0476653 20240111140943.620170801235959.9 85027682538 000443416600017 10.1109/SCSP.2017.7973843 4 s. E cav_un_epca*0476652978-1-5386-3826-2PiscatawayIEEE2017 data analysis multivariate analysis traffic accident cav_un_auth*0205791 Pecherková Pavla UTIA-B Zpracování signálů Department of Signal Processing ZS ZS Department of Signal Processing CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101167 Nagy Ivan UTIA-B Zpracování signálů Department of Signal Processing ZS ZS Department of Signal Processing Ústav teorie informace a automatizace AV ČR, v. v. i. pdf http://library.utia.cas.cz/separaty/2017/ZS/pecherkova-0476653.pdf cav_un_auth*0321440 GA15-03564S GA ČR 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. cav_un_auth*0348232 2017 Smart City Symposium Prague (SCSP) 20170525 20170526 Prague CZ BB 10000 10100 10102 2018 2 PO RVO:67985556 http://hdl.handle.net/11104/0273535 1 S 7973843 RIV/67985556:_____/17:00476653!RIV18-AV0-67985556 191975665 Doplnění UT WOS RIV/67985556:_____/17:00476653!RIV18-GA0-67985556 191965016 Doplnění UT WOS n.a. Proceedings Paper Computer Science Artificial Intelligence|Computer Science Interdisciplinary Applications|Urban Studies 2017 85027682538 SCOPUS 000443416600017 WOS pdf cav_un_epca*0476652 2017 Smart City Symposium Prague (SCSP) 978-1-5386-3826-2 Piscataway IEEE 2017