0481220 20240111140949.920171111235959.9 85033590254 000425566000002 10.1016/j.trc.2017.11.001 14 s. cav_un_epca*02552600968-090X861 (2018)23-36Elsevier driving style fuel consumption mixture-based clustering data-dependent pointer recursive mixture estimation cav_un_auth*0108105 Suzdaleva Evgenia Zpracování signálů Department of Signal Processing ZS ZS UTIA-B Department of Signal Processing RU Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101167 Nagy Ivan Zpracování signálů Department of Signal Processing ZS ZS UTIA-B Department of Signal Processing Ústav teorie informace a automatizace AV ČR, v. v. i. pdf http://library.utia.cas.cz/separaty/2017/ZS/suzdaleva-0481220.pdf cav_un_auth*0321440 GA15-03564S GA ČR The paper focuses on a task of stochastic modeling the driving style and its online estimation while driving. The driving style is modeled by means of a mixture model with normal and categorical components as well as a data-dependent pointer. The main contributions of the presented approach are: (i) the online estimation of the driving style while driving, taking into account data up to the current time instant, (ii) the joint model for continuous and discrete data measured on a vehicle, (iii) the data-dependent model of the driving style conditioned by the values of fuel consumption, (iv) the use of the model both for detection of clusters according to the driving style and prediction of the fuel consumption along with other variables, and (v) the universal modeling with the help of mixtures, which allows us to use different combinations of components and pointer models as well as to specify the initialization approach suitable for the considered problem. BB 10000 10100 10103 2019 2 4 A hod 4ah 20231122142807.9 RVO:67985556 http://hdl.handle.net/11104/0277004 1 Department of Signal Processing UTIA-B 10103 STATISTICS & PROBABILITY S 1 Article Transportation Science Technology TRANSPORTATIONSCIENCE&TECHNOLOGY 6.067 1.354 4.4 0.01703 1.13 10655 2.611 4.164 311 Q1 81.395 93.2 Q1 Q1* 2.06 Q1 93.243 2018 Soubory v repozitáři: suzdaleva-0481220.pdf 85033590254 SCOPUS PUBMED 000425566000002 WOS pdf cav_un_epca*0255260 Transportation Research. Part C: Emerging Technologies 0968-090X 1879-2359 Roč. 86 č. 1 2018 23 36 Elsevier