Unravelling the Complexity of Irregular Shiftwork, Fatigue and Sleep Health for Commercial Drivers and the Associated Implications for Roadway Safety.

Fatigue can be a significant problem for commercial motor vehicle (CMV) drivers. The lifestyle of a long-haul CMV driver may include long and irregular work hours, inconsistent sleep schedules, poor eating and exercise habits, and mental and physical stress, all contributors to fatigue. Shiftwork is associated with lacking, restricted, and poor-quality sleep and variations in circadian rhythms, all shown to negatively affect driving performance through impaired in judgment and coordination, longer reaction times, and cognitive impairment. Overweight and obesity may be as high as 90% in CMV drivers, and are associated with prevalent comorbidities, including obstructive sleep apnea, hypertension, and cardiovascular and metabolic disorders. As cognitive and motor processing declines with fatigue, driver performance decreases, and the risk of errors, near crashes, and crashes increases. Tools and assessments to determine and quantify the nature, severity, and impact of fatigue and sleep disorders across a variety of environments and populations have been developed and should be critically examined before being employed with CMV drivers. Strategies to mitigate fatigue in CMV operations include addressing the numerous personal, health, and work factors contributing to fatigue and sleepiness. Further research is needed across these areas to better understand implications for roadway safety.

Challenges in Conducting Empirical Epidemiological Research with Truck and Bus Drivers in Diverse Settings in North America.

Over 6.5 million commercial vehicle drivers were operating a large truck or bus in the United States in 2020. This career often has high stress and long working hours, with few opportunities for physical activity. Previous research has linked these factors to adverse health conditions. Adverse health conditions affect not only the professional drivers' wellbeing but potentially also commercial motor vehicle (CMV) operators' safe driving ability and public safety for others sharing the roadway. The prevalence of health conditions with high impact on roadway safety in North American CMV drivers necessitates empirical epidemiological research to better understand and improve driver health. The paper presents four challenges in conducting epidemiological research with truck and bus drivers in North America and potential resolutions identified in past and current research. These challenges include (1) the correlation between driving performance, driving experience, and driver demographic factors; (2) the impact of medical treatment status on the relationship between health conditions and driver risk; (3) capturing accurate data in self-report data collection methods; and (4) reaching the CMV population for research. These challenges are common and influential in epidemiological research of this population, as drivers face severe health issues, health-related federal regulations, and the impact of vehicle operation on the safety of themselves and others using the roadways.

What Medical Conditions Limit or Medically Disqualify Truck Drivers: A Large Cross Sectional Study.

Commercial Motor Vehicle drivers must be medically certified to obtain/maintain a commercial driver license. 88,246 exams from 2005 to 2012 were analyzed for relationships between health and certification length. Relationships were quantified using adjusted odds ratios (ORs). Most conditions and/or examination findings had statically significantly limited medical certification. Obesity > 35 kg/m2, hypertension and diabetes mellitus requiring medication were most common. Significant and meaningful relationships were found for opioid or benzodiazepine use (OR = 7.30), heart disease (OR = 5.19), musculoskeletal conditions (OR = 5.13), seizures (10.18), stroke (OR = 6.73), neurological (OR = 18.51) and vascular (OR = 11.83). Drivers with 2 or more of 13 medical conditions were statistically significantly more likely to have limited medical certification (OR = 122.35) or disqualification (OR = 4.91). Drivers with any condition are more likely to have limited medical certification. There is variability in medical certification lengths related to medical conditions and differences between examiners.

Reducing risky driving: Assessing the impacts of an automatically-assigned, targeted web-based instruction program.

INTRODUCTION: Transportation safety research has consistently shown driver behavior is the primary cause in the majority of crashes. This study evaluated the effectiveness of an automatically-assigned, targeted web-based instruction program to reduce risky driving behavior. METHOD: This quasi-experiment used a within-subjects, multiple-baseline stepwise ABC design; where "A" was the Phase I baseline, "B" was the Phase II driver awareness of program, and "C" was the Phase III WBI program. RESULTS: A significant reduction in rates of risky driving behaviors coincided with the implementation of the WBI program, even for those drivers who did not receive WBI but were included in the program. More specifically, excessive speeding was significantly reduced by 73.93% from baseline to intervention across all drivers. For those drivers who received WBI, the program coincided with statistically significant reductions in speeding, hard braking, and hard cornering. The first WBI course assigned and completed was the most impactful in reducing at-risk driving behavior. CONCLUSIONS: These results show that the automatically-assigned, targeted WBI program was an effective method in reducing risky driving behaviors, not only for those drivers that received training, but for all drivers. The authors hypothesize the reduction in risky driving behaviors was not the result of the WBI, but instead from the implicit feedback of being assigned a training courses, the development of implicit, non-specific goals to reduce risky driving behaviors that result in a WBI course assignment, and the resulting increased driver accountability created by the WBI program. Practical application: Through the use of an automatically-assigned, targeted WBI program, fleets may have fewer crashes and insurance claims. This reduction in crashes and insurance claims may result in lower insurance premiums and may help to prevent injuries and save lives.

