Short-Term Traffic Volume Forecasting Using Kalman Filter with Discrete Wavelet Decomposition

Abstract:   This article investigates the application of Kalman filter with discrete wavelet analysis in short-term traffic volume forecasting. Short-term traffic volume data are often corrupted by local noises, which may significantly affect the prediction accuracy of short-term traffic volumes. Discrete wavelet decomposition analysis is used to divide the original data into several approximate and detailed data such that the Kalman filter model can then be applied to the denoised data and the prediction accuracy can be improved. Two types of wavelet Kalman filter models based on Daubechies 4 and Haar mother wavelets are investigated. Traffic volume data collected from four different locations are used for comparison in this study. The test results show that both proposed wavelet Kalman filter models outperform the direct Kalman filter model in terms of mean absolute percentage error and root mean square error.     

基于小波变换的爆破振动时频特征分析

应用小波变换方法对短时非平稳爆破振动过程提出了时频特征分析。根据离散小波变换的分层分解展开关系，将爆破振动时间历史信号用分层重构信号进行扫描。应用这些信号可以给出不同频率带上爆破振动的相对能量分布和振动强度的时间变化规律。一个爆破振动实测结果的分析表明，与建立在传统Fourier变换基础上的频谱分析方法相比，基于小波变换的爆破振动时频特征分析可以给出更为准确的细节信息。文中的研究结果为爆破振动结构安全性分析提供了新的途径。     

Random Process Model for Urban Traffic Flow Using a Wavelet‐Bayesian Hierarchical Technique

Abstract: The existing well‐known short‐term traffic forecasting algorithms require large traffic flow data sets, including information on current traffic scenarios to predict the future traffic conditions. This article proposes a random process traffic volume model that enables estimation and prediction of traffic volume at sites where such large and continuous data sets of traffic condition related information are unavailable. The proposed model is based on a combination of wavelet analysis (WA) and Bayesian hierarchical methodology (BHM). The average daily “trend” of urban traffic flow observations can be reliably modeled using discrete WA. The remaining fluctuating parts of the traffic volume observations are modeled using BHM. This BHM modeling considers that the variance of the urban traffic flow observations from an intersection vary with the time‐of‐the‐day. A case study has been performed at two busy junctions at the city‐centre of Dublin to validate the effectiveness of the strategy.     

Decentralized damage identification using wavelet signal analysis embedded on wireless smart sensors

In this paper, a decentralized damage identification method using wavelet signal analysis tools embedded on wireless smart sensors (Imote2) has been proposed and experimentally validated. The damage identification analysis is decentralized by calculating discrete wavelet coefficients for acceleration in Imote2 sensors and transmitting the wavelet coefficients to a base station for damage identification through wavelet entropy indices. The wavelet entropy is modified to serve as a damage-sensitive signature that can be obtained both at different spatial locations and time stations to indicate existence of damage. It is known that wavelet-based approaches have clear advantages over Fourier transform-based ones for damage identification, since the wavelet transform allows for a wider choice of basis functions. This flexibility allows the wavelet transform to isolate changes in a signal that may be difficult to detect using other transform methods. To assess the reliability of the measurement signals, the wireless sensors have been compared with reference wired sensors. The proposed decentralized method for damage identification is verified via experimental tests using two laboratory structures: a three-story shear building structure and a three-dimensional truss bridge structure.     

Near-Term Travel Speed Prediction Utilizing Hilbert–Huang Transform

Abstract:  Accurate short-term prediction of travel speed as a proxy for time is central to many Intelligent Transportation Systems, especially for Advanced Traveler Information Systems and Advanced Traffic Management Systems. In this study, we propose an innovative methodology for such prediction. Because of the inherently direct derivation of travel time from speed data, the study was limited to the use of speed only as a single predictor. The proposed method is a hybrid one that combines the use of the empirical mode decomposition (EMD) and a multilayer feedforward neural network with backpropagation. The EMD is the key part of the Hilbert–Huang transform, which is a newly developed method at NASA for the analysis of nonstationary, nonlinear time series. The rationale for using the EMD is that because of the highly nonlinear and nonstationary nature of link speed series, by decomposing the time series into its basic components, more accurate forecasts would be obtained. We demonstrated the effectiveness of the proposed method by applying it to real-life loop detector data obtained from I-66 in Fairfax, Virginia. The prediction performance of the proposed method was found to be superior to previous forecasting techniques. Rigorous testing of the distribution of prediction errors revealed that the model produced unbiased predictions of speeds. The superiority of the proposed model was also verified during peak periods, midday, and night. In general, the method was accurate, computationally efficient, easy to implement in a field environment, and applicable to forecasting other traffic parameters.     

