Single point short-term traffic flow forecasting will play a key role in supporting demand forecasts needed by operational network models. Seasonal autoregressive integrated moving average (ARIMA), a classic parametric modeling approach to time series, and nonparametric regression models have been p
Neural-network models for classification and forecasting of freeway traffic flow stability
โ Scribed by L. Florio; L. Mussone
- Publisher
- Elsevier Science
- Year
- 1996
- Tongue
- English
- Weight
- 959 KB
- Volume
- 4
- Category
- Article
- ISSN
- 0967-0661
No coin nor oath required. For personal study only.
๐ SIMILAR VOLUMES
A combination approach based on Principal Component Analysis (PCA) and Combined Neural Network (CNN) is presented for short-term traffic flow forecasting. The historical data of the forecasted traffic volume and interrelated volumes have been processed by PCA. The results of PCA form the input data
A novel artificial intelligence based neural network (ANN) global online fault detection, pattern classification, and relaying detection scheme for synchronous generators in interconnected electric utility networks is presented. The input discriminant vector comprises the fast Fourier transform (FFT
## Abstract The main purpose of this study is to evaluate the potential of simulating the profiles of the mean velocity and turbulence intensities for the steep open channel flows over a smooth boundary using artificial neural networks. In a laboratory flume, turbulent flow conditions were measured