VTechWorks Repository :: Browsing by Author "Soccolich, Susan A."

Browsing by Author "Soccolich, Susan A."

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AI Dash Cam Performance Benchmark Testing
Camden, Matthew C.; Soccolich, Susan A.; Ridgeway, Christie; Parks, R. Lucas; Hanowski, Richard J. (2023-06-30)
The objective of this project was to benchmark the performance of three driver monitoring systems (DMSs): Motive DC-54, Samsara HW-CM32, and Lytx DriveCam SF400. The study was conducted in two phases. Phase One was an assessment to benchmark the performance of the three DMSs. This phase involved testing the ability of each system to successfully capture and alert unsafe driver behavior. Phase Two of the study, the user performance benchmarking phase, acquired feedback from heavy vehicle drivers regarding various attributes of each system’s quality. One hundred eighty-eight (188) CMV drivers with an active commercial driver’s license (CDL-A) participated in the survey.
Applying the Crash Trifecta Approach to SHRP 2 Data
Dunn, Naomi J.; Hickman, Jeffrey S.; Soccolich, Susan A.; Hanowski, Richard J. (National Surface Transportation Safety Center for Excellence, 2018-04-06)
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.
Assessing Factors Leading to Commercial Driver Seat Belt Non-Compliance
Camden, Matthew C.; Soccolich, Susan A.; McSherry, Thomas; Ridgeway, Christie; Stapleton, Steven (National Surface Transportation Safety Center for Excellence, 2024-10-24)
The current research study utilized a literature review and analysis of two data sources to determine situational factors associated with reduced seat belt usage among CMV drivers. The literature review identified characteristics of seat belt use, reasons drivers may or may not use seat belts, methods to improve seat belt use rates, and important gaps in the literature. The data analysis used data collected in two separate studies to assess seat belt use rates and explore the relationship between seat belt use and environmental, roadway, vehicle, and driver factors. The first study collected observational data in 2015 from multiple sites in Michigan with high rates of truck/bus-involved crashes. The second study collected naturalistic driving data during the Federal Motor Carrier Safety Administration’s Advanced System Testing Utilizing a Data Acquisition System on Highways (FAST DASH) second Safety Technology Evaluation Project (commonly referred to as FAST DASH 2). The naturalistic driving data set included safety-critical events (SCEs), which were reduced for driver behaviors and environmental and roadway information. In the current study, driver seat belt use was observed in 93% of the FAST DASH 2 naturalistic driving SCEs and in 81% of SCEs in the observational data set. The analysis of observational and FAST DASH 2 naturalistic driving study data identified several factors where seat belt use patterns changed significantly across the factor levels; however, the analyses for each data set did not show consistency in statistical significance. The observational data showed seat belt use to be associated with day of week, time of day, road type, truck type, and fleet type. Little correlation was found between seat belt use and other driver behaviors. The analysis of observational study data did find seat belt use to be significantly higher in observations where drivers were using a hands-free cell phone with earpiece compared to drivers not using a cell phone or talking on a handheld cell phone. The naturalistic driving data showed that drivers operating on divided highways had higher seat belt use compared to those driving on non-physically divided roadways.
Behavior-based Predictive Safety Analytics – Pilot Study
Engström, Johan; Miller, Andrew M.; Huang, Wenyan; Soccolich, Susan A.; Machiani, Sahar Ghanipoor; Jahangiri, Arash; Dreger, Felix; de Winter, Joost (SAFE-D: Safety Through Disruption National University Transportation Center, 2019-04)
This report gives an overview of the main findings from the Behavior-based Predictive Safety Analytics – Pilot Study project. The main objective of the project was to investigate the possibilities of developing statistical models predicting individual driver crash involvement based on individual driving style, demographic and behavioral history variables, using large sets of naturalistic driving data. The project was designed as a pilot project with the objective of providing the basis for a future more comprehensive research effort. Based on Second Strategic Highway Research Program (SHRP2) data, a subset of behavior and crash data including 2,458 drivers was created for analysis. The data were analyzed to investigate to what extent these drivers were differentially involved in crashes and near crashes, to what extent this was associated with individual characteristics, and if it is possible to predict individual drivers’ crash and near crash involvement based on variables representing individual characteristics. The results clearly demonstrated the presence of differential crash and near crash involvement and showed significant associations between enduring personal factors and crash involvement. Moreover, logistic regression and random forest classifiers were relatively successful in predicting crash and near crash involvement based on individual characteristics, but the ability to specifically predict involvement in crashes was more limited.
