The relative importance of real-time in-cab and external feedback in managing fatigue in real-world commercial transport operations

References

• Australian Government. CRC projects directory and selection round outcomes, 2016. Canberra, Australia: Australian Government; 2016.
   Google Scholar
• Azmi N. A driver fatigue monitoring and haptic jacket-based warning system. Ottawa, ON, Canada: University of Ottawa; 2012.
   Google Scholar
• Beanland V, Fitzharris M, Young KL, Lenné MG. Driver inattention and driver distraction in serious casualty crashes: Data from the Australian National Crash In-depth Study. Accid Anal Prev. 2013;54(0):99–107.
   PubMed Web of Science ®Google Scholar
• Bidasca L, Townsend E. Making taxis safer; Managing road risks for taxi drivers, their passengers and other road users. Brussels, Belgium: European Transport Safety Council (ETSC); 2016.
   Google Scholar
• Bunn TL, Slavova S, Struttmann TW, Browning SR. Sleepiness/fatigue and distraction/inattention as factors for fatal versus nonfatal commercial motor vehicle driver injuries. Accid Anal Prev. 2005;37(5):862–869.
   PubMed Web of Science ®Google Scholar
• Bureau of Infrastructure, Transport and Regional Economics. Freightline 1—Australian freight transport overview. Canberra, Australia: Author; 2014.
   Google Scholar
• Bureau of Infrastructure, Transport and Regional Economics. Heavy truck safety: Crash analysis and trends. Canberra, Australia: Author; 2016a.
   Google Scholar
• Bureau of Infrastructure, Transport and Regional Economics. Yearbook 2016—Australia Infrastructure Statistics. Canberra, Australia: Author; 2016b.
   Google Scholar
• Button K, Reggiani A. Transportation and Economic Development Challenges. London, UK: Edward Elgar; 2011.
   Google Scholar
• Cleeves M, Gould WW, Marchenko YV. An Introduction to Survival Analysis Using Stata. College Station, TX: StataCorp LP; 2016.
   Google Scholar
• Connor J, Norton R, Ameratunga S, et al. Driver sleepiness and the risk of serious injury to car occupants: Population-based case control study. Br Med J. 2002;324:1125–1129.
   PubMed Web of Science ®Google Scholar
• Dawson D, Searle AK, Paterson JL. Look before you (s)leep: Evaluating the use of fatigue detection technologies within a fatigue risk management system for the road transport industry. Sleep Med Rev. 2014;18(2):141–152.
   PubMed Web of Science ®Google Scholar
• Dinges DF, Mallis MM, Maislin G, Powell IV JW. Final report: Evaluation of techniques for ocular measurement as an index of fatigue and as the basis for alertness management. Washington, DC: National Highway Traffic Safety Administration (NHTSA); 1998.
   Google Scholar
• European Commission. Traffic safety basic facts on heavy goods vehicles and buses. Brussels, Belgium: European Commission, Directorate General for Transport; 2016a.
   Google Scholar
• European Commission. Saving lives: Boosting car safety in the EU, COM(2016) 787 final. Brussels, Belgium: Author; 2016b.
   Google Scholar
• Federal Motor Carrier Safety Administration. Report to Congress on the Large Truck Crash Causation Study. Washington, DC: Author; 2006.
   Google Scholar
• Federal Motor Carrier Safety Administration. 2016 Pocket Guide to Large Truck and Bus Statistics. Washington, DC: Author; 2016a.
   Google Scholar
• Federal Motor Carrier Safety Administration. Large truck and bus crash facts 2014. Washington, DC: Author; 2016b.
   Google Scholar
• Filtness AJ, Armstrong KA, Watson A, Smith SS. Sleep-related crash characteristics: Implications for applying a fatigue definition to crash reports. Accid Anal Prev. 2017;99(Part B):440–444.
   PubMed Web of Science ®Google Scholar
• Fletcher A, McCulloch K, Baulk SD, Dawson D. Countermeasures to driver fatigue: A review of public awareness campaigns and legal approaches. Aust N Z J Public Health. 2005;29:471–476.
   PubMed Web of Science ®Google Scholar
• Hanowski RJ, Wierwille WW, Dingus TA. An on-road study to investigate fatigue in local/short haul trucking. Accid Anal Prev. 2003;35(2):153–160.
   PubMed Web of Science ®Google Scholar
• Hanowski RJ, Perez MA, Dingus TA. Driver distraction in long-haul truck drivers. Transp Res Part F Traffic Psychol Behav. 2005;8(6):441–458.
   Web of Science ®Google Scholar
• Health and Safety Executive. A guide to workplace transport safety (3rd ed.). London, UK: Author; 2016.
   Google Scholar
• Hickman JS, Hanowski RJ. Use of a video monitoring approach to reduce at-risk driving behaviors in commercial vehicle operations. Transp Res Part F Traffic Psychol Behav. 2011;14(3):189–198.
   Web of Science ®Google Scholar
• Hilbe JM. Negative Binomial Regression. Cambridge, UK: Cambridge University Press; 2007.
   Google Scholar
• Horne JA, Reyner LA. Sleep related vehicle accidents. Br Med J. 1995;310:565–567.
   PubMed Web of Science ®Google Scholar
• Hosmer DW, Lemeshow S. Applied Survival Analysis: Regression Modelling of Time to Event Data. 2nd ed. New York, NY: John Wiley & Sons; 1999.
   Google Scholar
• Howard ME, Jackson ML, Berlowitz D, et al. Specific sleepiness symptoms are indicators of performance impairment during sleep deprivation. Accid Anal Prev. 2014;62:1–8.
