| bibtype |
K -
Conference Paper (Czech conference)
|
| ARLID |
0356536 |
| utime |
20240103194847.3 |
| mtime |
20110215235959.9 |
| title
(primary) (eng) |
Modelling of Traffic Flow with Bayesian Autoregressive Model with Variable Partial Forgetting |
| specification |
| page_count |
11 s. |
| media_type |
WWW |
|
| serial |
| ARLID |
cav_un_epca*0356535 |
| title
|
CTU Workshop 2011 |
| page_num |
1-11 |
| publisher |
| place |
Praha |
| name |
ČVUT v Praze |
| year |
2011 |
|
|
| keyword |
Bayesian modelling |
| keyword |
traffic modelling |
| author
(primary) |
| ARLID |
cav_un_auth*0242543 |
| name1 |
Dedecius |
| name2 |
Kamil |
| full_dept (cz) |
Adaptivní systémy |
| full_dept (eng) |
Department of Adaptive Systems |
| department (cz) |
AS |
| department (eng) |
AS |
| institution |
UTIA-B |
| full_dept |
Department of Adaptive Systems |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101167 |
| name1 |
Nagy |
| name2 |
Ivan |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| institution |
UTIA-B |
| full_dept |
Department of Signal Processing |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0228606 |
| name1 |
Hofman |
| name2 |
Radek |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| institution |
UTIA-B |
| full_dept |
Department of Adaptive Systems |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
SGS 10/099/OHK3/1T/16 |
| agency |
ČVUT v Praze |
| country |
CZ |
|
| research |
CEZ:AV0Z10750506 |
| abstract
(eng) |
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. |
| action |
| ARLID |
cav_un_auth*0269852 |
| name |
CTU Workshop 2011 |
| place |
Praha |
| dates |
01.02.2011-01.02.2011 |
| country |
CZ |
|
| reportyear |
2011 |
| RIV |
BB |
| permalink |
http://hdl.handle.net/11104/0195032 |
| arlyear |
2011 |
| mrcbU63 |
cav_un_epca*0356535 CTU Workshop 2011 1 11 Praha ČVUT v Praze 2011 |
|