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

ABSTRACT Objective: Real-time driver monitoring systems represent a solution to address key behavioral risks as they occur, particularly distraction and fatigue. The efficacy of these systems in real-world settings is largely unknown. This article has three objectives: (1) to document the incidence and duration of fatigue in real-world commercial truck-driving operations, (2) to determine the reduction, if any, in the incidence of fatigue episodes associated with providing feedback, and (3) to tease apart the relative contribution of in-cab warnings from 24/7 monitoring and feedback to employers. Methods: Data collected from a commercially available in-vehicle camera-based driver monitoring system installed in a commercial truck fleet operating in Australia were analyzed. The real-time driver monitoring system makes continuous assessments of driver drowsiness based on eyelid position and other factors. Data were collected in a baseline period where no feedback was provided to drivers. Real-time feedback to drivers then occurred via in-cab auditory and haptic warnings, which were further enhanced by direct feedback by company management when fatigue events were detected by external 24/7 monitors. Fatigue incidence rates and their timing of occurrence across the three time periods were compared. Results: Relative to no feedback being provided to drivers when fatigue events were detected, in-cab warnings resulted in a 66% reduction in fatigue events, with a 95% reduction achieved by the real-time provision of direct feedback in addition to in-cab warnings (p < 0.01). With feedback, fatigue events were shorter in duration a d occurred later in the trip, and fewer drivers had more than one verified fatigue event per trip. Conclusions: That the provision of feedback to the company on driver fatigue events in real time provides greater benefit than feedback to the driver alone has implications for companies seeking to mitigate risks associated with fatigue. Having fewer fatigue events is likely a reflection of the device itself and the accompanying safety culture of the company in terms of how the information is used. Data were analysed on a per-truck trip basis, and the findings are indicative of fatigue events in a large-scale commercial transport fleet. Future research ought to account for individual driver performance, which was not possible with the available data in this retrospective analysis. Evidence that real-time driver monitoring feedback is effective in reducing fatigue events is invaluable in the development of fleet safety policies, and of future national policy and vehicle safety regulations. Implications for automotive driver monitoring are discussed.

[1]  M. Jaśkiewicz,et al.  Boosting car safety in the EU , 2018, 2018 XI International Science-Technical Conference Automotive Safety.

[2]  Karl Pajo,et al.  Transport company safety climate—The impact on truck driver behavior and crash involvement , 2017, Traffic injury prevention.

[3]  A.J. Filtness,et al.  Sleep-related crash characteristics: Implications for applying a fatigue definition to crash reports. , 2017, Accident; analysis and prevention.

[4]  Michael G. Lenné,et al.  Predicting drowsiness-related driving events: a review of recent research methods and future opportunities , 2016 .

[5]  Mario Cleves,et al.  An Introduction To Survival Analysis Using Stata Third | , 2012 .

[6]  E Townsend,et al.  Making taxis safer: managing road risks for taxi drivers, their passengers and other road users , 2016 .

[7]  Sarah Jones,et al.  Driver state sensing (DSS) machines at Toll Resources and Government Logistics , 2016 .

[8]  Michael G. Lenné,et al.  Real-time feedback reduces the incidence of fatigue events in heavy vehicle fleets , 2016 .

[9]  Michael N. Mitchell Stata for the Behavioral Sciences , 2015 .

[10]  Keshia M. Pollack,et al.  Safety climate and the distracted driving experiences of truck drivers. , 2015, American journal of industrial medicine.

[11]  Mark Stevenson,et al.  Associations Between Heavy-Vehicle Driver Compensation Methods, Fatigue-Related Driving Behavior, and Sleepiness , 2014, Traffic injury prevention.

[12]  T. Dingus,et al.  Distracted driving and risk of road crashes among novice and experienced drivers. , 2014, The New England journal of medicine.

[13]  Feng Guo,et al.  Keep your eyes on the road: young driver crash risk increases according to duration of distraction. , 2014, The Journal of adolescent health : official publication of the Society for Adolescent Medicine.