Assessing the impact of sleep time on truck driver performance using a recurrent event model.

Driver fatigue is a major safety concern for commercial truck drivers and is directly related to the total hours of sleep prior to a working shift. To evaluate changes in driving performance over a long on-duty driving period, we propose a mixed Poisson process recurrent-event model with time-varying coefficients. We use data from 96 commercial truck drivers whose trucks were instrumented with an advanced in situ data acquisition system. The driving performance is measured by unintentional lane deviation events, a known performance deterioration related to fatigue. Driver sleep time and other activities are extracted from a detailed activity register. The time-varying coefficients are used to model the baseline intensity and difference among three cohorts of shifts in which the driver slept less than 7 hours, between 7 to 9 hours, and more than 9 hours prior to driving. We use the penalized B-splines approach to model the time-varying coefficients and an expectation-maximization algorithm with embedded penalized quasi-likelihood approximation for parameter estimation. Simulation studies show that the proposed model fits low and high event rate data well. The results show a significantly higher intensity after 8 hours of on-duty driving for shifts with less than 7 hours of sleep prior to work. The study also shows drivers tend to self-adjust sleep duration, total driving hours, and breaks. This study provides crucial insight into the impact of sleep time on driving performance for commercial truck drivers and highlights the on-road safety implications of insufficient sleep and breaks while driving.

FAST DASH: Program overview and key findings from initial technology evaluations.

One focus of the U.S. Federal Motor Carrier Safety Administration (FMCSA) is to provide leadership in the testing and evaluation of promising safety technologies developed for use in commercial motor vehicles (CMVs). To this end, a program was developed by FMCSA to conduct independent, short-turnaround evaluations of promising safety technologies. Vendors who had promising safety technologies, focused in the commercial vehicle domain, were solicited to participate and submit an application. One technology was selected by FMCSA for each evaluation cycle (lasting approximately 18 months). The technology was tested in both static and dynamic conditions, after which a trucking fleet, and its drivers, were brought in to test the technology in a field operational test (FOT) lasting approximately 6 weeks. During the FOT, 15-20 trucks were instrumented with the technology and other data collection equipment, including sensors and video cameras. A study was then conducted during which drivers used the technology in their revenue-producing operations. Initially, often for the first 2 months, the technology collected data but did but not actively present alerts to the driver. Following this baseline period, a four-month intervention period was conducted. Each evaluation has resulted in more than 1,000,000 km of driving data including continuous video data. Data analyses focused on understanding the efficacy of the technology in terms of (i) safety improvements, (ii) challenges to implementation (e.g., unintended consequences), and (iii) user acceptance (including driver, fleet manager, and other fleet personnel as appropriate). The technology vendors who applied for the first three evaluations can be classified into the following general categories: fatigue/drowsiness, fleet management, visibility safety systems, cell phone policy/enforcement, and other systems. Three technology evaluations were completed in the first 5 years of (i) a blind spot detection and warning system, (ii) an onboard monitoring system, and (iii) a novel mirror technology. High-level results of each of these three evaluations are highlighted in the paper.

Data and methods for studying commercial motor vehicle driver fatigue, highway safety and long-term driver health.

This article summarizes the recommendations on data and methodology issues for studying commercial motor vehicle driver fatigue of a National Academies of Sciences, Engineering, and Medicine study. A framework is provided that identifies the various factors affecting driver fatigue and relating driver fatigue to crash risk and long-term driver health. The relevant factors include characteristics of the driver, vehicle, carrier and environment. Limitations of existing data are considered and potential sources of additional data described. Statistical methods that can be used to improve understanding of the relevant relationships from observational data are also described. The recommendations for enhanced data collection and the use of modern statistical methods for causal inference have the potential to enhance our understanding of the relationship of fatigue to highway safety and to long-term driver health.

The impact of driver distraction in tractor-trailers and motorcoach buses.

