Fatigue Risk Management: Assessing and Ranking the Factors Affecting the Degree of Fatigue and Sleepiness of Heavy-Vehicle Drivers Using TOPSIS and Statistical Analyses

Abstract

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.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Notes

  1. 1.

    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.

  2. 2.

    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.

  3. 3.

    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.

  4. 4.

    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.

References

  1. Balkin TJ, Horrey WJ, Graeber RC, Czeisler CA, Dinges DF (2011) The challenges and opportunities of technological approaches to fatigue management. Accid Anal Prev 43:565–572

    Article  Google Scholar 

  2. Chen GX, Fang Y, Guo F, Hanowski RJ (2016) The influence of daily sleep patterns of commercial truck drivers on driving performance. Accid Anal Prev 91:55–63

    Article  Google Scholar 

  3. Connor J, Norton R, Ameratunga S, Robinson E, Civil I, Dunn R, Jackson R (2002) Driver sleepiness and risk of serious injury to car occupants: population based case control study. BMJ 324:1125

    Article  Google Scholar 

  4. Davidvic J, Pesic D, Antic B (2018) Professional drivers’ fatigue as a problem of the modern era. Transp Res Part F Traffic Psychol Behav 55:199–209

    Article  Google Scholar 

  5. Dawson D, Noy YI, Harma M, Kerstedt T, Belenky G (2011) Modelling fatigue and the use of fatigue models in work settings. Accid Anal Prev 43:549–564

    Article  Google Scholar 

  6. Dobbie K (2002) Fatigue related crashes: an analysis of fatigue related crashes on Australian roads using an operational definition of fatigue. Report OR 23, Australian Transport Safety Bureau, 1–30

  7. Fernandes R, Hatfield J, Soames Job RF (2010) A systematic investigation of the differential predictors for speeding, drink-driving, driving while fatigued, and not wearing a seat belt, among young drivers. Transp Res Part F Traffic Psychol Behav 13(3):179–196

    Article  Google Scholar 

  8. Friswell R, Williamson A (2013) Comparison of the fatigue experiences of short haul light and long distance heavy vehicle drivers. Saf Sci 57:203–213

    Article  Google Scholar 

  9. Fu R, Wang H, Zhao W (2016) Dynamic driver fatigue detection using hidden Markov model in real driving condition. Expert Syst Appl 63:397–411

    Article  Google Scholar 

  10. Gander P, Hartley L, Powell D, Cabon P, Hitchcock E, Mills A, Popkin SM (2011) Fatigue risk management: organizational factors at the regulatory and industry/company level. Accid Anal Prev 43:573–590

    Article  Google Scholar 

  11. Gardziejczyk W, Zabicki P (2014) The influence of the scenario and assessment method on the choice of road alignment variants. Transp Policy 36:294–305

    Article  Google Scholar 

  12. Grujicic M, Pandurangan B, Xie X, Gramopadhye AK, Wagner D, Ozen M (2010) Musculoskeletal computational analysis of the influence of car-seat design/adjustments on long-distance driving fatigue. Ind Ergon 40:345–355

    Article  Google Scholar 

  13. Horne J, Reyner L (1995) Falling asleep at the wheel. Report TRL 168. Transport Research Laboratory, CrowThorne

  14. Hwang CL, Yoon K (1981) Multiple attribute decision making methods and applications. Springer, Berlin

    Google Scholar 

  15. ICPI (2015) Website of information and communications of police of Islamic Republic of Iran. http://news.police.ir

  16. Jahanshahloo GR, Hosseinzadeh Lotfi F, Izadikhah M (2006) Extension of the TOPSIS method for decision-making problems with fuzzy data. Appl Math Comput 181:1544–1551

    MATH  Article  Google Scholar 

  17. Khorasan Razavi Department of Road Maintenance and Transportation (2017) 2016 annual report. Planning and Budget Office

  18. Knapik M, Cyganek B (2019) Driver fatigue recognition based on yawn detection in thermal images. Neurocomputing 338:274–292

    Article  Google Scholar 

  19. Kvam PH, Vidakovic B (2007) Nonparametric statistics with applications to science and engineering. Wiley, New York

    Google Scholar 

  20. Land Transport Safety Authority (1998) Factsheet 24. Fatigue and road accidents. PO Box 2840, Wellington, New Zealand

  21. Liu YC, Wu TJ (2008) Fatigued driver’s driving behavior and cognitive task performance: effect of road environments and road environment changes. Saf Sci 47:1083–1089

