The Experts below are selected from a list of 51480 Experts worldwide ranked by ideXlab platform
Adel W Sadek - One of the best experts on this subject based on the ideXlab platform.
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short term forecasting of Traffic Volume evaluating models based on multiple data sets and data diagnosis measures
Transportation Research Record, 2013Co-Authors: Lei Lin, Qian Wang, Adel W SadekAbstract:Although several methods for short-term forecasting of Traffic Volume have recently been developed, the literature lacks studies that focus on how to choose the appropriate prediction method on the basis of the statistical characteristics of the data set. This study first diagnosed the predictability of four Traffic Volume data sets on the basis of various statistical measures, including (a) complexity analysis methods, such as the delay time and embedding dimension method and the approximate entropy method; (b) nonlinearity analysis methods, such as the time reversibility of surrogate data; and (c) long-range dependency analysis techniques, such as the Hurst exponent. After the data sets were diagnosed, three models for short-term prediction of Traffic Volume were applied: (a) seasonal autoregressive integrated moving average (SARIMA), (b) k nearest neighbor (k-NN), and (c) support vector regression (SVR). The results from the statistical data diagnosis methods were then correlated to the performance res...
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a novel forecasting approach inspired by human memory the example of short term Traffic Volume forecasting
Transportation Research Part C-emerging Technologies, 2009Co-Authors: Shan Huang, Adel W SadekAbstract:Short-term Traffic Volume forecasting represents a critical need for Intelligent Transportation Systems. This paper develops a novel forecasting approach inspired by human memory, called the spinning network (SPN). The approach is then used for short-term Traffic Volume forecasting, utilizing a data set compiled from real-world Traffic Volume data obtained from the Hampton Roads Traffic operations center in Virginia. To assess the accuracy of the SPN approach, its performance is compared to two other approaches, namely a back propagation neural network and a nearest neighbor approach. The transferability of the SPN approach and its ability to forecast for longer time periods into the future is also assessed. The results of the performance testing conducted in this paper demonstrates the superior predictive accuracy and drastically lower computational requirements of the SPN compared to either the neural network or the nearest neighbor approach. The tests also confirm the ability of the SPN to predict Traffic Volumes for longer time periods into the future, as well as the transferability of the approach to other sites.
John Golias - One of the best experts on this subject based on the ideXlab platform.
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spatio temporal short term urban Traffic Volume forecasting using genetically optimized modular networks
Computer-aided Civil and Infrastructure Engineering, 2007Co-Authors: Eleni I Vlahogianni, Matthew G Karlaftis, John GoliasAbstract:Current interest in short-term Traffic Volume forecasting focuses on incorporating temporal and spatial Volume characteristics in the forecasting process. This paper addresses the problem of integrating and optimizing predictive information from multiple locations of an urban signalized arterial roadway and proposes a modular neural predictor consisting of temporal genetically optimized structures of multilayer perceptrons that are fed with Volume data from sequential locations to improve the accuracy of short-term forecasts. Results show that the proposed methodology provides more accurate forecasts compared to the conventional statistical methodologies applied, as well as to the static forms of neural networks.
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statistical methods for detecting nonlinearity and non stationarity in univariate short term time series of Traffic Volume
Transportation Research Part C-emerging Technologies, 2006Co-Authors: Eleni I Vlahogianni, Matthew G Karlaftis, John GoliasAbstract:Short-term Traffic Volume data are characterized by rapid and intense fluctuations with frequent shifts to congestion. Currently, research in short-term Traffic forecasting deals with these phenomena either by smoothing them or by accounting for them by nonlinear models. But, these approaches lead to inefficient predictions particularly when the data exhibit intense oscillations or frequent shifts to boundary conditions (congestion). This paper offers a set of tools and methods to assess on underlying statistical properties of short-term Traffic Volume data, a topic that has largely been overlooked in Traffic forecasting literature. Results indicate that the statistical characteristics of Traffic Volume can be identified from prevailing Traffic conditions; for example, Volume data exhibit frequent shifts from deterministic to stochastic structures as well as transitions between cyclic and strongly nonlinear behaviors. These findings could be valuable in the implementation of a variable prediction strategy according to the statistical characteristics of the prevailing Traffic Volume states.
Yuanchang Xie - One of the best experts on this subject based on the ideXlab platform.
