the stop-filtering criteria can improve the calculation speed of the EEMD-based method.Meanwhile,the endpoint effect can be suppressed by using the auto regressive and moving average model to extend a short-time series in dual directions.The results suggest that EEMD is a powerful tool for extraction of gait rhythmicity and it also provides valuable clues for extracting eigen rhythm of other physiological signals.     

负载流量碰撞检测算法在动态交通调度中的应用

随着复杂的环状路段,交叉路段、立交路段拥堵情况的不断增加,传统的交通拥堵调控策略为静态控制策略,调节的效率不高,容易出现调度冲突的问题;为了提高交通运行效率,提出一种用于解决动态交通调度的三级碰撞概率检测算法;根据交通流量的动态变化情况,通过计算流量时域内的动态流量偏差,计算三级路径之间可能存在的流量冲突概率;根据计算不同级之间的冲突概率反映动态交通拥堵的可能,确定控制的方案;实验表明,三级流量碰撞概率算法有效地降低了车辆平均延误时间,该算法有效。     

EMD算法在动态光谱无创测量血红蛋白浓度中的应用

将经验模态分解(EMD)算法结合动态光谱理论中的频域提取算法用于血红蛋白浓度的无创测量。在体采集57例光电容积脉搏波,选取636.98～1 086.86 nm范围内的光谱数据进行分析。首先通过EMD方法分别对各个样本每个波长的光电容积脉搏波进行去噪预处理,再利用离散傅里叶变换提取脉搏波的峰峰值构成动态光谱,最后运用偏最小二乘方法对各样本的动态光谱和血红蛋白浓度建立模型。与未经EMD处理的数据建模结果相比,EMD处理后,血红蛋白浓度预测集的相关系数从0.879 8提高到0.917 6,预测集均方根误差从6.675 9 g·L-1减小到5.300 1 g·L-1,相对误差从8.45%减小到6.71%,建模精度有了较大的提高。结果表明,采取经验模态分解的算法进行光电采集数据的去噪预处理可以提高光谱数据的信噪比,进而可以提高血液成分无创测量的准确性。     

基于集合经验模态分解和奇异值分解的激光雷达信号去噪

为了提高差分光柱像运动激光雷达(DCIM雷达)探测信噪比,提出了一种基于集合经验模态分解(EEMD)和奇异值分解(SVD)的混合降噪法.由EEMD获得含噪信号多层模态分量,根据各模态分量之间互相关系数的差分量确定主要噪声并予以滤除,利用奇异值分解识别模态分量中的残余噪声并提取有用信号.利用混合降噪法EEMD-SVD和EEMD方法分别对模拟仿真信号和实测激光雷达信号进行降噪处理.结果表明,当模拟噪声标准差在0.05~0.2之间时,相比与未降噪直接反演的湍流廓线,EEMD-SVD方法降噪后反演的湍流廓线信噪比提高了2.718 7dB~6.921 5dB,相应的EEMD方法提高了1.446 1dB~3.366 1dB;两个不同时段DCIM雷达降噪前后反演廓线与探空廓线的对比发现,EEMD-SVD和EEMD两种方法降噪后反演廓线较之于未降噪的反演廓线,信噪比最大提高了2.526 5dB和2.155 6dB.EEMD-SVD的降噪效果优于EEMD,能够更有效地识别和滤除噪声,较大地提高了原始信号的信噪比,获得更准确的大气湍流廓线反演结果.     

基于CEEMD特征提取的无绝缘轨道电路补偿电容故障诊断

本文利用电路网络理论和传输线理论构建ZPW-2000A轨道电路传输模型,仿真并分析了补偿电容故障对轨面电压的影响,提出基于互补的总体经验模式分解(CEEMD)特征提取的补偿电容故障诊断方法。实验结果表明,相比于传统经验模式分解(EMD)和总体经验模式分解(EEMD),基于CEEMD特征提取的补偿电容故障诊断方法可以有效地克服EMD方法引起的模态混叠和能量泄露现象,减少EEMD方法在信号重构过程中的白噪声残留,为补偿电容的故障诊断提供了一种快速准确的方法,为保证信号传输质量提供了参考依据。     

Effects of the number of on-rampson the ring traffic flow

Since ramp is an important composition of traffic system and there often exist multi ramps in a traffic system, the number of ramps can have great effects on main road traffic and produce some complex phenomena. In this paper, we employ the model presented by Tang \textit{et al}. [2009 {\em Communications in Theoretical Physics} {\bf 51}({1}) 71] to further study the effects of the number of on-ramps on the stability of traffic flow on a ring road. The numerical results show that this model can reproduce some complex traffic phenomena resulting from multi on-ramps on the ring road and the effects of the number of on-ramps on traffic flow, but the phenomena and the effects are both related to the initial density of the main road.     