    Article  Google Scholar 

  22. Liu CC, Hoskinga SG, Lennéa MG (2009) Predicting driver drowsiness using vehicle measures: recent insights and future challenges. J Saf Res 40:239–245

    Article  Google Scholar 

  23. Maclean AW, Davies DR, Thiele K (2003) The hazards and prevention of driving while sleepy. Sleep Med Rev 7(6):507–521

    Article  Google Scholar 

  24. Meng F, Li S, Cao L, Peng Q, Li M, Wang C, Zhang W (2016) Designing fatigue warning systems: the perspective of professional drivers. Appl Ergon 53:122–130

    Article  Google Scholar 

  25. Milia DL, Smolensky MH, Costa G, Howarth HD, Ohayon MM, Philip P (2011) Demographic factors, fatigue and driving accidents: an examination of the published literature. Accid Anal Prev 43:516–532

    Article  Google Scholar 

  26. Montella A (2005) Safety reviews of existing roads: quantitative safety assessment methodology. Transp Res Rec 1922:62–72

    Article  Google Scholar 

  27. Morad Y, Barkana Y, Zadok D, Hartstein M, Pras E, Bar-Dayan Y (2009) Ocular parameters as an objective tool for the assessment of truck drivers fatigue. Accid Anal Prev 41:856–860

    Article  Google Scholar 

  28. Ronen A, Oron-Gilad T, Gershon P (2014) The combination of short rest and energy drink consumption as fatigue countermeasures during a prolonged drive of professional truck drivers. J Saf Res 49:39–43

    Article  Google Scholar 

  29. Sadeghi A, Farhad H, Mohammadzadeh Moghaddam A, Jalili Qazizadeh M (2018) Identification of accident-prone sections in roadways with incomplete and uncertain inspection-based information: a distributed hazard index based on evidential reasoning approach. Reliab Eng Syst Saf 178:278–289

    Article  Google Scholar 

  30. Sagberg F (1999) Road accidents caused by drivers falling asleep. Accid Anal Prev 31(6):639–649

    Article  Google Scholar 

  31. Shiau TA, Huang WK (2014) User perspective of age-friendly transportation: a case study of Taipei City. Transp Policy 36:184–191

    Article  Google Scholar 

  32. Smolensky MH, Milia DL, Ohayon MM, Philip P (2011) Sleep disorders, medical conditions, and road accident risk. Accid Anal Prev 43:533–548

    Article  Google Scholar 

  33. Soccolich SA, Blanco M, Hanowski RJ, Olson RL, Morgan JF, Guo F, Wu S (2013) An analysis of driving and working hour on commercial motor vehicle driver safety using naturalistic data collection. Accid Anal Prev 58:249–258

    Article  Google Scholar 

  34. Thompson J, Newnam S, Stevenson M (2015) A model for exploring the relationship between payment structures, fatigue, crash risk and regulatory response in a heavy- vehicle transport system. Transp Res Part A 82:204–2015

    Google Scholar 

  35. Useche SA, Ortiz VG, Cendales BE (2017) Stress- related psychosocial factors at work, fatigue and risky driving behavior in bus rapid transport (BRT) drivers. Accid Anal Prev 104:106–114

    Article  Google Scholar 

  36. Wang Y, Ma C, Li Y (2018) Effects of prolonged tasks and rest patterns on driver’s visual behaviors, driving performance, and sleepiness awareness in tunnel environments: a simulator study. Iran J Sci Technol Trans Civ Eng 42:143–151

    Article  Google Scholar 

  37. WHO (2015) Global status report on road safety. World Health Organization, Geneva

    Google Scholar 

  38. Williamson A, Friswell R (2013) The effect of external non-driving factors, payment type and waiting and queuing on fatigue in long distance trucking. Accid Anal Prev 58:26–34

    Article  Google Scholar 

  39. Williamson A, Lombardi DA, Folkard S, Stutts J, Courtney TK, Connor JL (2011) The link between fatigue and safety. Accid Anal Prev 43:498–515

    Article  Google Scholar 

  40. Zhang G, Yau KKW, Zhang X, Li Y (2016) Traffic accidents involving fatigue driving and their extent of casualties. Accid Anal Prev 87:34–42

    Article  Google Scholar 

  41. Zheng C, Xiaojuan B, Yu W (2016) Fatigue driving detection based on haar feature and extreme learning machine. J China Univ Posts Telecommun 23(4):91–100

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Aliasghar Sadeghi.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Keywords

  • Heavy vehicle
  • Fatigue management
  • Statistical analysis
  • TOPSIS analysis