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The purpose of this study was to identify, review, and document existing CMV driver H&W programs and to identify industry best practices. The study began with a thorough literature review to understand the common medical conditions found among CMV drivers and the health risks—both behavioral and environment—associated with driving a commercial vehicle, along with a review of existing risk factor intervention programs and H&W programs designed for commercial drivers. Next, the study team conducted phone interviews with fleet and industry representatives to document and detail their H&W programs and initiatives and to identify program metrics and reported outcomes. This report includes a discussion of key aspects of existing programs, program recruitment methods, health assessments and testing, health education and coaching, and follow-up and maintenance activities. Findings from this study will inform recommendations for a larger study to evaluate the effectiveness of an H&W program for motor carrier operations.

The Virginia Tech Transportation Institute (VTTI) collaborated with a leading third-party population sleep health provider, Fusion Health, and two for-hire carriers to perform an independent analysis of the safety benefits of two, carrier-implemented obstructive sleep apnea (OSA) programs for commercial motor vehicle (CMV) drivers (OSAFE). The research team obtained OSA diagnostic and treatment data from Fusion Health and safety and driver information from the participating carriers. The crash types assessed in the analysis included claims-only, on-road, and preventable crashes. To compare OSA-treated drivers to drivers without diagnosed OSA, a matched cohort approach was taken to include treated drivers and drivers without diagnosed OSA. Safety performance was investigated to explore crash rates before and during PAP treatment, crash rates by treatment adherence and average usage, and crash rates before and during treatment for drivers with a previous crash. The main findings from this study include the following: (1) high PAP adherence rates among OSA drivers, with more than 90% of drivers from both carriers meeting full-adherence requirements for PAP compliance; (2) support for the safety benefits of PAP treatment in CMV drivers who were OSA positive and demonstrated full adherence to treatment; (3) pronounced safety benefits for CMV drivers that experienced a crash prior to beginning PAP treatment; (4) a dose-response safety benefit of PAP use among drivers at Carrier A; and (5) for Carrier A drivers with full adherence to PAP, higher rates of claims-only and preventable crashes before and during treatment compared to similar control drivers. Given that fully compliant PAP treatment was associated with reductions in on-road and preventable crashes, carriers should focus on (1) identifying OSA drivers through effective screening programs, (2) providing PAP treatment, (3) monitoring PAP compliance, and (4) providing support to assist drivers in maintaining OSA compliance.

Large trucks are overrepresented in work zone crashes. Data from the Federal Motor Carrier Safety Administration’s Large Truck and Bus Crash Facts show that, in 2017, 30% of work zone fatal crashes and 12% of work zone injury crashes involved at least one large truck, which was more than double the percentage of all crashes that occurred outside of work zones, where 12% of fatal crashes and 5% of injury crashes involved at least one truck. This study used data from four major truck naturalistic driving studies to investigate the risk associated with a variety of work zone roadway, environmental, and safety features. The vast majority of the work zone observations occurred with no adverse weather conditions present, dry road conditions, straight roadway alignment, and light traffic, with roughly three-quarters of commercial motor vehicle (CMV) drivers wearing a seatbelt. The most common features observed in work zones were warning signs, barrel barriers, and lane closures, which were used in various combinations. Traffic cones were associated with an increase of safety-critical event (SCE) risk, likely as a result of their small size and being less visible to CMV drivers. Reflective signs, barrels, and concrete barriers, however, reduced the SCE risk for CMV drivers in work zones by 40%–60%. There was a small decrease in SCE risk associated with light traffic in a work zone compared to moderate and heavy traffic. There was also a nearly 3-times greater risk of a CMV driver being involved in an SCE in work zones on single-lane roadways versus four-lane roadways. Active work zones had nearly twice the SCE risk for CMV drivers compared to inactive work zones. In active work zones, non-driving-related distractions, internal distractions, and external distractions all resulted in an almost three-fold increase in SCE risk for CMV drivers. When planning smaller, more temporary work zones, it may be beneficial for worker and road user safety if planners employ larger, more visible safety barriers such as barrels and reflective signage.

