A Performance evaluation of neural network models in traffic volume forecasting

Nonlinear time series analysis of ground-level ozone dynamics in Southern Taiwan

“…It may lead to reduce the possible estimation bias due to linear dependence. (Peters, 1994;Yun et al, 1998). Katsev and L'Heureux (2003) suggested that the Hurst exponents measured from short datasets (less than 500 points) would be usually too large for most practical purposes.…”

PREDICTING THE EFFECTIVENESS OF WEB INFORMATION SYSTEMS USING NEURAL NETWORKS MODELING: FRAMEWORK & EMPIRICAL TESTING

“…Many studies indicated that ANN modeling is superior with respect to the traditional linear model (Yun et al, 1998). Furthermore, using ANN in system modeling and forecasting has many advantages (Yun et al, 1998;Zhang et al, 1998): (1) ANN has the ability to learn from examples, (2) it can predict and forecast even in case of noisy dataset, and (3) ANN can approximate multivariate functions with high accuracy, (4) ANN is a selfadaptive method and so it includes few pre assumptions, and (5) ANN can be utilized whenever there is a limited dataset or the relationship between input features and targets are vague. There are several types of ANNs such as Generalized Regression Neural Network (GRNN), Back Propagation (BP), and Radial Bias Function (RBF).…”

How to Provide Accurate and Robust Traffic Forecasts Practically?

“…Over the past decade, another nonparametric technique, artificial neural networks (ANNs) have been applied in traffic forecasting because of their strong ability to capture the indeterministic and complex nonlinearity of time series (Smith & Demetsky, 1994Chang & Su, 1995;Dougherty & Cobbet, 1997;Lam & Xu, 2000;Park et al, 1999;Dharia & Adeli, 2003;Wei et al, 2009;Wei & Lee 2007;Lee, 2009). Motivated by the universal approximation property, neural network models ranging from purely static to highly dynamic structures include the multilayer perceptrons (MLPs) (Clark et al, 1993;Vythoulkas, 1993;Lee & Fambro, 1999;Gilmore & Abe, 1995;Ledoux, 1997;Innamaa, 2000;Florio & Mussone, 1996;Yun et al, 1998;Zhang, 2000;Chen et al, 2001), the radial basis function (RBF) ANNs (Lyons et al, 1996;Park & Rilett, 1998;Chen et al, 2001), the time-delayed ANNs (Lingras et al, 2000;Lingras & Mountford, 2001;Yun et al, 1998;Yasdi 1999;Abdulhai et al, 1999;Dia, 2001;Ishak & Alecsandru, 2003), the recurrent ANNs (Dia, 2001;Van Lint et al, 2002, and the hybrid ANNs (Abdulhai et al, 1999;Chen et al, 2001;Lingras & Mountford, 2001;Park, 2002;Yin et al, 2002;Vlahogianni ...…”