Driver distraction has become an increasing concern over the last decade as portable technology has emerged and its presence while driving has become more common. Driver distraction occurs when inattention leads to a delay in recognition of information necessary to accomplish the driving task. Two recent studies were conducted using a naturalistic data collection method and analysis of driver distraction. The Commercial Motor Vehicle Driver Distraction study (Olson et al., 2009) was conducted using heavy truck data, and the Distraction and Drowsiness in Motorcoach Drivers study (Hammond et al., 2016) was conducted using motorcoach data. Data were collected continuously every time the instrumented vehicle was turned on and in motion. Data were reduced to identify safety-critical events such as crashes, near-crashes, crash-relevant conflicts, and unintentional lane deviations. Results show that 40% of truck crashes and 56% of motorcoach crashes had some kind of distracting behavior. Odds ratios were calculated on individual secondary tasks and analyses of variance (ANOVAs) were calculated on eye-glance data to determine the effects of eyes off the forward roadway. Fewer distractions were identified in the motorcoach data, most notably the use of handheld cell phones. This suggests that the 2010 ban on handheld phones has had a positive effect on decreasing cell phone use while driving.

Prevalence of operator fatigue in winter maintenance operations.

Similar to commercial motor vehicle drivers, winter maintenance operators are likely to be at an increased risk of becoming fatigued while driving due to long, inconsistent shifts, environmental stressors, and limited opportunities for sleep. Despite this risk, there is little research concerning the prevalence of winter maintenance operator fatigue during winter emergencies. The purpose of this research was to investigate the prevalence, sources, and countermeasures of fatigue in winter maintenance operations. Questionnaires from 1043 winter maintenance operators and 453 managers were received from 29 Clear Road member states. Results confirmed that fatigue was prevalent in winter maintenance operations. Over 70% of the operators and managers believed that fatigue has a moderate to significant impact on winter maintenance operations. Approximately 75% of winter maintenance operators reported to at least sometimes drive while fatigued, and 96% of managers believed their winter maintenance operators drove while fatigued at least some of the time. Furthermore, winter maintenance operators and managers identified fatigue countermeasures and sources of fatigue related to winter maintenance equipment. However, the countermeasures believed to be the most effective at reducing fatigue during winter emergencies (i.e., naps) were underutilized. For example, winter maintenance operators reported to never use naps to eliminate fatigue. These results indicated winter maintenance operations are impacted by operator fatigue. These results support the increased need for research and effective countermeasures targeting winter maintenance operator fatigue.

Do the benefits outweigh the costs? Societal benefit-cost analysis of three large truck safety technologies.

Although research has found advanced safety technologies to be effective at preventing large truck crashes, limited empirical data exists regarding their cost effectiveness to the U.S. society. Without these data, carriers are hesitant to adopt costly technologies and government agencies are hesitant to create regulation mandating their use. The objective of this study was to provide scientifically-based estimates of the societal benefits and costs of large truck automatic emergency braking (AEB), lane departure warning (LDW), and video-based onboard safety monitoring (OSM). For each technology, benefit-cost analyses were performed for installing the technology on all large trucks (including retrofitting existing trucks) and for equipping new large trucks only. Sensitivity analyses examined three cost estimates (low, average, high; values technology-specific), two estimates of system efficacy (low and high; values technology-specific), and three discount rates (0%, 3%, 7%) for each technology. Equipping trucks with LDW and video-based OSM systems were found to be cost effective for all combinations of costs, efficacy, and discount rates examined, for both new and existing trucks. Results for AEB and were mixed. Only a $500 AEB system was cost effective when equipping new trucks and retrofitting existing trucks. However, all cost estimates were cost effective with a 28% efficacy rate when only equipping new large trucks. Overall, these data suggested all three technologies can be cost-effective for new large trucks provided the current costs and efficacy rates can be maintained or improved upon.

A synthetic approach to compare the large truck crash causation study and naturalistic driving data.