When determining crash causation, crash databases emphasize a single, unitary critical reason (CR) as the primary proximal cause of a safety-critical event (SCE), which leaves no room for the specification of any other potential contributing factors to the crash/event genesis. This is despite the fact that it is well established in the transportation safety field that crash genesis typically involves a convergence of several factors. The aim of this study was to investigate the crash trifecta concept to determine if the convergence of multiple elements, rather than a single, unitary critical reason, has greater value in explaining the complexities of crash genesis. Specifically, the crash trifecta concept is defined as three separate, but converging, elements: 1. Unsafe pre-incident behavior or maneuver (e.g., speeding, tailgating, unsafe turn); 2. Transient driver inattention (which may be driving related, such as mirror use, or unrelated, such as reaching for an object); and 3. An unexpected traffic event (e.g., unexpected stopping by the vehicle ahead). The value of the crash trifecta concept and convergence concepts in crash causation is that these concepts provide a structure for understanding the complexities of crash genesis. Thus, the crash trifecta concept may help explain the differences between the genesis of a crash and lower-severity SCEs (e.g., near-crashes).

Automated vehicles (AVs) promise to revolutionize driving safety. Driver models can aid in achieving this promise by providing a tool for designers to ensure safe interactions between human drivers and AVs. In this project, we performed a literature review to identify important factors for AV takeover safety and promising models to capture these factors. We also conducted a driving simulation experiment to address a research gap in silent automation failures. Finally, we developed a series of models to predict driver decision-making, braking,and steering responses using crash/near-crash data from the SHRP 2 naturalistic driving study and a driving simulation experiment. The analyses highlight the importance of visual parameters (in particular, visual looming) in driver responses and interactions with AVs. The modeling analysis suggested that models based on visual looming captured driver responses better than traditional baseline reaction time and closed-loop models. Further,the analysis of SHRP 2 data showed that gaze eccentricity of the last glance plays a critical role in driver decision-making. With further development, including the integration of important factors in takeover performance identified in the literature review and refinement of the role of gaze eccentricity, these models could be a valuable tool for AV software designers.

Despite a plethora of research examining commercial motor vehicle (CMV) crash risk as a function of time of day, there are few studies that have included objective measures of exposure. The purpose of this study was to use carrier-owned crash and electronic logging device (ELD) data to assess CMV crash rates and, as a function of time of day, using the amount of driving time in each hour as a measure of exposure. This study used the recently completed the Hour-of-Service (HOS) Rules Impact Analysis (under agency review), which contained crash and driver duty status data from 11 carriers with 36,000 crashes and ELD data from over 134,000 drivers over 21,639,182 log-days. The dataset included carrier descriptive information, detailed crash variables, driver log variables, and driver information. Three analyses were performed: crash rate by hour of day, crash rate by daytime vs. nighttime period, and crash rate by morning rush hour, evening rush hour, and non-rush hour periods. Results showed that CMV crash rates per 1 million driving hours were highest at nighttime in the 9:00 p.m. hour, 11:00 p.m. hour, and between 2:00 a.m. and 7:00 a.m. This study also provided some explanation for the inconsistencies in previous results regarding the effect of time of day on CMV crash risk related to operational differences among carriers.

Although large truck advanced safety technologies such as automatic emergency braking, lane departure warning, and video-based onboard monitoring are becoming more prevalent, adoption rates and use are lagging due to concerns about return on investment (ROI). To address this, researchers at the Virginia Tech Transportation Institute (VTTI) developed an Excel-based ROI calculator and accompanying user manual. This report describes an extension of that original project: a web-based version of that calculator available at https://www.vtti.vt.edu/roicalculator/. The report describes the rationale behind the calculator and an provides an overview of the resources available on the website.

The Omnibus Transportation Employee Testing Act of 1991 requires all Department of Transportation (DOT) agencies to conduct drug and alcohol testing of safety-sensitive transportation employees, which includes commercial motor vehicle (CMV) drivers. This report surveys the research literature to outline the effects that alcohol and drugs have on CMV driving safety and provides an overview of the most common testing methods. Additionally, the report provides guidelines on maintaining a drug-free workplace in the CMV industry, covering best practices for drug and alcohol policy, employee training, employee assistance programs, drug and alcohol testing, and drug and alcohol program evaluation.

