The prevalence of fatigue among truck drivers constitutes a critical concern, posing a substantial risk to road safety. The multifaceted nature of this issue necessitates a rigorous examination of the contributing factors and an evaluation of the efficacy of existing assessment tools. This research seeks to evaluate the level of fatigue among truck drivers and develop a predictive fatigue model utilizing the Psychomotor Vigilance Task (PVT) and the Karolinska Sleepiness Scale (KSS). Employing a quantitative methodology, this study utilizes statistical techniques such as the Paired Sample T-Test, Wilcoxon Signed Rank Test, Kruskal-Wallis Test, and Spearman's Rho to analyse data collected from 15 professional male truck drivers across various variables. Additionally, Logistic Regression is employed to construct a fatigue model. The findings indicate a significant difference in KSS scores and PVT performance. Furthermore, the analysis reveals a significant relationship between fatigue and braking behaviour. Logistic Regression analysis identifies three key variables as significant predictors of fatigue: the duration of work, the number of individuals in the vehicle, and the quantity of goods transported. The resultant model offers a valuable tool for assessing the risk of fatigue in truck drivers, based on those three factors. The study also highlighted the necessity to develop strategic intervention to minimize the risk of fatigue among truck drivers. Ensuring adequate rest for drivers is crucial in managing fatigue effectively.
Fatigue Modelling in Truck Drivers Transporting Liquefied Gas Cylinders Using the Psychomotor Vigilance Test (PVT) and Karolinska Sleepiness Scale (KSS)
25.09.2024
515639 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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