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A Mixed-Effect Ordered Logit Model for Investigating Drivers’ Perceived Frustration Levels in Complex Traffic Environments

    Description: This research investigates how different driving behavior factors and sociodemographic characteristics influence a driver's perceived frustration across four complex traffic scenarios, such as being stuck in a traffic jam with a honking vehicle behind or being trapped between two large trucks. The study treats frustration as a reflection of mental instability caused by situational stressors like congestion, blocked travel paths, and the sporadic movement of other vehicles

  • The study identifies unobserved heterogeneity (random human factors) as critical in understanding frustration, revealing that drivers over 64 years old are the most resilient, while female and occasional drivers are significantly more prone to high frustration .
  • Researchers utilized a 428-response questionnaire survey, employed Partial Least Squares Structural Equation Modeling (PLS–SEM) to extract seven latent factors, and applied a mixed-effect ordered logit model (MOLM) with marginal effect analysis .
  • The findings support the development of targeted frustration management modules in driver training and the creation of adaptive Advanced Driver Assistance Systems (ADAS) that can provide real-time feedback or automated interventions based on identified "risky" or "impatient" driver profiles .
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