Many segments in the trucking industry experience extremely high rates of driver turnover. Although some research has shown a link between high driver turnover and increased crash risk, it is not known if voluntary turnover affects crash risk. The purpose of this study was to examine the relationship between voluntary and involuntary driver turnover with involvement in Federal Motor Carrier Safety Administration (FMCSA)-reportable crashes and moving violations. This study used data collected in the recently completed Commercial Driver Safety Risk Factors study, which examined individual driver risk factors using a sample of 21,000 drivers from a single, large, for-hire carrier. Poisson regression models were used to measure the relationship between safety outcome rate and the employment status of the drivers. Overall, drivers who had continuous employment were significantly less likely to be involved in a future FMCSA-reportable crash or receive a violation compared to drivers that left the carrier at any time. Furthermore, drivers that left the carrier without a recent crash were significantly less likely to be involved in an FMCSA-reportable injury crash compared to drivers that left the carrier following a recent crash. These results support the need for carriers to adopt programs and policies designed to encourage safe drivers to remain at the same carrier and thus help to realize lower crash rates.

This study documents innovative and successful practices for pre-employment screening in the commercial motor vehicle (CMV) industry and assesses the prevalence and effectiveness of these pre-employment screening practices in a sample of CMV operations. A 47-item Web-based survey queried participating carriers’ (i) use of various pre-employment screening practices, (ii) effectiveness of pre-employment screening practices, (iii) reasons why pre-employment screening practices are used or unused, and (iv) descriptive data on the participating carrier (fleet size, operation type, etc.). The 40 survey respondents included carriers of different sizes and operation types but should be viewed as a convenience sample and not reflective of the overall CMV population. Based on the results, effective screening techniques in order of their effectiveness ratings from survey respondents include performance or skills testing, background checks, personality testing, medical examination, physical ability testing, and social media screening.

There are many situations where tacit communication between drivers and pedestrians governs and enhances safety. The goal of this study was to formalize this communication and apply it to the driving strategy of an autonomous vehicle. Toward this, we performed a field study of the interaction between drivers and pedestrians. Vehicles were instrumented to capture behavioral information on a driver as well as passengers and the traffic scenario in general. The data captured were reduced by data analysts to provide insights into the communication and driving patterns. The categorical reduction on driver, pedestrian, and environmental variables was captured. A domain specific language (DSL) was developed to precisely describe the driver-pedestrian behavior, toward the development of a behavioral model for generating autonomous vehicle controls. Specifically, interaction was formalized through a probabilistic model, namely a partially observable Markov decision process (POMDP). This enabled study of what-if scenarios with different risk averseness characteristics. One particular strategy was implemented on an autonomous vehicle and experimental observations were made. Future work will consider (i) richer DSLs to better quantify the driver-human communication, (ii) faster POMDP solvers for real-time operation, and (iii) further applications.

In 2015, large trucks were involved in 415,000 crashes in the United States that resulted in approximately 116,000 injuries and 4,067 fatalities. One way to reduce these crashes and their resulting injuries and fatalities is through the adoption of advanced safety technologies (ASTs) such as automatic emergency braking, lane departure warning, and video-based onboard safety monitoring. Despite studies showing that ASTs effectively prevent or mitigate crashes, commercial motor vehicle (CMV) carriers often lack data on their associated return-on-investment (ROI). This project offers CMV carriers a research-based ROI calculator in the form of an Excel spreadsheet with an accompanying user guide.

