Heterogeneous treatment effects of emotional arousal on aggressive driving among ride-hailing drivers under distraction and passenger presence.

This study investigates the heterogeneous treatment effects of emotional arousal on aggressive driving behaviors among ride-hailing drivers using naturalistic driving data. Three types of aggressive driving-abnormal acceleration, moderate speeding, and severe speeding-were identified through vehicle kinematics and video recordings. Emotional arousal was classified as positive or negative based on emotional valence. A double machine learning framework (DML) with generalized random forests (GRF) was employed to estimate the average treatment effect (ATE) and conditional average treatment effect (CATE). Results show that both high positive and high negative arousal increase the likelihood of aggressive driving, with negative arousal exerting a stronger effect across all three aggressive behaviors. Abnormal acceleration emerged as the primary means for emotional expression. Drivers who were distracted, without passenger were more susceptible to emotional influence-especially under positive arousal. This study underscores the heterogeneous treatment effects of emotional arousal on aggressive driving. The findings emphasizes the need for more refined and differentiated emotional management and intervention strategies by ride-hailing platforms.