Introducing a multivariate model for predicting driving performance: The role of driving anger and personal characteristics

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet

Authors

Introduction Maladaptive driving is an important source of self-inflicted accidents and this driving style could include high speeds, speeding violations, and poor lateral control of the vehicle. The literature suggests that certain groups of drivers, such as novice drivers, males, highly motivated drivers, and those who frequently experience anger in traffic, tend to exhibit more maladaptive driving patterns compared to other drivers. Remarkably, no coherent framework is currently available to describe the relationships and distinct influences of these factors. Method We conducted two studies with the aim of creating a multivariate model that combines the aforementioned factors, describes their relationships, and predicts driving performance more precisely. The studies employed different techniques to elicit emotion and different tracks designed to explore the driving behaviors of participants in potentially anger-provoking situations. Study 1 induced emotions with short film clips. Study 2 confronted the participants with potentially anger-inducing traffic situations during the simulated drive. Results In both studies, participants who experienced high levels of anger drove faster and exhibited greater longitudinal and lateral acceleration. Furthermore, multiple linear regressions and path-models revealed that highly motivated male drivers displayed the same behavior independent of their emotional state. The results indicate that anger and specific risk characteristics lead to maladaptive changes in important driving parameters and that drivers with these specific risk factors are prone to experience more anger while driving, which further worsens their driving performance. Driver trainings and anger management courses will profit from these findings because they help to improve the validity of assessments of anger related driving behavior.

OriginalspracheEnglisch
ZeitschriftJournal of Safety Research
Jahrgang47
Seiten (von - bis)47-56
Anzahl der Seiten10
ISSN0022-4375
DOIs
PublikationsstatusErschienen - 2013

Zugehörige Projekte

  • Forschungsschwerpunkt Psychonik

    Projekt: Forschung

  • Entwicklung eines Trainings zur Gefahrenwahrnehmung im Straßenverkehr

    Projekt: Forschung

  • Programmentwicklung für den Aufbau eines Fahrsimulationszentrums

    Projekt: Transfer (FuE-Projekt)

DOI

Zuletzt angesehen

Publikationen

  1. A sensor fault detection scheme as a functional safety feature for DC-DC converters
  2. Sensor Fusion for Power Line Sensitive Monitoring and Load State Estimation
  3. Who can receive the pass? A computational model for quantifying availability in soccer
  4. A statistical study of the spatial evolution of shock acceleration efficiency for 5 MeV protons and subsequent particle propagation
  5. For a return to the forgotten formula: 'Data 1 + Data 2 > Data 1'
  6. Hybrid modelling by machine learning corrections of analytical model predictions towards high-fidelity simulation solutions
  7. Interplays between relational and instrumental values
  8. Does thinking-aloud affect learning, visual information processing and cognitive load when learning with seductive details as expected from self-regulation perspective?
  9. A Geometric Approach by Using Switching and Flatness Based Control in Electromechanical Actuators for Linear Motion
  10. Introduction
  11. What motivates people to use energy feedback systems? A multiple goal approach to predict long-term usage behaviour in daily life
  12. Rotational complexity in mental rotation tests
  13. Special Issue The Discourse of Redundancy Introduction
  14. Contextualizing certification and auditing
  15. HyperKult
  16. Comparison of EKF and TSO for Health Monitoring of a Textile-Based Heater Structure and its Control
  17. Adjustable automation and manoeuvre control in automated driving
  18. E-privacy concerns
  19. Introduction
  20. Evaluation of mechanical property predictions of refill Friction Stir Spot Welding joints via machine learning regression analyses on DoE data
  21. "And I Think That Is a Very Straightforward Way of Dealing With It''
  22. Introduction to Philosophy of Management