Assessing driving risk through unsupervised detection of anomalies in telematics time series data
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| Tag | 1 | 2 | Valor |
|---|---|---|---|
| LDR | 00000cab a2200000 4500 | ||
| 001 | MAP20260001661 | ||
| 003 | MAP | ||
| 005 | 20260202102310.0 | ||
| 008 | 260130e20250512bel|||p |0|||b|eng d | ||
| 040 | $aMAP$bspa$dMAP | ||
| 084 | $a6 | ||
| 100 | $0MAPA20260001180$a Weng Chan, Ian | ||
| 245 | 1 | 0 | $aAssessing driving risk through unsupervised detection of anomalies in telematics time series data$cIan Weng Chan, Andrei L. Badescu and X. Sheldon Lin |
| 520 | $aVehicle telematics provides granular data for dynamic driving risk assessment, but current methods often rely on aggregated metrics and do not fully exploit the rich time-series structure of telematics data. In this paper, we introduce a flexible framework using continuous-time hidden Markov model to model and analyse trip-level telematics data | ||
| 650 | 4 | $0MAPA20080602437$aMatemática del seguro | |
| 650 | 4 | $0MAPA20080603779$aSeguro de automóviles | |
| 650 | 4 | $0MAPA20080556730$aTelemática | |
| 650 | 4 | $0MAPA20080601522$aEvaluación de riesgos | |
| 650 | 4 | $0MAPA20080576783$aModelo de Markov | |
| 650 | 4 | $0MAPA20080578848$aAnálisis de datos | |
| 700 | 1 | $0MAPA20210030147$aBadescu, Andrei L. | |
| 700 | 1 | $0MAPA20170014539$aSheldon Lin, X. | |
| 710 | 2 | $0MAPA20100017661$aInternational Actuarial Association | |
| 773 | 0 | $wMAP20077000420$g12/05/2025 Volume 55 Issue 2 - may 2025 , p. 205 - 241$x0515-0361$tAstin bulletin$dBelgium : ASTIN and AFIR Sections of the International Actuarial Association |