Fitness for duty prediction model for bus driver of batik solo trans based on physical, mental, and work aspects. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Suhardi, Bambang; Rosyidasari, Anisa; Astuti, Rahmaniyah Dwi; Adiasa, Iksan
• Is Part Of: Cogent engineering. Volume 9:Issue 1(2022)
• Publication Date: 2022-12-31
• Subjects: attention -- bus driver -- fatigue -- fitness for duty -- sleepiness; Engineering -- Periodicals; Technology -- Periodicals; Engineering; Technology; Periodicals; 620
• DOI: 10.1080/23311916.2022.2143068
• Languages: English
• ISSNs: 2331-1916

Abstract

Abstract: The development of fitness for duty models in bus drivers based on physical, mental, and work aspects is significant. The study aims to develop fitness for duty model for Batik Solo Trans (BST) drivers using a Psychomotor Vigilance Task (PVT), Visual Analogue Scale (VAS), Karolinska Sleepiness Scale (KSS), and logistic regression. Thirty bus drivers BST corridor 1 with the route Terminal Palur—Adi Sumarmo PP Airport supported the study with age, weight, height, sleep length, sleep quality, cigarette consumption, caffeine consumption, shift, attention, fatigue, sleepiness as independent variables and FFD as the dependent variable. The results showed that the assessment and evaluation of the fitness for duty level are pretty good. Based on the models that have been made, 90, 9% of drivers are ready for duty. As much as 86, 7% of bus drivers are right predicted. The value of adjusted R2 in the model is 68%. The ability of the model to predict its observational value is high (76%). VAS and KSS can be used to fit for duty of bus drivers, while PVT cannot be utilized. Fatigue and sleepiness affect the fitness for duty of bus drivers, while attention does not. Fatigue affects the level of sleepiness. The more tired, the higher the level of sleepiness felt. A shift has no significant effect on the level of attention, fatigue, and sleepiness. Shifts can affect the duration and quality of a driver's sleep. The results of the logistic regression showed that the order of variables that had the most influence on the driver's job readiness were age, cigarette consumption, weight, sleep quality, changes in KSS, changes in PVT, changes in VAS, caffeine consumption, shifts, height, and sleep duration.