Defining Prosocial Behavior in Traffic: Linking Behavior to Perception

Abstract

This study investigates prosocial behavior—actions benefiting other road users—increasingly important in traffic environments with diverse mobility options and autonomous technologies. Despite growing relevance, research lacks empirically-validated definitions connecting behavioral metrics to perceived prosociality. Using a 2 × 2 within-subjects design, we simulated pedestrian interactions varying in coordination demands, time pressure, and spatial constraints to elicit prosocial responses. Analysis of behavioral metrics, linguistic patterns, and time-series features revealed three prosociality dimensions: movement adaptation, spatial coordination, and prosocial awareness. Slower speed at minimum pedestrian proximity reliably predicted higher prosociality. Rapid speed variation, pattern complexity, and frequency of acceleration-deceleration shifts emerged as top predictors of prosocial intent, yielding 75.8% classification accuracy. Our findings establish a behavioral-perceptual framework linking driving patterns to perceived prosociality in traffic environments.