Synthetic data generation using Copula model and driving behavior analysis

Autor:	Efe Savran, Fatih Karpat
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	Driving behavior classification Synthetic data generation Copula K-means Data privacy Real-world data Engineering (General). Civil engineering (General) TA1-2040
Zdroj:	Ain Shams Engineering Journal, Vol 15, Iss 12, Pp 103060- (2024)
Druh dokumentu:	article
ISSN:	2090-4479 89151143
DOI:	10.1016/j.asej.2024.103060
Popis:	In this study, the generation of synthetic driving data that can reflect real behavior well using the Copula model was investigated. To see the difference in behavior patterns in the generated synthetic driving data, a feature correlation comparison was made. The difference in driving behavior was provided with the K-means based classification model. It was shown that with a Random Forest model trained with synthetic data and having high accuracy, the privacy of real data could be protected by 98.55%. At the end of the study, it was seen that the Copula model could obtain synthetic driving data with sufficient accuracy with CAN bus data without additional sensor support.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/f8d6ad8915114380a9fd941582bb397f Zobrazit plný text záznamu View record in DOAJ