| bibtype |
J -
Journal Article
|
| ARLID |
0575836 |
| utime |
20240903170559.5 |
| mtime |
20230926235959.9 |
| SCOPUS |
85175788005 |
| WOS |
001075119400005 |
| DOI |
10.14311/NNW.2023.33.017 |
| title
(primary) (eng) |
Using Poisson proximity-based weights for traffic flow state prediction |
| specification |
| page_count |
25 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0290321 |
| ISSN |
1210-0552 |
| title
|
Neural Network World |
| volume_id |
33 |
| volume |
4 (2023) |
| page_num |
291-315 |
| publisher |
| name |
Ústav informatiky AV ČR, v. v. i. |
|
|
| keyword |
traffic counts |
| keyword |
traffic flow state |
| keyword |
cluster prediction |
| keyword |
Poisson mixture |
| keyword |
recursive mixture estimation |
| author
(primary) |
| ARLID |
cav_un_auth*0383037 |
| name1 |
Uglickich |
| name2 |
Evženie |
| institution |
UTIA-B |
| full_dept (cz) |
Zpracování signálů |
| full_dept (eng) |
Department of Signal Processing |
| department (cz) |
ZS |
| department (eng) |
ZS |
| full_dept |
Department of Signal Processing |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101167 |
| name1 |
Nagy |
| name2 |
Ivan |
| institution |
UTIA-B |
| full_dept (cz) |
Zpracování signálů |
| full_dept |
Department of Signal Processing |
| department (cz) |
ZS |
| department |
ZS |
| full_dept |
Department of Signal Processing |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| source |
|
| cas_special |
| project |
| project_id |
8A21009 |
| agency |
GA MŠk |
| ARLID |
cav_un_auth*0432581 |
|
| abstract
(eng) |
The development of traffic state prediction algorithms embedded in intelligent transportation systems is of great importance for improving traffic conditions for drivers and pedestrians. Despite the large number of prediction methods, existing limitations still confirm the need to find a systematic solution and its adaptation to specific traffic data. This paper focuses on the relationship between traffic flow states in different urban locations, where these states are identified as clusters of traffic counts. Extending the recursive Bayesian mixture estimation theory to the Poisson mixtures, the paper uses the mixture pointers to construct the traffic state prediction model. Using the predictive model, the cluster at the target urban location is predicted based on the traffic counts measured in real time at the explanatory urban location. The main contributions of this study are: (i) recursive identification and prediction of the traffic state at each time instant, (ii) straightforward Poisson mixture initialization, and (iii) systematic theoretical background of the prediction approach. Results of testing the prediction algorithm on real traffic counts are presented. |
| result_subspec |
WOS |
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2024 |
| num_of_auth |
2 |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0345847 |
| confidential |
S |
| mrcbC91 |
A |
| mrcbT16-e |
COMPUTERSCIENCEARTIFICIALINTELLIGENCE |
| mrcbT16-j |
0.164 |
| mrcbT16-D |
Q4 |
| arlyear |
2023 |
| mrcbU14 |
85175788005 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
001075119400005 WOS |
| mrcbU56 |
pdf |
| mrcbU63 |
cav_un_epca*0290321 Neural Network World 1210-0552 Roč. 33 č. 4 2023 291 315 Ústav informatiky AV ČR, v. v. i. |
|