114 research outputs found
Commercial motor vehicle health and fatigue study: final report
No full textFatigue is a major risk factor in commercial motor vehicle operations, identified in naturalistic driving studies as a contributing factor in approximately 20 percent of safety-critical incidents. Understanding the nature of fatigued driving requires attention to several elements of the driving situation, including driver characteristics. The purpose of the present report is to explore driver body mass index (BMI) as a characteristic which may put one at increased risk for driving while fatigued.Douglas M. Wiegand, Richard J. Hanowski and Shelby E. McDonal
Fatigue analyses: from 16 months of naturalistic commercial motor vehicle driving data
No full textUnder the sponsorship of the National Surface Transportation Safety Center for Excellence, an existing naturalistic data set from the Drowsy Driver Warning System Field Operational Test (DDWS FOT) was expanded and analyzed to gain a greater understanding of the conditions which are associated with fatigue in commercial motor vehicle (CMV) driving.Douglas M. Wiegand, Richard J. Hanowski, Rebecca Olson and Whitney Melvi
The Impact of Local/Short Haul Operations on Driver Fatigue
No full textMassie, Blower, and Campbell (1997) indicate that trucks that operate less than 50 miles from the vehicle's home base comprise 58% of the trucking industry. However, despite being the largest segment, research involving local/short haul (L/SH) operations has been scant. In fact, little is known about the general safety issues in L/SH operations.
As a precursor to the present research, Hanowski, Wierwille, Gellatly, Early, and Dingus (1998) conducted a series of focus groups in which L/SH drivers provided their perspective on safety issues, including fatigue, in their industry. As a follow-up to the Hanowski et al. work, the effort presented here consisted of an on-road field study where L/SH trucks were instrumented with data collection equipment. Two L/SH trucking companies and 42 L/SH drivers participated in this research. To the author's knowledge, this is the first in-situ data collection effort of its kind with L/SH drivers.
The analyses focused on determining if fatigue is an issue in L/SH operations. Of primary interest were critical incidents (near-crashes) where drivers were judged to be at fault. The results of the analyses indicated that fatigue was present immediately prior to driver involvement in at-fault critical incidents. Though it is difficult to determine why fatigue was present, the results seem to indicate that much of the fatigue that the drivers' experienced was brought with them to the job, rather than being caused by the job.
There are four basic outputs of the Phase II research: (1) a description of the L/SH drivers who participated, (2) a description of critical incidents, (3) a determination if fatigue is an issue in L/SH trucking, and (4) the validation of the fatigue factors cited in Hanowski et al. (1998) using a proposed fatigue model. These four outputs culminate in a set of pragmatic guidelines to address fatigue and other safety issues in L/SH operations. Five guidelines are proposed that are directed at: (1) driver education with regard to on-the-job drowsiness/inattention, (2) driver education with regard to sleep hygiene, (3) driver training, particularly for novice L/SH truck drivers, (4) driver screening, and (5) public monitoring of L/SH driver performance.Ph. D
J Occup Environ Med
No full textObjectiveThis study aims to quantify the crash risk for truck drivers with multiple comorbid medical conditions, after adjusting for confounders.MethodsThis retrospective cohort of 38,184 drivers evaluated concomitant medical conditions and subsequent crash data between 1/1/2005\u201310/31/2012. Hazard ratios (HRs) and 95% confidence intervals (95% CI) were calculated for any cause and preventable crashes of varying severity.ResultsDrivers with three or more medical conditions had significantly increased risk of preventable Department of Transportation (DOT) reportable crashes (HR=2.53, 95% CI=1.65\u20133.88) and preventable crashes with injuries (HR=2.23, 95% CI=1.09\u20135.31) after adjustment for covariates. Similarly, adjusted HRs were 2.55 (95% CI=1.37\u20134.73) for any cause DOT-reportable crashes and 3.21 (95% CI=1.18\u20138.75) for any cause crashes with injuries.ConclusionsHaving three concomitant medical conditions may be a statistically significant risk factor for preventable and any cause DOT-reportable crashes and crashes with injuries.K01 OH009794/OH/NIOSH CDC HHS/United StatesT42 OH008414/OH/NIOSH CDC HHS/United State
J Occup Environ Med
No full textObjective:The objective of this study was to assess relationships between body mass index (BMI) and comorbid conditions within a large sample of truck drivers.Methods:Commercial driver medical examination data from 88,246 commercial drivers between 2005 and 2012 were analyzed for associations between BMI, medical disorders, and driver certification.Results:Most drivers were obese (53.3%, BMI >30.0 kg/m2) and morbidly obese (26.6%, BMI >35.0 kg/m2), higher than prior reports. Obese drivers were less likely to be certified for 2 years and more likely to report heart disease, hypertension, diabetes mellitus, nervous disorders, sleep disorders, and chronic low back pain (all P < 0.0001). There are relationships between multiple potentially disqualifying conditions and increasing obesity (P < 0.0001). Morbid obesity prevalence increased 8.9% and prevalence of three or more multiple conditions increased fourfold between 2005 and 2012.Conclusions:Obesity is related to multiple medical factors as well as increasing numbers of conditions that limit driving certification.1K01OH009794/OH/NIOSH CDC HHS/United StatesT42/CCT810426-10/PHS HHS/United State
Towards developing a US-EU common distracted driving taxonomy : updating a naturalistic driving data coding approach
No full textNaturalistic video data reduction is a process of identifying information from video and putting it into a format that can be analyzed. Developing a sufficiently detailed event coding scheme is critical to this process. This report outlines an effort to refine VTTI's existing coding scheme, to identify 'driver distraction' using a pragmatic definition of driver distraction from the literature. -- Report website.Richard J. Hanowsk
