Open AccessLeveraging longitudinal driving behaviour data with data mining techniques for driving style analysis
Author(s) -
Qi Geqi,
Du Yiman,
Wu Jianping,
Xu Ming
Publication year - 2015
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2014.0139
Subject(s) - cruise control , cluster analysis , latent dirichlet allocation , computer science , data mining , set (abstract data type) , machine learning , artificial intelligence , control (management) , topic model , programming language
Accurately understanding driving behaviour is of crucial importance for advanced driving assistant systems such as adaptive cruise control system and intelligent forward collision warning system. To understand different driving styles, this study employs the clustering method and topic model to extract latent driving states, which can elaborate and analyse the commonness and individuality of driving behaviour characteristics with the longitudinal driving behaviour data collected by the instrumented vehicle. To handle the large set of data and discover the valuable knowledge, the data mining techniques including ensemble clustering method based on the kernel fuzzy C‐means algorithm and the modified latent Dirichlet allocation model are employed in this study. The ‘aggressive’, ‘cautious’ and ‘moderate’ driving states are discovered and the underlying quantified structure is built for the driving style analysis.