The commercial motor vehicle (CMV) industry comprises a largely aging workforce, which adds to a widely held concern about a growing CMV driver shortage. As CMV drivers age and retire, there are fewer workers to step in and fill the gap. A possible solution to this problem is to recruit and hire younger drivers, although this poses a potential safety risk due to a lack of CMV driving experience among the younger driver population. However, it is largely unknown in the CMV industry what impact age has on driver risk independent of CMV driving experience, and vice versa. Thus, this study used data collected and compiled in a study sponsored by the Federal Motor Carrier Safety Administration (FMCSA), Commercial Driver Safety Risk Factors (Hickman et al., under Agency review), from more than 9,000 CMV drivers to determine the impact of age and CMV driving experience on crash rates, crash involvement, and moving violations. The results indicate that, while both age and CMV driving experience play a role in driver risk, CMV driving experience is more important than age when considering risk. This may be especially true for older inexperienced CMV drivers (e.g., over 55 years of age with less than 1 year of CMV driving experience), who had higher crash rates and odds of being involved in a crash than their younger, inexperienced counterparts. Generally speaking, the first year of driving a CMV is riskier in terms of crash rates, crash involvement, and moving violations, regardless of age. Thus, motor carriers may want to focus on driver training, including engaging older, experienced drivers in driver mentoring programs to share their knowledge with inexperienced CMV drivers. In addition, there are vehicle technologies that use dash cameras to help fleet managers improve driver safety, such as the Lytx DriveCam system. These cameras continually record video and provide evidence-based opportunities for driver training, which may provide additional benefits.

Although several studies have estimated the prevalence of obstructive sleep apnea (OSA) in the commercial motor vehicle (CMV) driver population, limitations such as small sample sizes and study samples that are not representative of the STOP-Bang is a subjective and objective screening tool that assesses snoring, tiredness, observed apneas, hypertension, Body Mass Index (BMI), age, neck circumference, and gender. This study investigated the potential benefit of using a modified version of the STOP-Bang questionnaire to screen for OSA in the CMV population using data from the Commercial Driver Safety Risk Factors (CDSRF) study. Using the modified version of the STOP-Bang, 10 times more drivers (897 vs. 9,382) in the CDSRF study were classified as screening high for OSA (i.e., OSA Potential). Although these drivers moved from the No OSA group to the Potential OSA group, analyses using Poisson regression models still found that the OSA Diagnosed: Treated group had a significantly lower crash rate compared to the No OSA group. The efficacy of the STOP-Bang in screening for OSA in this population should be further evaluated.

Although previous research has documented safety improvement strategies, many carriers may not have real-world data and/or information on the strategies that other carriers have successfully used to improve safety. This study identified strategies that CMV fleets have successfully used to improve their safety outcomes. The research team worked with CMV insurers and state trucking associations to identify nine CMV carriers that successfully improved safety outcomes. Safety improvements involved Federal Motor Carrier Safety Administration-reportable crashes, less severe crashes, insurance claims, and/or Compliance, Safety, and Accountability (CSA) Behavior Analysis and Safety Improvement Category (BASIC) scores. After verifying these improved safety outcomes, the research team recruited the nine carriers to participate in an hour-long interview to document the strategies they used. Successful strategies included comprehensive driver training, collision mitigation technologies, onboard safety monitoring, strict hiring criteria, and developing a strong safety culture.

According to the National Academies of Science, Engineering, and Medicine, fatigue plays a role in up to 20% of fatal commercial motor vehicle (CMV) crashes. Because fatigue is difficult to self-assess, the use of fatigue detection technologies is growing in the CMV industry. The Virginia Tech Transportation Institute (VTTI) conducted a comprehensive literature review to identify the types of fatigue detection technologies that are commercially available and detail their respective features for application in CMV operations. The literature review discusses technologies that (i) detect fatigue using physiological sensors from the driver; (ii) use computer vision to monitor driver behavior and positioning; (iii) record and monitor driver performance metrics, and (iv) combine multiple measures in a hybrid approach to monitoring and detection. Informed by the technologies discussed in the literature review, the VTTI research team then developed an inventory of fatigue detection technologies that are currently commercially available for implementation in CMV operations. The inventory details their specific features, capabilities, limitations, applications, and efficacy (where available). Availability, cost, practicality for the industry, and stage of development for testing or implementation are also detailed for each technology. Finally, each technology is categorized, taking into consideration factors such as empirical validation, effectiveness, practicality, and availability.

