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 Catch22 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.