Statistical Analysis of Driver Behavior Data in Different Regimes of the Car-Following Stage

Abstract

An instrumented vehicle has been used to study car-following behavior on Swedish motorways. In this study, the previous data collection and preprocessing work were briefly reviewed. To understand the driving behavior in the car-following stage more clearly, the collected time series were classified into a number of regimes using unsupervised fuzzy clustering methods. Then, the statistical relations between the driver acceleration response and the perceptual variables in each regime were analyzed using correlation and regression methods. It was found that regime classification helps discern the behavioral variance between those regime clusters. According to the data analysis, some of the car-following regimes, for example, opening and braking, can be described adequately in the statistical sense by a linear regression model (Helly's model). Therefore, a multiple regime car-following model with simple model forms, for example, linear models, has the potential to robustly represent the general car-following behavior in most regimes.