1995
DOI: 10.1061/(asce)0733-947x(1995)121:3(249)
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Short-Term Prediction of Traffic Volume in Urban Arterials

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Cited by 462 publications
(166 citation statements)
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“…Many approaches based on nonparametric models to tackle this problem have been proposed, such as multilayer perceptron with a learning rule based on a Kalman filter [49], wavelet-based neural network [18], fuzzy-neural model [52], ARIMA models [23], graphical-lasso neural network [20], multi-task neural network [19], multi-task ensemble neural network [45], k-nearest neighbour nonparametric regression [53].…”
Section: Previous Work In the Fields Of Data Streammentioning
confidence: 99%
“…Many approaches based on nonparametric models to tackle this problem have been proposed, such as multilayer perceptron with a learning rule based on a Kalman filter [49], wavelet-based neural network [18], fuzzy-neural model [52], ARIMA models [23], graphical-lasso neural network [20], multi-task neural network [19], multi-task ensemble neural network [45], k-nearest neighbour nonparametric regression [53].…”
Section: Previous Work In the Fields Of Data Streammentioning
confidence: 99%
“…Thereinto, the autoregressive integrated moving average (ARIMA) (Ahmed & Cook, 1979) family of models such as simple ARIMA (Levin & Tsao, 1980;Nihan & Holmesland, 1980;Hamed et al, 1995;Smith, 1995;Williams, 1999), ATHENA , subset ARIMA (Lee & Fambro, 1999), SARIMA family (Smith et al, 2002;Williams et al, 1998Williams et al, , 2003Ghosh et al, 2005), are classical milestones in forecasting area. Such time series methods belong to time domain approaches, and frequency domain approaches like spectral analysis, "which are regressions on periodic sines and cosines, show their important insights into traffic data which may not apparent in an analysis in the time domain only" (Stathopoulos & Karlaftis, 2001a, b).…”
Section: Parametric Traffic Forecasting Approachesmentioning
confidence: 99%
“…Davis et al (1991) applied a single ARIMA model to forecast the bottleneck formation on a freeway. Later, Hamed et al (1995) applied an ARIMA model to forecast urban traffic volumes.…”
Section: Travel Time Prediction Techniques and Travel Time Estimationmentioning
confidence: 99%