The crash trifecta model does not consider crash genesis as a simple unitary element, but rather as a convergence of three separate, converging elements: (1) unsafe pre-incident behavior or maneuver; (2) transient driver inattention; and (3) an unexpected traffic event. Previous results from Phase I of the Crash Trifecta study showed that the presence of all three crash trifecta elements increased as the severity of a safety-critical event (SCE) increased. Given the limited number of crashes available in Phase I, however, it was not possible to identify trends in the presence of specific crash trifecta elements or to break the data down by incident type or crash severity. The current study built on the methods and results from Phase I by applying the crash trifecta model to the Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study (NDS), which greatly increased the number of SCEs available for analysis. The results of Phase II show that elements well within a driver’s control are at the core of the majority of SCEs. Unsafe driving behavior was the most prevalent crash trifecta element, occurring in 70% of crashes and 52% of near-crashes. Unsafe driving behavior combined with transient inattention contributed to over 25% of crashes and almost 33% of at-fault crashes in the current study, compared to 5% of near-crashes and 8% of at-fault near-crashes, indicating that a crash is much more likely to occur if the unsafe driver is also not paying attention. The prevalence of the remaining two crash trifecta elements (i.e., transient inattention and unexpected event) varied depending on the severity of the SCE. An unexpected event was more likely to be present in near-crashes (74%) compared to crashes (25%), while the opposite was true for transient inattention near-crashes (28%) and crashes (43%). The increased number of SCEs in Phase II compared Phase I meant that the data set could be broken down by incident type for a more in-depth assessment of the applicability of the crash trifecta model. Of the 16 different incident types, the most common crashes were animal related, rear end (striking), rear end (struck), and road departure (left or right). The most common near-crashes were animal related, rear end (striking), sideswipe (same direction), and turn into path (same direction). The majority of different types of near-crashes tended to be associated with pedestrians, animals, pedalcyclists, or other vehicles behaving unexpectedly. The presence of transient inattention in a number of incident types resulted in a higher proportion of crashes than near-crashes. As was the case in Phase I, the results of the current Phase II study suggest that assigning a single, unitary critical reason as the proximal cause of the SCE without considering additional contributing factors is likely to be a limitation that does not address the complexities involved in the genesis of a crash.

Occupational drivers are a unique subset of the driving population, as driving per se is not their primary job duty, but can nonetheless encompass a large portion of their working day. These occupational drivers make sales calls to various clients and other business entities throughout the day, and often use fleet vehicles as a critical component of business. The purpose of this research was to conduct an analysis of naturalistic data to better understand the behaviors of occupational drivers. Naturalistic data were collected by Lytx, a driver risk management company, over a 3-month period. Supervisors in utilities and service organizations were the target vehicles in the analyses, as these employees had a driving profile that involved travel to site locations throughout the working day (e.g., they engaged in daily driving to various locations over the course of the day). The final data set contained 312,672 naturalistic driving safety-critical events (SCEs) and spurious baselines (BLs) reduced for driver behaviors and tasks. Overall, non-driving-related distraction tasks were uncommon in spurious BLs and SCEs (only 2.26% of spurious BLs and 2.51% of SCEs had at least one distraction task observed). The task most frequently observed in the spurious BLs and SCEs was any task using a handheld cell phone, a task that included several cell phone subtasks. Observations of drowsy or falling asleep behaviors were rare (less than 0.01% of the SCEs and spurious BLs had observable signs of drowsiness). However, the odds of driver drowsiness for an SCE that did not include a confidence interval of “1.0” were higher than for spurious BLs (odds ratio estimate = 6.59, 95% confidence interval = [3.21, 13.57]). Future research should include an analysis of risk, distraction occurrence, and driver drowsiness for different types of occupational drivers. Also, future research should investigate the impact of safety policies, practices, and culture on SCE occurrence.