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gaussian processes for short term Traffic Volume forecasting
Transportation Research Record, 2010Co-Authors: Yuanchang Xie, Kaiguang Zhao, Ying Sun, Dawei ChenAbstract:The accurate modeling and forecasting of Traffic flow data such as Volume and travel time are critical to intelligent transportation systems. Many forecasting models have been developed for this purpose since the 1970s. Recently kernel-based machine learning methods such as support vector machines (SVMs) have gained special attention in Traffic flow modeling and other time series analyses because of their outstanding generalization capability and superior nonlinear approximation. In this study, a novel kernel-based machine learning method, the Gaussian processes (GPs) model, was proposed to perform short-term Traffic flow forecasting. This GP model was evaluated and compared with SVMs and autoregressive integrated moving average (ARIMA) models based on four sets of Traffic Volume data collected from three interstate highways in Seattle, Washington. The comparative results showed that the GP and SVM models consistently outperformed the ARIMA model. This study also showed that because the GP model is formul...
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short term Traffic Volume forecasting using kalman filter with discrete wavelet decomposition
Computer-aided Civil and Infrastructure Engineering, 2007Co-Authors: Yuanchang Xie, Yunlong ZhangAbstract: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.
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a wavelet network model for short term Traffic Volume forecasting
Journal of Intelligent Transportation Systems, 2006Co-Authors: Yuanchang Xie, Yunlong ZhangAbstract:Wavelet networks (WNs) are recently developed neural network models. WN models combine the strengths of discrete wavelet transform and neural network processing to achieve strong nonlinear approximation ability, and thus have been successfully applied to forecasting and function approximations. In this study, two WN models based on different mother wavelets are used for the first time for short-term Traffic Volume forecasting. The Levenberg-Marquardt algorithm is used to train the WN models because it has better efficiency than the other algorithms based on gradient descent. Using the Traffic Volume data collected on Interstate 80 in California, the WN models are compared with the widely used back-propagation neural network (BPNN) and radial basis function neural network (RBFNN) models. The performance evaluation is based on mean absolute percentage error (MAPE) and variance of absolute percentage error (VAPE). The test and comparison results show that the WN models consistently produce lower average MAPE...
Byungkyu Park - One of the best experts on this subject based on the ideXlab platform.
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hybrid neuro fuzzy application in short term freeway Traffic Volume forecasting
Transportation Research Record, 2002Co-Authors: Byungkyu ParkAbstract:A hybrid neuro-fuzzy application for short-term freeway Traffic Volume forecasting was developed. The hybrid model consists of two components: a fuzzy C-means (FCM) method, which classifies Traffic flow patterns into a couple of clusters, and a radial-basis-function (RBF) neural network, which develops forecasting models associated with each cluster. The new hybrid model was compared with previously developed clustering-based RBF models. In addition, the dynamic linear model was studied for comparison. The study results showed that the clustering-based hybrid method did not produce time-lag phenomena, whereas the dynamic linear model and the RBF model without clustering revealed apparent time-lag phenomena. The forecasting performance for freeway Traffic Volumes from the San Antonio, Texas, TransGuide system shows that even though the hybrid of the FCM and RBF models appears to be promising, additional research efforts should be devoted to achieving more reliable Traffic forecasting.
Eleni I Vlahogianni - One of the best experts on this subject based on the ideXlab platform.
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spatio temporal short term urban Traffic Volume forecasting using genetically optimized modular networks
Computer-aided Civil and Infrastructure Engineering, 2007Co-Authors: Eleni I Vlahogianni, Matthew G Karlaftis, John GoliasAbstract:Current interest in short-term Traffic Volume forecasting focuses on incorporating temporal and spatial Volume characteristics in the forecasting process. This paper addresses the problem of integrating and optimizing predictive information from multiple locations of an urban signalized arterial roadway and proposes a modular neural predictor consisting of temporal genetically optimized structures of multilayer perceptrons that are fed with Volume data from sequential locations to improve the accuracy of short-term forecasts. Results show that the proposed methodology provides more accurate forecasts compared to the conventional statistical methodologies applied, as well as to the static forms of neural networks.
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statistical methods for detecting nonlinearity and non stationarity in univariate short term time series of Traffic Volume
Transportation Research Part C-emerging Technologies, 2006Co-Authors: Eleni I Vlahogianni, Matthew G Karlaftis, John GoliasAbstract:Short-term Traffic Volume data are characterized by rapid and intense fluctuations with frequent shifts to congestion. Currently, research in short-term Traffic forecasting deals with these phenomena either by smoothing them or by accounting for them by nonlinear models. But, these approaches lead to inefficient predictions particularly when the data exhibit intense oscillations or frequent shifts to boundary conditions (congestion). This paper offers a set of tools and methods to assess on underlying statistical properties of short-term Traffic Volume data, a topic that has largely been overlooked in Traffic forecasting literature. Results indicate that the statistical characteristics of Traffic Volume can be identified from prevailing Traffic conditions; for example, Volume data exhibit frequent shifts from deterministic to stochastic structures as well as transitions between cyclic and strongly nonlinear behaviors. These findings could be valuable in the implementation of a variable prediction strategy according to the statistical characteristics of the prevailing Traffic Volume states.
