This descriptive–analytic study identified the factors affecting the degree of fatigue and sleepiness of heavy-vehicle drivers, assessed their effects, and ranked them according to extent of influence by using statistical analysis and the technique for order of preference by similarity to ideal solution (TOPSIS). Data were collected through interviews guided by a questionnaire, through which three main categories of factors that contribute to crashes caused by fatigue and sleepiness were discussed. These categories are (I) human, (II) road and environmental conditions, and (III) vehicle-related factors. The results showed that human and road and environmental conditions exert the strongest and weakest effects, respectively. The statistical and TOPSIS results revealed that the first four factors that exert the strongest effects are inappropriate behaviors of passengers and goods owners, non-standard roads, inappropriate behaviors of police, and economic problems of heavy-vehicle drivers.
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Reliability is defined as the extent to which a questionnaire produces the same results on repeated trials. That is, it refers to the stability or consistency of scores over time or across raters. Questionnaire reliability is most commonly measured using Cronbach’s alpha, whose theoretical value varies from 0 to 1. Higher alpha values (≥ 0.7) are more desirable.
Validity is defined as the extent to which a questionnaire measures what it purports to measure. Factor analysis is a statistical procedure used to identify a small number of factors that can be used to represent relationships among a set of interrelated variables.
The KMO measure determines the sampling adequacy that is used to compare the magnitudes of observed correlation coefficients in relation to the magnitudes of partial correlation coefficients. Sampling adequacy can be interpreted as follows: 0.90 = marvelous, 0.80 = meritorious, 0.70 = middling, 0.60 = mediocre, 0.50 = miserable.
Bartlett’s test of sphericity tests the hypothesis that the correlation matrix is an identity matrix; that is, all diagonal elements are 1, and all off-diagonal elements are 0. If the significance value of this test is less than our alpha level (< 0.05), then the null hypothesis (i.e., The population matrix is not an identity matrix.) can be rejected.
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Ghasemi Noughabi, M., Sadeghi, A., Mohammadzadeh Moghaddam, A. et al. Fatigue Risk Management: Assessing and Ranking the Factors Affecting the Degree of Fatigue and Sleepiness of Heavy-Vehicle Drivers Using TOPSIS and Statistical Analyses. Iran J Sci Technol Trans Civ Eng 44, 1345–1357 (2020). https://doi.org/10.1007/s40996-019-00320-9
- Heavy vehicle
- Fatigue management
- Statistical analysis
- TOPSIS analysis