一种基于 Sobel 算子的小波与 IHS 遥感影像融合算法

针对IHS图像融合算法中颜色畸变比较明显的问题,提出一种新的基于小波与IHS相结合的遥感影像算法.经IHS变换的多光谱影像Mul的I分量与全色影像Pan由二维离散小波分解,对小波高频和低频分量采用不同融合规则:低频分量采用绝对值加权平均的方法,把两者的低频系数按其权值比例合成到新的分量I1中;高频系数采用基于区域分块的Sobel算子的绝对值取大. 实验结果与IHS法、传统小波与IHS结合法相比较,该算法能获取更多的光谱信息,人眼视觉效果也较好.     

Crack Detection from the Slope of the Mode Shape Using Complex Continuous Wavelet Transform

Abstract: A new method for cracks detection in beams is proposed by using the slope of the mode shape to detect cracks, and by introducing the angle coefficients of complex continuous wavelet transform. This study is aimed at detecting the location of the nonpropagating transverse crack. A series of beams with cracks that are simulated by rotational springs with equivalent stiffness are analyzed. The mode shape and the slope of this lumped crack model are calculated. Through complex continuous wavelet transform of the slope of the mode shape using Complex Gaus1 wavelet (CGau1), the locations of cracks are detected from the modulus line and the angle line of wavelet coefficients. By comparison, the singularity is much more apparent from the angle line of complex continuous wavelet transform. This demonstrates that the proposed method outperforms the existing method of wavelet transform of the mode shape with real wavelets. Also, this method can detect cracks in beams with different boundary conditions. The influence of crack locations and crack depth on crack detection is discussed. Finally, the noise effect is studied. Through the multiscale analysis, the locations of cracks may be detected from the angle of wavelet coefficients.     

Feature Extraction for Traffic Incident Detection Using Wavelet Transform and Linear Discriminant Analysis

To eliminate false alarms, an effective traffic incident detection algorithm must be able to extract incident-related features from the traffic patterns. A robust feature-extraction algorithm also helps reduce the dimension of the input space for a neural network model without any significant loss of related traffic information, resulting in a substantial reduction in the network size, the effect of random traffic fluctuations, the number of required training samples, and the computational resources required to train the neural network. This article presents an effective traffic feature-extraction model using discrete wavelet transform (DWT) and linear discriminant analysis (LDA). The DWT is first applied to raw traffic data, and the finest resolution coefficients representing the random fluctuations of traffic are discarded. Next, LDA is employed to the filtered signal for further feature extraction and reducing the dimensionality of the problem. The results of LDA are used as input to a neural network model for traffic incident detection.     

电梯选型配置与优化设计研究

针对高层建筑电梯垂直交通的特点,应用专家系统建立电梯交通流量预测模型,研究电梯智能交通理论和流量预测方法,为电梯最佳选型配置提供理论依据。通过在预测模型中设置映射变量,将预测模型推广到其它不同用途的高层建筑电梯交通流量预测中。提出外呼信号智能目的选层电梯群控策略;结合预测静态分区和智能动态分区,应用前向神经网络技术,以平均候梯时间、平均乘梯时间和目的楼层重复度为控制目标,寻找不同交通模式的最佳动态调度方法。分别对上行高峰、下行高峰、午餐上／下行高峰、随机层间交通及空闲交通5种模式进行建模,并实现预测模型与5种交通模式的模型嵌套。应用数据挖掘和小波分析技术,通过实时模拟运行,对电梯运行数据进行融合处理,对系统参数进行定时不定期在线自动整定修改,提高电梯运行效率和服务质量。研究电梯在风摆、火灾、地震等特殊情况下的运行模式,提出相应控箭和减灾措施,提高电梯运行安全和舒适性。     

Time-Frequency Signal Analysis of Earthquake Records Using Mexican Hat Wavelets

Abstract:   A method is presented for time-frequency signal analysis of earthquake records using Mexican hat wavelets. Ground motions in earthquakes are postulated as a sequence of simple penny-shaped ruptures at different locations along a fault line and occurring at different times. The single point source displacement of ground motion is idealized by a Gaussian function. For the purpose of signal analysis of accelerograms, the ground motion record generated by a simple penny-shaped rupture is used to form the basis wavelet function. After a careful study of the characteristics of various wavelet functions, the Mexican hat wavelet was found to be the most appropriate wavelet basis function to represent the acceleration of a single point source rupture. The result of the signal processing of an accelerogram is presented in the form of a scalogram using the coefficients of the continuous Mexican hat wavelet transform to describe the signal energy in the time-scale domain. The proposed signal processing methodology can be used to investigate the characteristics of accelerograms recorded on various types of sites and their effects on different types of structures.     

