Clustering of Driver Behavioral Strategies During Speed Cushion Traversal: A Driving Simulator Study

Traffic calming measures are widely used to reduce operating speeds and mitigate crash risk in urban corridors; however, the way drivers adapt their control strategy when traversing Berlin speed cushions is still poorly described from a multivariate behavioral perspective. This study proposes a behavior-oriented analysis to identify recurring speed-cushion traversal strategies using driving simulator telemetry. A fixed-base simulator reproduced a real urban corridor, and trajectories were segmented in device-centered spatial windows capturing approach, traversal, and immediate recovery. Each segment was summarized by three indicators describing longitudinal and lateral control: mean speed, peak braking demand, and average lane position deviation. Features were standardized and clustered using k-means. The number of clusters was selected primarily through mean silhouette evaluation, while resampling-based checks and a Gaussian mixture modeling comparison were used as supportive evidence rather than competing decision rules. Three traversal profiles emerged: smooth cautious, reactive cautious, and unmoderated fast. The introduction of speed cushions shifted the distribution of segments towards cautious profiles, while driver-level concentration within a single profile was moderate. Overall, results indicate that speed cushions influence the whole vehicle control strategy, offering a quantitative basis for behavior-oriented evaluation of local traffic calming interventions in smart-city contexts.