| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0476653 |
| utime |
20240111140943.6 |
| mtime |
20170801235959.9 |
| SCOPUS |
85027682538 |
| WOS |
000443416600017 |
| DOI |
10.1109/SCSP.2017.7973843 |
| title
(primary) (eng) |
Analysis of discrete data from traffic accidents |
| specification |
| page_count |
4 s. |
| media_type |
E |
|
| serial |
| ARLID |
cav_un_epca*0476652 |
| ISBN |
978-1-5386-3826-2 |
| title
|
2017 Smart City Symposium Prague (SCSP) |
| publisher |
| place |
Piscataway |
| name |
IEEE |
| year |
2017 |
|
|
| keyword |
data analysis |
| keyword |
multivariate analysis |
| keyword |
traffic accident |
| author
(primary) |
| ARLID |
cav_un_auth*0205791 |
| name1 |
Pecherková |
| name2 |
Pavla |
| 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 |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0321440 |
| project_id |
GA15-03564S |
| agency |
GA ČR |
|
| abstract
(eng) |
This paper deals with the data analysis of traffic accidents. Traffic accidents can be caused by different reasons, e.g., by watchfulness of a driver, failure of a vehicle, bad structural arrangements, etc. The aim of this paper is to investigate seriousness of incidents in dependence on different circumstances of an accident. Description of these circumstances leads to the use of a high number of different variables (about 50 variables), which are mostly discrete. The majority of statistical methods dealing with discrete variables use a frequency table. This is not suitable for traffic data because of a huge dimension. In this paper, several methods are proposed for solution to the problem with high-dimensional traffic data. |
| action |
| ARLID |
cav_un_auth*0348232 |
| name |
2017 Smart City Symposium Prague (SCSP) |
| dates |
20170525 |
| mrcbC20-s |
20170526 |
| place |
Prague |
| country |
CZ |
|
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10102 |
| reportyear |
2018 |
| num_of_auth |
2 |
| presentation_type |
PO |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0273535 |
| mrcbC61 |
1 |
| confidential |
S |
| article_num |
7973843 |
| mrcbC83 |
RIV/67985556:_____/17:00476653!RIV18-AV0-67985556 191975665 Doplnění UT WOS |
| mrcbC83 |
RIV/67985556:_____/17:00476653!RIV18-GA0-67985556 191965016 Doplnění UT WOS |
| mrcbC86 |
n.a. Proceedings Paper Computer Science Artificial Intelligence|Computer Science Interdisciplinary Applications|Urban Studies |
| arlyear |
2017 |
| mrcbU14 |
85027682538 SCOPUS |
| mrcbU34 |
000443416600017 WOS |
| mrcbU56 |
pdf |
| mrcbU63 |
cav_un_epca*0476652 2017 Smart City Symposium Prague (SCSP) 978-1-5386-3826-2 Piscataway IEEE 2017 |
|