Network‐wide traffic speed forecasting: 3D convolutional neural network with ensemble empirical mode decomposition

Accurate traffic speed forecasting is one of the most critical tasks in proactive traffic management and the deployment of advanced traveler information systems. This paper proposes a hybrid forecasting approach named DeepEnsemble by integrating the three‐dimensional convolutional neural network (3D CNN) with ensemble empirical mode decomposition (EEMD). There are four steps in this hybrid approach. First, EEMD is adopted to decompose the complex traffic speed time series data with noise into several intrinsic mode functions (IMFs) and a residue. Second, a three‐dimensional tensor is established and fed into 3D CNN for prediction. Third, the output of 3D CNN prediction is obtained by a linear combination of the results of all components. Finally, the 3D CNN prediction output, external features, and historical features are fused to predict the network‐wide traffic speed simultaneously. The proposed DeepEnsemble approach is tested on the three‐month traffic speed series data of a real‐world large‐scale urban expressway network with 308 traffic flow detectors in Beijing, China. The experimental results indicate that DeepEnsemble outperforms the state‐of‐the‐art network‐wide traffic speed forecasting models. 3D CNN learns temporal, spatial, and depth information better than 2D CNN. Moreover, forecasting accuracy can be improved by employing EEMD. DeepEnsemble is a promising model with scalability and portability for network‐wide traffic speed prediction and can be further extended to conduct traffic status monitoring and congestion mitigation strategies.     

Fuzzy Modeling Approach for Combined Forecasting of Urban Traffic Flow

Abstract:   This article addresses the problem of the accuracy of short-term traffic flow forecasting in the complex case of urban signalized arterial networks. A new, artificial intelligence (AI)-based approach is suggested for improving the accuracy of traffic predictions through suitably combining the forecasts derived from a set of individual predictors. This approach employs a fuzzy rule-based system (FRBS), which is augmented with an appropriate metaheuristic (direct search) technique to automate the tuning of the system parameters within an online adaptive rolling horizon framework. The proposed hybrid FRBS is used to nonlinearly combine traffic flow forecasts resulting from an online adaptive Kalman filter (KF) and an artificial neural network (ANN) model. The empirical results obtained from the model implementation into a real-world urban signalized arterial demonstrate the ability of the proposed approach to considerably overperform the given individual traffic predictors .     

Wavelet-Based Generation of Energy- and Spectrum-Compatible Earthquake Time Histories

Abstract:  A method of modifying earthquake ground motion based on the wavelet transform is proposed, to take into account the effects of linear/nonlinear response spectra, frequency content, and ground motion energy. A wavelet-based procedure has been used to decompose recorded ground motion into finite wavelet coefficients, and then, with matrix processing, the coefficients have been suitably substituted and scaled to match the response spectra and total energy of earthquake ground motions. The proposed method has been verified by modifying five recorded accelerograms such that they are compatible with the same linear/nonlinear and energy spectra.     

基于Lyapunov指数的交通量混沌预测方法

交通量预测分析已成为交通工程领域重点研究课题 ,智能运输系统的核心研究内容之一。在判定交通流量存在混沌的前提下 ,对交通量的实测数据进行相空间重构 ,而后在重构相空间上 ,利用基于Lyapunov指数的混沌预测方法预测交通量。苏州某交叉路口的交通量预测实例表明了该方法用于交通量预测的有效性和可行性     

Identifying the Modal Parameters of a Structure from Ambient Vibration Data via the Stationary Wavelet Packet

Ambient vibration tests are conducted widely to estimate the modal parameters of a structure. The work proposes an efficient wavelet‐based approach to determine the modal parameters of a structure from its ambient vibration responses. The proposed approach integrates the time series autoregressive (AR) model with the stationary wavelet packet transform. In addition to providing a richer decomposition and allowing for an improved time–frequency localization of signals over that of the discrete wavelet transform, the stationary wavelet packet transform also has significantly higher computational efficiency than the wavelet packet transform in terms of decomposing time‐shifted signals because the former has a time‐invariance property. The correlation matrices needed in determining the coefficient matrices in an AR model are established in subspaces expanded by stationary wavelet packets. The formulation for estimating the correlation matrices is shown for the first time. Because different subspaces contain signals with different frequency subbands, the fine filtering property enhances the ability of the proposed approach to identify not only the modes with strong modal interference, but also many modes from the responses of very few measured degrees of freedom. The proposed approach is validated by processing the numerically simulated responses of a seven‐floor shear building, which has closely spaced modes, with considering the effects of noise and incomplete measurements. Furthermore, the present approach is employed to process the velocity responses of an eight‐storey steel frame subjected to white noise input in a shaking table test and ambient vibration responses of a cable‐stayed bridge.     

