A MODEL FOR EVALUATING DRIVERS' MEAN SPEED : USING PSYCHOLOGICAL AND DRIVING SIMULATOR DATA

javanbakht, navid; mirbaha, babak · 2023 · Crossref

DOI: 10.61186/nmce.2303.1027

archive: archived pipeline: cataloged verified

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Summary

This study investigates the direct and indirect relationships between psychological variables and drivers' mean speed, addressing the critical role of human factors in traffic safety. Motivated by the fact that human activity contributes to over 85% of traffic accidents and that speed is a primary determinant of crash severity, the research aims to model how latent psychological traits influence driving behavior. The authors propose a structural model linking attitude, subjective norms, hostile behavior, and various types of driving violations (risky, self-willed, and inexperience-related) to mean driving speed. The methodology involved 71 participants (38% female, 62% male) randomly selected from Tehran, Iran. Data collection utilized two instruments: the Aggressive Driving Questionnaire and the Driving Behavior Questionnaire (DBQ) to assess psychological factors, and a driving simulator to record mean speed. The simulator featured a 2.5-kilometer two-lane highway with an 80 km/h speed limit and a simulated hazardous pedestrian scenario. The researchers employed Structural Equation Modeling (SEM) using the partial least squares method to analyze the data, testing eight specific hypotheses regarding the pathways between these psychological constructs and driving speed. The results confirmed all eight hypotheses at a 99% significance level. The analysis revealed that attitude positively influences hostile behavior, while subjective norms have an inverse relationship with risky violations, suggesting social pressure reduces risk-taking. Hostile behavior was found to directly increase both risky and self-willed violations. Furthermore, risky violations positively affect self-willed violations, which in turn positively influence inexperience violations/errors. Crucially, self-willed violations were shown to have a direct positive effect on mean driving speed. The strongest impact coefficient (0.57) was observed between self-willed violations and inexperience errors. Conversely, inexperience violations had a statistically significant but negative and weak effect on mean speed. The study concludes that psychological traits, particularly self-willed violations driven by convenience rather than risk-seeking, are significant predictors of higher driving speeds. The findings imply that drivers who violate rules for ease often lack experience, leading to errors, yet they still drive at high speeds. The authors suggest that driver training programs should incorporate lessons on the consequences of speeding and aggressive attitudes to mitigate these behaviors. Limitations include the small sample size and the impact of the COVID-19 pandemic on participant recruitment, as well as the low familiarity of participants with driving simulators, which may affect data accuracy. Future research is recommended to expand sample sizes and improve simulator accessibility to validate these findings.

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Topics

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• speed choice
• dbq psychometrics
• cultural cross national
• human error taxonomy

Information type

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• Methodological Resource: validation psychometrics
• Theoretical Contribution: computational model, theory or model