Studies show that most commercial motor vehicle (CMV) crashes involve risky driving behaviors or errors on the part of the driver and thus are preventable. One method to reduce risky driving behaviors is through the use of onboard safety monitoring (OSM) technologies. OSM devices incorporate in-vehicle recording technology that can continuously measure and record the driver’s performance. However, the implementation of OSM systems involves more than installing the technology in vehicles. It requires detailed planning and involvement from all levels within a fleet. Furthermore, criticism and resistance can be expected when implementing an OSM device. The purpose of this project is to provide a manual for use by fleet management personnel prior to implementing an OSM technology (or for carriers that have implemented an OSM device but who are having problems achieving results). This manual is a practical, easy-to-understand reference guide for implementing an OSM program. It includes an overview of safety culture, a step-by-step guide for implementing a behavior-based safety (BBS) program in conjunction with an OSM device, and provides a list of commercially available OSM systems.

At the heart of traffic safety is the identification of factors that lead to crashes. With this knowledge, interventions can be developed to mitigate or prevent these factors from occurring in the future. Post hoc reconstructions of crashes (e.g., the Large Truck Crash Causation Study; LTCCS) and naturalistic driving studies have provided information on crash genesis. However, there is another source of driving data that is currently untapped. Today, numerous commercial vehicle fleets, transit fleets, and personal vehicles use onboard safety monitoring (OBSM) systems to monitor and improve driving behavior. Data from these video-based OBSM systems could be used by researchers to learn more about crash genesis and address some of the limitations inherent in post-crash reconstruction. This study created a data directory of common data elements in the LTCCS, commercially available, video-based OBSM systems, and other public sources to be used together to provide researchers with more valid and reliable information on crash genesis. Researchers used the LTCCS codebook as the structure of the new data directory. The LTCCS variables were analyzed by a trained researcher, who determined whether the variable could be collected with an OBSM system or through related information by using one of three responses: yes, no, and maybe. Lytx™ and SmartDrive were used as comparison OBSM system vendors to determine the kind and types of data they can capture. Other related sources such as Police Accident Reports (PARs) were used as possible sources of information for each variable when relevant. If a variable was labeled as “MAYBE,” the conditions under which the variable could be captured by a video-based OBSM system or through related information were outlined. Analysis determined that approximately half of all 802 variables in the LTCCS codebook could be captured using video-based OBSM systems and related information. In addition, another almost 30% of the variables were labeled as “MAYBE.” An analysis of the data collected in the actual LTCCS revealed that only 75% of the crashes had data and 48% of the variables had the option to be coded as “unknown.” Based on the results in the current report, it appears that the use of OBSM systems and other data sources could yield a similar amount of data as that obtained by data analysts in the LTCCS.

The purpose of this project was to quantitatively identify detention times in the commercial motor vehicle (CMV) industry. Although there is currently no standard definition, the industry commonly defines detention time as “any time drivers have to wait beyond 2 hours, which is the average time it takes to load or unload their cargo." Results indicated that drivers experienced detention time on approximately 1 in every 10 stops for an average duration of 1.4 hours. This represents the length of time the driver was detained beyond 2 hours; thus, he/she was loading/unloading at that delivery location for 3.4 hours in total. Medium-sized carriers (51-500 power units) had similar average detention times as large carriers (more than 500 power units); however, they experienced driver detention about twice as often as large carriers. For example, 19 percent of stops made by medium-sized carriers were accompanied by detention time compared to 9 percent of stops made by large carriers. The calculation of odds ratios (ORs) provided similar results for medium-sized carriers when compared to large carriers. The odds of a driver being detained were 2.17 times greater for medium-sized carriers than for large carriers. Operation type did not have much impact on the average length of detention time; however, operation type influenced how frequently drivers experienced detention time, with for-hire truck load (TL) carriers experiencing detention time more than twice as frequently as for-hire less-than-truckload (LTL) carriers and four times more often than private carriers. The OR analysis also indicated that for-hire TL carriers were worse off than for-hire LTL or private carriers. The odds of a driver being detained were nearly 5 times greater for for-hire TL carriers than for private carriers and 2.6 times greater than for for-hire LTL carriers. The odds of a driver being detained were 6.3 and 1.9 times greater for temperature controlled freight carriers than for dry bulk carriers, and liquid bulk/tank freight carriers, respectively. United States. Federal Motor Carrier Safety Administration