Improved Driver Clustering Framework by Considering the Variability of Driving Behaviors across Traffic Operation Conditions

Abstract

Analysis of driving behaviors and related driver clustering is of great significance for improving driving safety, but traffic operation conditions (especially road types and operating speed) often are neglected in existing clustering studies, and the impact of excluding traffic conditions has not been investigated thoroughly. This research proposes an improved driver clustering framework by accounting for road types and average speed. The clustering results were compared with those without considering traffic conditions. The input data of more than 34 million records of second-by-second vehicle trajectories from 315 vehicles in Beijing were sliced into segments of 30 s, and these seconds were classified by road types (expressway versus non-expressway) and by 10-km/h average speed intervals. For each driver, the speed variation coefficients (SVCs), acceleration standard deviations (ASTDs), and average negative accelerations (ANAs) by traffic condition were entered into a Gaussian mixture model for an unsupervised clustering of drivers into types of prudent, normal, and aggressive drivers. The improved clustering framework is capable of capturing the variability of driving behaviors (especially dangerous driving behaviors such as sharp decelerations) across drivers, and the comparison demonstrated significant differences between the improved model and the original model with respect to the proportion of every driver type. The improved clustering framework performs better in both intraclass aggregation and interclass separation, and the results of this research indicate the need to consider traffic conditions in driving behavior–based clustering of drivers.