Advertisement

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

  • Masoud Ghasemi Noughabi
  • Aliasghar SadeghiEmail author
  • Abolfazl Mohammadzadeh Moghaddam
  • Morteza Jalili Qazizadeh
Research Paper
  • 2 Downloads

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.

Keywords

Heavy vehicle Fatigue management Statistical analysis TOPSIS analysis 

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 43:565–572CrossRefGoogle 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 91:55–63CrossRefGoogle 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 324:1125CrossRefGoogle 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 55:199–209CrossRefGoogle 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 43:549–564CrossRefGoogle 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, 1–30Google Scholar
  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):179–196CrossRefGoogle Scholar
  8. Friswell R, Williamson A (2013) Comparison of the fatigue experiences of short haul light and long distance heavy vehicle drivers. Saf Sci 57:203–213CrossRefGoogle Scholar
  9. Fu R, Wang H, Zhao W (2016) Dynamic driver fatigue detection using hidden Markov model in real driving condition. Expert Syst Appl 63:397–411CrossRefGoogle 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 43:573–590CrossRefGoogle Scholar
  11. Gardziejczyk W, Zabicki P (2014) The influence of the scenario and assessment method on the choice of road alignment variants. Transp Policy 36:294–305CrossRefGoogle 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 40:345–355CrossRefGoogle Scholar
  13. Horne J, Reyner L (1995) Falling asleep at the wheel. Report TRL 168. Transport Research Laboratory, CrowThorneGoogle Scholar
  14. Hwang CL, Yoon K (1981) Multiple attribute decision making methods and applications. Springer, BerlinCrossRefGoogle 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 181:1544–1551zbMATHGoogle Scholar
  17. Khorasan Razavi Department of Road Maintenance and Transportation (2017) 2016 annual report. Planning and Budget OfficeGoogle Scholar
  18. Knapik M, Cyganek B (2019) Driver fatigue recognition based on yawn detection in thermal images. Neurocomputing 338:274–292CrossRefGoogle Scholar
  19. Kvam PH, Vidakovic B (2007) Nonparametric statistics with applications to science and engineering. Wiley, New YorkCrossRefGoogle Scholar
  20. Land Transport Safety Authority (1998) Factsheet 24. Fatigue and road accidents. PO Box 2840, Wellington, New ZealandGoogle Scholar
  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 47:1083–1089CrossRefGoogle Scholar
  22. Liu CC, Hoskinga SG, Lennéa MG (2009) Predicting driver drowsiness using vehicle measures: recent insights and future challenges. J Saf Res 40:239–245CrossRefGoogle Scholar
  23. Maclean AW, Davies DR, Thiele K (2003) The hazards and prevention of driving while sleepy. Sleep Med Rev 7(6):507–521CrossRefGoogle 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 53:122–130CrossRefGoogle 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 43:516–532CrossRefGoogle Scholar
  26. Montella A (2005) Safety reviews of existing roads: quantitative safety assessment methodology. Transp Res Rec 1922:62–72CrossRefGoogle 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 41:856–860CrossRefGoogle 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 49:39–43CrossRefGoogle 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 178:278–289CrossRefGoogle Scholar
  30. Sagberg F (1999) Road accidents caused by drivers falling asleep. Accid Anal Prev 31(6):639–649CrossRefGoogle Scholar
  31. Shiau TA, Huang WK (2014) User perspective of age-friendly transportation: a case study of Taipei City. Transp Policy 36:184–191CrossRefGoogle Scholar
  32. Smolensky MH, Milia DL, Ohayon MM, Philip P (2011) Sleep disorders, medical conditions, and road accident risk. Accid Anal Prev 43:533–548CrossRefGoogle 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 58:249–258CrossRefGoogle 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 82:204–2015Google 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 104:106–114CrossRefGoogle 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 42:143–151CrossRefGoogle Scholar
  37. WHO (2015) Global status report on road safety. World Health Organization, GenevaGoogle 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 58:26–34CrossRefGoogle Scholar
  39. Williamson A, Lombardi DA, Folkard S, Stutts J, Courtney TK, Connor JL (2011) The link between fatigue and safety. Accid Anal Prev 43:498–515CrossRefGoogle Scholar
  40. Zhang G, Yau KKW, Zhang X, Li Y (2016) Traffic accidents involving fatigue driving and their extent of casualties. Accid Anal Prev 87:34–42CrossRefGoogle 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):91–100CrossRefGoogle Scholar

Copyright information

© Shiraz University 2019

Authors and Affiliations

  • Masoud Ghasemi Noughabi
    • 1
  • Aliasghar Sadeghi
    • 2
    Email author
  • Abolfazl Mohammadzadeh Moghaddam
    • 3
  • Morteza Jalili Qazizadeh
    • 4
  1. 1.Transportation and Terminals Department of South Khorasan ProvinceBirjandIran
  2. 2.Department of Civil EngineeringHakim Sabzevari UniversitySabzevarIran
  3. 3.Department of Civil EngineeringFerdowsi University of MashhadMashhadIran
  4. 4.Department of Civil EngineeringQuchan University of TechnologyQuchanIran

Personalised recommendations