Original title: Modelling of Traffic Flow with Bayesian Autoregressive Model with Variable Partial Forgetting
Authors: Dedecius, Kamil ; Nagy, Ivan ; Hofman, Radek
Document type: Papers
Conference/Event: CTU Workshop 2011, Praha (CZ), 2011-02-01 / 2011-02-01
Year: 2011
Language: eng
Abstract: Computing the future road traffic intensities in urban and suburban areas is considered inthis paper. The statistical properties of the traffic flow advocate the use of a low-order lin- ear autoregressive models, in which the previous intensities determine the following ones. To achieve adaptivity, the Bayesian modelling framework was chosen. The regression coefficients are considered random, hence they are modelled using a suitable distribution. A significant improvement of the overall modelling performance is further reached with techniques allowing the parameters vary by modification of their distribution. We present the partial forgetting method, allowing to individually track the parameters even in the case of their different variability rate.
Keywords: Bayesian modelling; traffic modelling
Project no.: CEZ:AV0Z10750506 (CEP), SGS 10/099/OHK3/1T/16
Funding provider: ČVUT v Praze
Host item entry: CTU Workshop 2011

Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2011/AS/dedecius-modelling of traffic flow with bayesian autoregressive model with variable partial forgetting.pdf
Original record: http://hdl.handle.net/11104/0195032

Permalink: http://www.nusl.cz/ntk/nusl-42547


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Research > Institutes ASCR > Institute of Information Theory and Automation
Conference materials > Papers