Accid Anal Prev
No full textFatigued and drowsy driving has been found to be a major cause of truck crashes. Lack of sleep is the number one cause of fatigue and drowsiness. However, there are limited data on the sleep patterns (sleep duration, sleep percentage in the duration of non-work period, and the time when sleep occurred) of truck drivers in non-work periods and the impact on driving performance. This paper examined sleep patterns of 96 commercial truck drivers during non-work periods and evaluated the influence these sleep patterns had on truck driving performance. Data were from the Naturalistic Truck Driving Study. Each driver participated in the study for approximately four weeks. A shift was defined as a non-work period followed by a work period. A total of 1397 shifts were identified. Four distinct sleep patterns were identified based on sleep duration, sleep start/end point in a non-work period, and the percentage of sleep with reference to the duration of non-work period. Driving performance was measured by safety-critical events, which included crashes, near-crashes, crash-relevant conflicts, and unintentional lane deviations. Negative binomial regression was used to evaluate the association between the sleep patterns and driving performance, adjusted for driver demographic information. The results showed that the sleep pattern with the highest safety-critical event rate was associated with shorter sleep, sleep in the early stage of a non-work period, and less sleep between 1 a.m. and 5 a.m. This study also found that male drivers, with fewer years of commercial vehicle driving experience and higher body mass index, were associated with deteriorated driving performance and increased driving risk. The results of this study could inform hours-of-service policy-making and benefit safety management in the trucking industry.CC999999/Intramural CDC HHS/United State
Evaluation of a Prototype System for the Automatic Capture of School Bus Passing Violations
No full textIt is illegal to pass a stopped school bus when the vehicle's stop-arm is extended and the red lights are flashing. Public opinion on this issue is very clear. A random phone survey of the public conducted by the National Highway Traffic Safety Administration (NHTSA) reported that more than 90 percent of respondents rated "passing a school bus that has its red lights flashing and the stop arm in full view" as a somewhat or extremely dangerous driving behavior (Boyle, Dienstfrey, and Sothoron, 1998). Despite this public opinion, there is evidence that the number of vehicles that illegally pass school buses each day is substantial. Based on data collected throughout the state of Illinois, the Illinois Department of Transportation (1996) estimated that more than 10,000 vehicles illegally pass school buses every day. Similar findings were reported in Florida (Center for Urban Transportation Research, 1996).
To address this problem, NHTSA sponsored a research effort aimed at developing an automated system for detecting and recording the license plates of vehicles as well as their drivers who illegally pass school buses. The overall objective of this research was to develop a prototype system that would automatically detect and record vehicles that illegally pass school buses (i.e., bus' stop-arm is extended and lights are flashing). Based on the results of technical, administrative, and legal feasibility analyses, system specifications were developed and a prototype unit was built. The prototype system was then field-tested in a variety of real-world conditions in both a controlled setting and on an actual school bus route. The results of the field test proved the prototype system to be comparable with other automated enforcement systems. Testing showed that recorded images were more identifiable when the violation occurred in the lane next to the school bus. In addition, frontal facial recordings were found to be 1.5 times more useful then profile recordings. It must be stressed that the purpose of the field test was to gather data that could be used in support of design recommendations and changes for the next generation of the system.Master of Scienc
Development and evaluation of a naturalistic observer rating of drowsiness protocol : final report
No full textVTTI researchers have developed a method for rating driver drowsiness based on the evaluation of naturalistic video footage of the driver's face and upper torso. This measure, referred to as the Observer Rating of Drowsiness (ORD) is based on subjective assessments of the driver's facial tone, behavior, and mannerisms, and is set to a 100-point continuous scale. ORD is assessed based on the 60 seconds of video prior to a trigger event (or baseline epoch). Therefore, ORD is a relatively quick/efficient method for assessing one's drowsiness level, which can then be used to describe a driver's state and investigate whether drowsiness was a contributing factor to a safety-critical event.Douglas M. Wiegand, Julie McClafferty, Shelby E. McDonald and Richard J. Hanowsk
Identifying high-risk commercial truck drivers using a naturalistic approach
No full textThe current report investigated the 'high-risk' driver concept, and predictors associated with group membership, in a sample of 200 CMV drivers using naturalistic data from the Drowsy Driver Warning System Field Operational Test and the Naturalistic Truck Driving Study. A cluster analysis revealed three distinct groups of drivers (safe, average, and risky) based on the rate of safety-critical events per mile traveled. The risky group accounted for 50.3% of the total safety-critical events, but only 7.1% of the total miles traveled. Various anthropometric and demographic variables were found to have an association to group membership; however, these relationships were weak (mainly due to the small sample size). The current study found support for the high-risk driver concept; future research should focus on identifying risky drivers so that targeted safety management techniques can be used to improve driving behavior. -- Report website.Susan Soccolich, Jeffrey Hickman and Richard Hanowsk
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