Truck crashes represent a significant problem on our nation's highways. There is a great opportunity to learn about crash causation by analyzing and comparing the Large Truck Crash Causation Study (LTCCS) and naturalistic driving (ND) data. These data sets provide in-depth information, but have contrasting strengths and weaknesses. The LTCCS contains information on high-severity crashes (crashes and fatal crashes), but relied on data collected during crash investigations. The LTCCS identified principal driver errors in the crash, such as the Critical Reason, but not detailed behaviors or scenario sequences. The ND data sets relate primarily to non-crashes that are detectable from dynamic vehicle events, such as hard braking, swerve, etc., provide direct video observations of the driver and the surrounding driving scene and precise information on driver inputs (kinematics) and captured events, and provide certain types of exposure data that cannot easily be obtained using crash reconstruction data. The ND data are collected continuously, thereby capturing both safety-critical events and normative driving (i.e., baseline). The current project evaluated large-truck crash data from the LTCCS and two large-truck ND data sets, the Naturalistic Truck Driving Study and the Drowsy Driver Warning System Field Operational Test. A synthetic risk ratio analysis on the associated factor, Following Too Closely, indicated that truck drivers in the LTCCS were 1.34 times more likely to be involved in a crash, than an ND crash-relevant conflict, if they were following too closely (i.e., tailgating). Given several caveats noted in the paper, this study suggests it's possible to use the ND data set to calculate the exposure of a given behavior and use the LTCCS data set to calculate the crash exposure to the same behavior.

A retrospective analysis of cardiometabolic health in a large cohort of truck drivers compared to the American working population.

BACKGROUND: Truck drivers face many health challenges, including lifestyle, diet, inactivity, stressors, and social support. METHODS: A repeated cross-sectional analysis compared 88,246 truck drivers with data from (n = 11 918) the National Health and Nutrition Examination Survey (NHANES). Trends over time for body mass index (BMI), high blood pressure, diabetes mellitus, heart disease, and sleep disorders were analyzed, adjusting for potential confounders. RESULTS: Truck drivers had significantly worsening health metrics between 2005 and 2012 compared to NHANES participants. Truck drivers were significantly more likely to be obese and morbidly obese with prevalence odds ratios (POR) and 95% confidence intervals (95%CI) of 1.99 (1.87, 2.13) and 2.34 (2.16, 2.54), respectively. Measured blood pressure, self-reported high blood pressure, diabetes mellitus, and heart disease all significantly increased. Also, sleep disorders increased among truck drivers from 2005 to 2012 (POR = 6.55, 95%CI 5.68, 7.55). CONCLUSION: These data suggest disproportionate increases in poor health among truck drivers from 2005 to 2012.

Benefit-cost analysis of lane departure warning and roll stability control in commercial vehicles.

INTRODUCTION: This paper presents the cost benefits of two different onboard safety systems (OSS) installed on trucks as they operated during normal revenue deliveries. Using a formal economic analysis approach, the study quantified the costs and benefits associated with lane departure warning (LDW) systems and roll stability control (RSC) systems. METHODS: The study used data collected from participating carriers (many of these crashes were not reported to state or Federal agencies), and the research team also reviewed each crash file to determine if the specific OSS would have mitigated or prevented the crash. The deployment of each OSS was anticipated to increase the safety of all road users, but impact different sectors of society in different ways. Benefits that were inherent in each group (e.g., industry, society) were considered, and different benefit-cost analyses (BCAs) were performed. RESULTS: This paper presents two BCAs: a BCA focused on the costs and benefits in the carrier industry by implementing each OSS, and a BCA that measured the societal benefits of each OSS. In addition, a BCA for a theoretical mandatory deployment option for each OSS is presented. CONCLUSIONS: BCA results for LDW and RSC clearly showed their benefits outweighed their costs for the carrier and society. Practical applications: Cost information is a crucial factor in purchasing decisions in carriers; similarly, regulators must consider the cost burden prior to mandating technologies. The results in this study provide carrier decision makers and regulators with information necessary to make an informed decision regarding RSC and LDW.

Multiple Conditions Increase Preventable Crash Risks Among Truck Drivers in a Cohort Study.

OBJECTIVE: This study aims to quantify the crash risk for truck drivers with multiple comorbid medical conditions, after adjusting for confounders. METHODS: This retrospective cohort of 38,184 drivers evaluated concomitant medical conditions and subsequent crash data between January 1, 2005, and October 31, 2012. Hazard ratios (HRs) and 95% confidence intervals (95% CIs) were calculated for any cause and preventable crashes of varying severity. RESULTS: Drivers with three or more medical conditions had a significantly increased risk of preventable Department of Transportation (DOT) reportable crashes (HR = 2.53, 95% CI = 1.65 to 3.88) and preventable crashes with injuries (HR = 2.23, 95% CI = 1.09 to 5.31) after adjustment for covariates. Similarly, adjusted HRs were 2.55 (95% CI = 1.37 to 4.73) for any cause DOT-reportable crashes and 3.21 (95% CI = 1.18 to 8.75) for any cause crashes with injuries. CONCLUSIONS: Having three concomitant medical conditions may be a statistically significant risk factor for preventable and any cause DOT-reportable crashes and crashes with injuries.

