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.

Investigating the impact of driving automation systems on distracted driving behaviors.

Driving automation systems (e.g., SAE Level 2) ultimately aim to enhance the comfort and safety of drivers. At present, these systems are able to control some portions of the driving task (e.g., braking, steering) for extended time periods, giving drivers the opportunity to disengage from the responsibilities associated with driving. In this study, data derived from two naturalistic driving studies involving automation-equipped vehicles were analyzed to evaluate driver behaviors with respect to driving automation system use, specifically distraction-related factors (i.e., secondary task engagement, eye-glance behavior, and drowsiness). The results indicate that when drivers had prior experience using driving automation systems, they were almost two times as likely to participate in distracted driving behaviors when the systems were active than during manual driving. Drivers with less experience and familiarity with driving automation systems were less likely to drive distracted when the systems were active; however, these drivers tended to be somewhat drowsy when driving with systems activated. The results provide important insights into different operational phases of driving automation system use (i.e., learning/unfamiliar vs experienced users), whereby experience results in overtrust and overreliance on the advanced technologies, which subsequently may negate some of the safety benefits of these systems. Thus, while the safety benefits of driving automation systems are evident, it is imperative to better understand the impact these advanced technologies may have on driver behavior and performance in order to evaluate and address any unintended consequences associated with system use.

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.

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.

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.

Comparing handheld and hands-free cell phone usage behaviors while driving.

OBJECTIVE: The goal of this study was to compare cell phone usage behaviors while driving across 3 types of cell phones: handheld (HH) cell phones, portable hands-free (PHF) cell phones, and integrated hands-free (IHF) cell phones. Naturalistic driving data were used to observe HH, PHF, and IHF usage behaviors in participants' own vehicles without any instructions or manipulations by researchers. METHODS: In addition to naturalistic driving data, drivers provided their personal cell phone call records. Calls during driving were sampled and observed in naturalistically collected video. Calls were reviewed to identify cell phone type used for, and duration of, cell phone subtasks, non-cell phone secondary tasks, and other use behaviors. Drivers in the study self-identified as HH, PHF, or IHF users if they reported using that cell phone type at least 50% of the time. However, each sampled call was classified as HH, PHF, or IHF if the talking/listening subtask was conducted using that cell phone type, without considering the driver's self-reported group. RESULTS: Drivers with PHF or IHF systems also used HH cell phones (IHF group used HH cell phone in 53.2% of the interactions, PHF group used HH cell phone for 55.5% of interactions). Talking/listening on a PHF phone or an IHF phone was significantly longer than talking/listening on an HH phone (P <.05). HH dialing was significantly longer in duration than PHF or IHF begin/answer tasks. End phone call task for HH phones was significantly longer in duration than the end phone call task for PHF and IHF phones. Of all the non-cell phone-related secondary tasks, eating or drinking was found to occur significantly more often during IHF subtasks (0.58%) than in HH subtasks (0.15%). Drivers observed to reach for their cell phone mostly kept their cell phone in the cup holder (36.3%) or in their seat or lap (29.0% of interactions); however, some observed locations may have required drivers to move out of position. CONCLUSIONS: Hands-free cell phone technologies reduce the duration of cell phone visual-manual tasks compared to handheld cell phones. However, drivers with hands-free cell phone technologies available to them still choose to use handheld cell phones to converse or complete cell phone visual-manual tasks for a noteworthy portion of interactions.

An analysis of driving and working hour on commercial motor vehicle driver safety using naturalistic data collection.

Current hours-of-service (HOS) regulations prescribe limits to commercial motor vehicle (CMV) drivers' operating hours. By using naturalistic-data-collection, researchers were able to assess activities performed in the 14-h workday and the relationship between safety-critical events (SCEs) and driving hours, work hours, and breaks. The data used in the analyses were collected in the Naturalistic Truck Driving Study and included 97 drivers and about 735,000 miles of continuous driving data. An assessment of the drivers' workday determined that, on average, drivers spent 66% of their shift driving, 23% in non-driving work, and 11% resting. Analyses evaluating the relationship between driving hours (i.e., driving only) and SCE risk found a time-on-task effect across hours, with no significant difference in safety outcomes between 11th driving hour and driving hours 8, 9 or 10. Analyses on work hours (i.e., driving in addition to non-driving work) found that risk of being involved in an SCE generally increased as work hours increased. This suggests that time-on-task effects may not be related to driving hours alone, but implies an interaction between driving hours and work hours: if a driver begins the day with several hours of non-driving work, followed by driving that goes deep into the 14-h workday, SCE risk was found to increase. Breaks from driving were found to be beneficial in reducing SCEs (during 1-h window after a break) and were effective in counteracting the negative effects of time-on-task.