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Assessing driving risk through unsupervised detection of anomalies in telematics time series data
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Title: Assessing driving risk through unsupervised detection of anomalies in telematics time series data / Ian Weng Chan, Andrei L. Badescu and X. Sheldon LinAuthor: Weng Chan, Ian ‌
Notes: Sumario: Vehicle 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 dataRelated records: En: Astin bulletin. - Belgium : ASTIN and AFIR Sections of the International Actuarial Association = ISSN 0515-0361. - 12/05/2025 Volume 55 Issue 2 - may 2025 , p. 205 - 241Materia / lugar / evento: Matemática del seguro ‌Seguro de automóviles ‌Telemática ‌Evaluación de riesgos ‌Modelo de Markov ‌Análisis de datos ‌Otros autores: Badescu, Andrei L. ‌
Sheldon Lin, X. ‌
International Actuarial Association ‌
Other categories: 6
Rights: In Copyright (InC)
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