信号灯控制下的主道双车道入匝道系统交通流特性研究

采用元胞自动机模型研究了具有信号灯控制的主道为双车道的入匝道系统交通流特性.将信号灯设置在入匝道口处,通过信号灯来引导主道和匝道上的车辆通行.分析了信号灯控制对主道与匝道的车流量、系统通行能力以及入匝道口处的车流平均速度的影响.通过相图比较,说明信号灯控制的双车道入匝道系统能模拟出比信号灯控制的单车道入匝道系统更加符合实际的交通流特性.与姜锐提出的模型［Jiang R 2003 J. Phys. A 36 11713］结果相比,信号灯控制下的匝道系统的交通流状态得到改善并且道路通行能力有所提 关键词： 交通流 元胞自动机 入匝道系统 信号灯     

脉冲星信号的经验模态分解模态单元比例萎缩消噪算法

针对脉冲星信号的消噪问题, 提出了一种基于模态单元比例萎缩的经验模态分解(EMD)消噪方法. 利用经验模态分解将含噪脉冲星信号分解为一组内蕴模态函数(IMF), 将IMF中两个过零点间的部分定义为模态单元, 以模态单元为基本单位构造最优比例萎缩因子, 对IMF中的每个模态单元进行比例萎缩去噪, 进而建立基于模态单元比例萎缩的脉冲星信号滤波模型.对含噪脉冲星信号进行了消噪实验分析, 实验结果表明, 与小波硬阈值消噪法、比例萎缩小波消噪法和基于模态单元阈值的EMD消噪法相比, 该方法可以更有效地去除脉冲星信号中的噪声, 同时更好地保留了原信号中的有用细节信息. 关键词： 经验模态分解 脉冲星信号 模态单元比例萎缩 消噪     

考虑自适应巡航车辆影响的上匝道系统混合交通流模型

在考虑自适应巡航(adaptive cruise control, ACC)车辆的交通流模型的基础上, 建立了考虑ACC车辆影响的上匝道系统混合交通流模型, 研究ACC车辆引入对上匝道交通系统交通流的影响. 为了描述ACC车辆和手动驾驶车辆在交通流运行中的差异, 分别构建了基于常车头时距原则的ACC 车辆跟驰子模型和手动驾驶车辆MCD元胞自动机子模型; 基于上匝道车辆合流驶入主线的需求, 建立了换道子模型, 引入了表征驾驶员换道心理的参数λ. 通过对混合交通流模型进行数值模拟发现, ACC车辆的混入可以有效改善上匝道系统交通流的运行, 降低合流等事件对于交通流运行的影响, 抑制交通拥堵的时空范围及拥堵强度, 提高交通流的平均速度和流量. 此外在混合交通流模型中, ACC车辆期望车头时距H_d的减小与换道心理参数λ 的增大均可以提高混合交通流运行的速度和流量, 而合流区长度l_w对混合交通流影响则因上匝道车辆驶入概率的不同而存在差异.     

Road traffic noise prediction based on speed-flow diagram

The method enables calculation of the annual average sound level of the road traffic noise, when the characteristics of the speed-flow diagram are available: the average speed of freely cruising vehicles, capacity of the traffic flow, traffic speed at the traffic flow capacity, and the slope of the decreasing traffic speed versus traffic flow. Under any conditions, traffic congestion reduces the annual average sound level. The final conclusion is that strong traffic congestion cannot be ignored in noise prediction.     

道路弯道对交通流影响的研究

在经典NaSch模型的基础上,考虑行驶弯道的曲率半径、弧长以及路面的摩擦系数等对车辆运行的影响,提出一种改进的适应特殊路况的单车道元胞自动机模型.按此模型,通过计算机数值模拟,研究了具有不同曲率半径、不同弧长和不同摩擦系数的弯道对交通流的影响.模拟结果显示,改进后的模型能准确反映特殊路况对行驶车辆的影响,同时再现了与实际交通相一致的时停时走交通波等复杂的非线性现象,根据所反映出的实际交通现象,分析得出了增大弯道曲率半径和弯道处的摩擦系数,并尽量地减小弯道弧长,可以减少弯道处交通事故的发生,避免交通拥堵的发生,提高道路通行能力.     

A New Macro Model for Traffic Flow on a Highway with Ramps and Numerical Tests

In this paper, we present a new macro model for traffic flow on a highway with ramps based on the existing models. We use the new model to study the effects of on-off-ramp on the main road traffic during the morning rush period and the evening rush period. Numerical tests show that, during the two rush periods, these effects are often different and related to the status of the main road traffic. If the main road traffic flow is uniform, then ramps always produce stop-and-go traffic when the main road density is between two critical values, and ramps have little effect on the main road traffic when the main road density is less than the smaller critical value or greater than the larger critical value. If a small perturbation appears on the main road, ramp may lead to stop-and-go traffic, or relieve or even eliminate the stop-and-go traffic, under different circumstances. These results are consistent with real traffic, which shows that the new model is reasonable.