[14]  Drew Dawson,et al.  Look before you (s)leep: evaluating the use of fatigue detection technologies within a fatigue risk management system for the road transport industry. , 2014, Sleep medicine reviews.

[15]  Contribution of transport to economic development , 2014 .

[16]  Justine Westlake,et al.  Specific sleepiness symptoms are indicators of performance impairment during sleep deprivation. , 2014, Accident; analysis and prevention.

[17]  Yueng-Hsiang Huang,et al.  A mediation model linking dispatcher leadership and work ownership with safety climate as predictors of truck driver safety performance. , 2014, Accident; analysis and prevention.

[18]  Michael Fitzharris,et al.  Driver inattention and driver distraction in serious casualty crashes: data from the Australian National Crash In-depth Study. , 2013, Accident; analysis and prevention.

[19]  J. Burnham,et al.  Freight Transport and the Modern Economy , 2013 .

[20]  George Yannis,et al.  Traffic Safety Basic Facts 2012 : Heavy Goods Vehicles and Buses , 2013 .

[21]  John D. Lee,et al.  How Dangerous Is Looking Away From the Road? Algorithms Predict Crash Risk From Glance Patterns in Naturalistic Driving , 2012, Hum. Factors.

[22]  Niloufar Azmi,et al.  A Driver Fatigue Monitoring and Haptic Jacket-based Warning System , 2012 .

[23]  Jeffrey S. Hickman,et al.  Use of a video monitoring approach to reduce at-risk driving behaviors in commercial vehicle operations , 2011 .

[24]  A. Reggiani,et al.  Transportation and Economic Development Challenges , 2011 .

[25]  Dov Zohar,et al.  Thirty years of safety climate research: reflections and future directions. , 2010, Accident; analysis and prevention.

[26]  Sharon Newnam,et al.  Safety in work vehicles: a multilevel study linking safety values and individual predictors to work-related driving crashes. , 2008, The Journal of applied psychology.

[27]  J. Hilbe Negative Binomial Regression: Preface , 2007 .

[28]  Richard Goldstein,et al.  Regression Methods in Biostatistics: Linear, Logistic, Survival and Repeated Measures Models , 2006, Technometrics.

[29]  Thomas A. Dingus,et al.  The Prevalence of Driver Fatigue in an Urban Driving Environment : Results from the 100-Car Naturalistic Driving Study , 2006 .

[30]  Thomas A. Dingus,et al.  Driver distraction in long-haul truck drivers , 2005 .

[31]  S D Baulk,et al.  Countermeasures to driver fatigue: a review of public awareness campaigns and legal approaches , 2005, Australian and New Zealand journal of public health.

[32]  T L Bunn,et al.  Sleepiness/fatigue and distraction/inattention as factors for fatal versus nonfatal commercial motor vehicle driver injuries. , 2005, Accident; analysis and prevention.

[33]  Jane C Stutts,et al.  Driver risk factors for sleep-related crashes. , 2003, Accident; analysis and prevention.

[34]  Richard J Hanowski,et al.  An on-road study to investigate fatigue in local/short haul trucking. , 2003, Accident; analysis and prevention.

[35]  K. Campbell,et al.  The large truck crash causation study , 2002 .

[36]  R. Norton,et al.  Driver sleepiness and risk of serious injury to car occupants: population based case control study , 2002, BMJ : British Medical Journal.

[37]  A T McCartt,et al.  Factors associated with falling asleep at the wheel among long-distance truck drivers. , 2000, Accident; analysis and prevention.

[38]  P. V. Rao,et al.  Applied Survival Analysis: Regression Modeling of Time to Event Data , 2000 .

[39]  C. Cook,et al.  Sleep related vehicle accidents , 1995, BMJ.

[40]  Ronald R Knipling,et al.  CRASHES AND FATALITIES RELATED TO DRIVER DROWSINESS/FATIGUE , 1994 .