Temporal Evolution of Short-Term Urban Traffic Flow: A Nonlinear Dynamics Approach

Abstract:   Recognizing temporal patterns in traffic flow has been an important consideration in short-term traffic forecasting research. However, little work has been conducted on identifying and associating traffic pattern occurrence with prevailing traffic conditions. We propose a multilayer strategy that first identifies patterns of traffic based on their structure and evolution in time and then clusters the pattern-based evolution of traffic flow with respect to prevailing traffic flow conditions. Temporal pattern identification is based on the statistical treatment of the recurrent behavior of jointly considered volume and occupancy series; clustering is done via a two-level neural network approach. Results on urban signalized arterial 90-second traffic volume and occupancy data indicate that traffic pattern propagation exhibits variability with respect to its statistical characteristics such as deterministic structure and nonlinear evolution. Further, traffic pattern clustering uncovers four distinct classes of traffic pattern evolution, whereas transitional traffic conditions can be straightforwardly identified .     

An Adaptive Conjugate Gradient Neural Network–Wavelet Model for Traffic Incident Detection

Artificial neural networks are known to be effective in solving problems involving pattern recognition and classification. The traffic incident-detection problem can be viewed as recognizing incident patterns from incident-free patterns. A neural network classifier has to be trained first using incident and incident-free traffic data. The dimensionality of the training input data is high, and the embedded incident characteristics are not easily detectable. In this article we present a computational model for automatic traffic incident detection using discrete wavelet transform, linear discriminant analysis, and neural networks. Wavelet transform and linear discriminant analysis are used for feature extraction, denoising, and effective preprocessing of data before an adaptive neural network model is used to make the traffic incident detection. Simulated as well as actual traffic data are used to test the model. For incidents with a duration of more than 5 minutes, the incident-detection model yields a detection rate of nearly 100 percent and a false-alarm rate of about 1 percent for two- or three-lane freeways.     

Organized turbulent structures—Link between experimental data and LES

Methods of eddy structure identification are applied to velocity data of atmospheric surface layer flows modeled in a boundary-layer wind tunnel. The objective is to test their potential to serve as mathematical tools for the validation of eddy-resolving numerical models like large-eddy simulation and for the generation of realistic turbulent inflow conditions. The reconstruction of complex atmospheric flows on the basis of two-point space-time statistics is tested with the proper orthogonal decomposition and linear stochastic estimation that are both applied to spatially well-resolved flow data. The continuous wavelet transform is used to derive joint time-frequency information from single-point velocity time series. Whereas the proper orthogonal decomposition and the continuous wavelet transform show particular strengths in the spatiotemporal characterization of turbulent flows, the stochastic estimation is moreover qualified to generate new flow scenarios from a minimum number of instantaneous data.     

Estimation of Frequency-Dependent Strong Motion Duration Via Wavelets and Its Influence on Nonlinear Seismic Response

Abstract:   A procedure for estimation of frequency-dependent strong motion duration (FDSMD) is developed. The proposed procedure utilizes the continuous wavelet transform and is based on the decomposition of the earthquake record into a number of component time histories (named "pseudo-details") with frequency content in a selected range. The "significant" strong motion duration of each pseudo-detail is calculated based on the accumulation of the Arias intensity (AI). Finally, the FDSMD of the earthquake record in different frequency ranges is defined as the strong motion duration of the corresponding pseudo-detail scaled by a weight factor that depends on the AI of each pseudo-detail. The efficiency of this new strong motion definition as an intensity measure is evaluated using incremental dynamic analysis (IDA). The results obtained show that the proposed FDSMD influence the peak response of short-period structures with stiffness and strength degradation .     

A Wavelet-Based Approach to Identifying Structural Modal Parameters from Seismic Response and Free Vibration Data

Abstract:   This work presents the use of a discrete wavelet transform to determine the natural frequencies, damping ratios, and mode shapes of a structure from its free vibration or earthquake response data. The wavelet transform with orthonormal wavelets is applied to the measured acceleration responses of a structural system, and to reconstruct the discrete equations of motion in various wavelet subspaces. The accuracy of this procedure is numerically confirmed; the effects of mother wavelet functions and noise on the ability to accurately estimate the dynamic characteristics are also investigated. The feasibility of the present procedure to elucidate real structures is demonstrated through processing the measured responses of steel frames in shaking table tests and the free vibration responses of a five-span arch bridge with a total length of 440 m.