0356536 20240103194847.320110215235959.9 11 s. WWW cav_un_epca*03565351-11PrahaČVUT v Praze2011 Bayesian modelling traffic modelling cav_un_auth*0242543 Dedecius Kamil Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101167 Nagy Ivan Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Signal Processing Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0228606 Hofman Radek Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2011/AS/dedecius-modelling of traffic flow with bayesian autoregressive model with variable partial forgetting.pdf SGS 10/099/OHK3/1T/16 ČVUT v Praze CZ CEZ:AV0Z10750506 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. cav_un_auth*0269852 CTU Workshop 2011 Praha 01.02.2011-01.02.2011 CZ 2011 BB http://hdl.handle.net/11104/0195032 2011 cav_un_epca*0356535 CTU Workshop 2011 1 11 Praha ČVUT v Praze 2011