What is the appropriate temporal distance range for driving style analysis?

Abstract

Building human-centered intelligent transport systems (ITSs) requires thorough understanding of the diversified driving styles among drivers. In data-driven driving behavior studies, the temporal distance is deemed as an important variable. However, with respect to the driving style analysis, the appropriate temporal distance range has not been clear yet, and little attention has been drawn to the larger temporal distance that may also have a potential effect on driving style. This paper proposes a new three-layer structure of driving style by using the modified latent Dirichlet allocation (mLDA) model. It is found that the results revealed by the mLDA model based on real driving behavior data are able to align themselves with the results from a driving style questionnaire, and some self-reporting bias is uncovered. More comprehensive driving styles are discovered quantitatively, and the appropriate temporal distance range for driving style analysis is determined. The analyzed results indicate that the time-gap range larger than 10 s are still pivotal and the time-gap range below 20 s is a suitable range for driving style analysis

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