An observational study of the safety benefits of electronic logging devices using carrier-collected data.

OBJECTIVE: Fatigue has been shown to be a contributing factor in many large truck crashes. Long duty periods, irregular work schedules, and poor sleeping environments make fatigue a noteworthy concern in trucking operations. One way to limit fatigued driving is through prescriptive hours-of-service (HOS) regulations. This duty status information is typically recorded in written logs; however, more trucking companies are moving toward electronic HOS recorders. These devices were first marketed as productivity tools; however, more recently they have been touted for their safety benefits in reducing fatigued truck drivers (because falsifying electronic logs is difficult). METHODS: The purpose of the current study was to assess the benefits of electronic logging devices (ELDs) on safety and HOS violations in trucks as they operated during normal revenue service. Data on crashes, HOS violations, mileage, and onboard safety systems were obtained from participating motor carriers. Although the final data sets included data from 11 carriers representing medium and large carriers (including a total of 82,943 crashes, 970 HOS violations, and 224,034 truck-years that drove a total of 15.6 billion miles), the data set in the study was skewed toward larger, for-hire carriers and may not represent the overall U.S. trucking population. RESULTS: After controlling for calendar year, carriers in the data set, onboard safety system status, and long-haul/regional indicator, ELD-equipped trucks had a significantly lower total crash rate (11.7% reduction), preventable crash rate (5.1% reduction), driving-related HOS violation rate (53% reduction), and non-driving-related HOS violation rate (49% reduction) than trucks not equipped with ELDs. CONCLUSION: The results show a clear safety benefit, in terms of crash and HOS violation reductions, for trucks equipped with ELDs.

The influence of daily sleep patterns of commercial truck drivers on driving performance.

Fatigued 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.

Repeated Cross-Sectional Assessment of Commercial Truck Driver Health.

OBJECTIVE: To assess relationships and trends over time in individual conditions and multiple conditions among a large sample of independent, nonoverlapping truck drivers using a repeated cross-sectional study design. METHODS: Commercial driver medical examinations were conducted on 95,567 commercial drivers between January 1, 2005, and October 31, 2012. Specific medical conditions that have been identified by the Federal Motor Carrier Safety Administration's Medical Review Board as possibly increasing crash risk were examined. Prevalence and trends over time were analyzed. RESULTS: A total of 8 of the 13 conditions significantly increased from 2005 to 2012. Prevalence of multiple concomitant conditions also increased, with prevalence odds ratios as high as 7.39 (95% confidence interval, 3.92 to 13.98) for four or more conditions in 2012 as compared with 2005. CONCLUSIONS: Individual and multiple conditions thought to be associated with increased crash risk significantly increased between 2005 and 2012.

Drivers' visual behavior when using handheld and hands-free cell phones.

OBJECTIVE: This study investigated driver distraction and how the use of handheld (HH), portable hands-free (PHF), and integrated hands-free (IHF) cell phones affected the visual behavior of motor vehicle drivers. METHOD: A naturalistic driving study recorded 204 participating drivers using video cameras and vehicle sensors for an average of 31 days. A total of 1564 cell phone calls made and 844 text messages sent while driving were sampled and underwent a video review. Baselines were established by recording epochs prior to the cell phone interactions. Total eyes-off-road time (TEORT) was examined to assess the visual demands of cell phone subtasks while driving. Percent TEORT was reported and compared against the baseline. RESULTS: Visual-manual subtasks performed on HH, PHF, and IHF cell phones were found to significantly increase drivers' mean percent TEORT. In contrast, conversing on an HH cell phone was found to significantly decrease drivers' mean percent TEORT, indicating that drivers looked at the forward roadway more often. No significant differences in percent TEORT were found for drivers conversing using PHF or IHF cell phones. The mean TEORT durations for visual-manual subtasks performed on an HH cell phone were significantly longer than the mean TEORT durations on either IHF or PHF cell phones. PRACTICAL APPLICATIONS: This research helps to further reinforce the distinction made between handheld and hands-free cell phone use in transportation distraction policy.

Commercial Driver Medical Examinations: Prevalence of Obesity, Comorbidities, and Certification Outcomes.

OBJECTIVE: 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/m) and morbidly obese (26.6%, BMI >35.0 kg/m), 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.