   PubMed Web of Science ®Google Scholar
• Klauer SG, Neale VL, Dingus TA, Sudweeks JD, Ramsey DJ. The prevalence of driver fatigue in an urban driving environment: Results from the 100-Car Naturalistic Driving Study. In: Proceedings of the 2005 International Conference on Fatigue Management in Transportation Operations, September 2005 (TP 14620E), 2005.
   Google Scholar
• Klauer SG, Guo F, Simons-Morton BG, Ouimet MC, Lee SE, Dingus TA. Distracted driving and risk of road crashes among novice and experienced drivers. N Eng J Med. 2014;370(1):54–59.
   PubMed Web of Science ®Google Scholar
• Knipling RR, Wan J-S. Crashes and fatalities related to driver drowsiness/fatigue, Research note (November). Washington, DC: National Highway Traffic Safety Administration; 1994.
   Google Scholar
• Law B, Jones S. Driver state sensing (DSS) machines at toll resources and government logistics. J Australas Coll Road Saf. 2016;27(1):52.
   Web of Science ®Google Scholar
• Lenné MG, Fitzharris M. Real-time feedback reduces the incidence of fatigue events in heavy vehicle fleets. In: Proceedings of the 23rd ITS World Congress, Melbourne, Australia, 2016.
   Google Scholar
• Lenné MG, Jacobs EE. Predicting drowsiness-related driving events: a review of recent research methods and future opportunities. Theor Issues Ergon Sci. 2016;17(5–6):533–553.
   Web of Science ®Google Scholar
• Liang Y, Lee JD, Yekhshatyan L. How dangerous is looking away from the road? Algorithms predict crash risk from glance patterns in naturalistic driving. Hum Factors. 2012;54(6):1104–1116.
   PubMed Web of Science ®Google Scholar
• McCartt AT, Rohrbaugh JW, Hammer MC, Fuller SZ. Factors associated with falling asleep at the wheel among long-distance truck drivers. Accid Anal Prev. 2000;32(4):493–504.
   PubMed Web of Science ®Google Scholar
• Ministry of Transport. Contribution of transport to economic development: International literature review with New Zealand perspectives. Wellington, New Zealand: New Zealand Government; 2014.
   Google Scholar
• Mitchell MN. Stata for the Behavioral Sciences. College Station, TX: StataCorp LP; 2015.
   Google Scholar
• National Heavy Vehicle Regulator. Standard hours—Work and rest hours requirements. 2017. Fortitude Valley, Brisbane: Author; 2017.
   Google Scholar
• National Highway Traffic Safety Administration. NHTSA drowsy driving research and program plan, DOT HS 812 252. Washington, DC: Author; 2016.
   Google Scholar
• Newnam S, Griffin MA, Mason C. Safety in work vehicles: A multilevel study linking safety values and individual predictors to work-related driving crashes. J Appl Psychol. 2008;93(3):632.
   PubMed Web of Science ®Google Scholar
• Newton J, Howard E, Wishart D. A guide to applying road safety within a workplace (WRSG). Canberra, Australia, and Wellington, New Zealand: National Transport Commission (Australia) and Accident Compensation Corporation (New Zealand); 2013.
   Google Scholar
• Occupational Safety and Health Administration. Guidelines for employers to reduce motor vehicle crashes. 2016. https://www.osha.gov/Publications/motor_vehicle_guide.html. Accessed January 8, 2017.
   Google Scholar
• Savy M, Burnham J. Freight Transport and the Modern Economy. London, UK: Edward Elgar; 2013.
   Google Scholar
• Simons-Morton BG, Guo F, Klauer SG, Ehsani JP, Pradhan AK. Keep your eyes on the road: Young driver crash risk increases according to duration of distraction. J Adolesc Health. 2014;54(5):S61–S67.
   PubMed Web of Science ®Google Scholar
• StataCorp. Stata MP Version 12 for Windows [computer program]. College Station, TX: Author; 2011.
   Google Scholar
• Stutts JC, Wilkins JW, Scott Osberg J, Vaughn BV. Driver risk factors for sleep-related crashes. Accid Anal Prev. 2003;35(3):321–331.
   PubMed Web of Science ®Google Scholar
• Sullman MJ, Stephens AN, Pajo K. Transport company safety climate—The impact on truck driver behavior and crash involvement. Traffic Inj Prev. 2016:1–6.
   Web of Science ®Google Scholar
• Swedler DI, Pollack KM, Agnew J. Safety climate and the distracted driving experiences of truck drivers. Am J Ind Med. 2015;58(7):746–755.
   PubMed Web of Science ®Google Scholar
• Thompson J, Stevenson M. Associations Between heavy-vehicle driver compensation methods, fatigue-related driving behavior, and sleepiness. Traffic Inj Prev. 2014;15(suppl 1):S10–S14.
   PubMed Web of Science ®Google Scholar
• Vittinghoff E, Glidden DV, Shiboski SC, McCulloch CE. Regression Methods in Biostatistics: Linear, Logistic, Survival and Repeated Measures Models. New York, NY: Springer; 2005.
   Google Scholar
• Zohar D. Thirty years of safety climate research: Reflections and future directions. Accid Anal Prev. 2010;42(5):1517–1522.
   PubMed Web of Science ®Google Scholar
• Zohar D, Huang Y-H, Lee J, Robertson M. A mediation model linking dispatcher leadership and work ownership with safety climate as predictors of truck driver safety performance. Accid Anal Prev. 2014;62:17–25.
   PubMed Web of Science ®Google Scholar