A Case Study Approach to Understand Heavy Truck Safety-critical Events in Work Zones
Dunn, Naomi; Soccolich, Susan A.; Hickman, Jeffrey S. (National Surface Transportation Safety Center for Excellence, 2022-08)
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.
Challenges in Conducting Empirical Epidemiological Research with Truck and Bus Drivers in Diverse Settings in North America
Soccolich, Susan A.; Ridgeway, Christie; Mabry, J. Erin; Camden, Matthew C.; Miller, Andrew M.; Iridiastadi, Hardianto; Hanowski, Richard J. (MDPI, 2022-09-30)
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.
Commercial Motor Vehicle Crash Risk by Time of Day
Camden, Matthew C.; Soccolich, Susan A.; Hickman, Jeffrey S.; Rossi-Alvarez, Alexandria; Hanowski, Richard J. (National Surface Transportation Safety Center for Excellence, 2020-11-16)
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.
Commercial Motor Vehicle Driver Risk Based on Age and Driving Experience
Dunn, Naomi J.; Soccolich, Susan A.; Hickman, Jeffrey S. (National Surface Transportation Safety Center for Excellence, 2020-04-17)
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.
Driver Detention Times in Commercial Motor Vehicle Operations
Dunn, Naomi J.; Hickman, Jeffrey S.; Soccolich, Susan A.; Hanowski, Richard J. (United States. Federal Motor Carrier Safety Administration. Office of Analysis, Research, and Technology, 2014-12)
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.
Driver Visual Behavior While Using Adaptive Cruise Control on Commercial Motor Vehicles
Grove, Kevin; Soccolich, Susan A.; Hanowski, Richard J. (National Surface Transportation Safety Center for Excellence, 2019-03-25)
This study compared whether commercial motor vehicle drivers spent less time looking at the roadway while cruise control was engaged. The trucks in the study were equipped with commercially available systems that provide adaptive cruise control (ACC), which uses radar to regulate headway in addition to speed when following a lead vehicle. Three metrics were analyzed to assess drivers’ eye-glance behavior during periods of traditional cruise control usage, full ACC usage, and manual car-following: total eyes-off-road time (TEORT), durations of glances off-road, and number of glances off-road. Drivers were observed to spend less time looking at the forward roadway when cruise control was engaged. Drivers were observed to spend less time looking at the roadway when ACC was engaged compared to when manually following a lead vehicle. This difference appears to be due to the truck drivers taking longer glances away from the roadway rather than taking more frequent glances away from the roadway. These differences are important for system designers to consider, as drivers are expected to maintain their attention on the roadway while using driver assistance technologies.
Estimating the Prevalence of Synthetic Cannabinoid Use Among Commercial Motor Vehicle Drivers: Developing a Pilot Test to Collect Data on Substance Use
Soccolich, Susan A.; Camden, Matthew C.; Glenn, T. Laurel; Link-Owens, Christine; Hall, Anne; Hodge, Julie; Hanowski, Richard J. (National Surface Transportation Safety Center for Excellence, 2022-08-19)
The extent to which commercial motor vehicle (CMV) drivers are using synthetic cannabinoids (SCs) and the magnitude of SC-impaired driving remains unclear. This study was the first of its kind to specifically pilot test methods to collect SC use data in the CMV driver population. The objectives of this study were to (1) develop an effective method for estimating the prevalence of synthetic substances/designer drugs in CMV drivers and (2) establish preliminary prevalence data on alcohol, synthetics, illicit drugs, prescription medications, and over-the-counter drugs among CMV drivers. Data were collected from an initial focus group followed by anonymous questionnaire and drug test. Eligible participants in both study portions needed to have a valid Class A commercial driver’s license, be currently employed as a CMV driver, and read and speak English comfortably. The drug history questionnaire included data from 206 drivers. The most reported substance was tobacco, with 62 drivers reporting use in the past year (32.80%). The following substances were not reported as used within the past year by any of the participating drivers: benzodiazepines, barbiturates, heroin, ketamine, LSD, PCP, Rohypnol, and SCs. Urine samples were tested for 84 substances. The urine test data included 202 drivers. Of these samples, 35 included at least one positive result (17.33%), 165 had no positive results (81.68%), and two tests had been diluted (0.99%). There were 18 substances found within the urine samples. The total number of positive results for all drivers and substances was 46, as drivers may have had multiple substances with a positive result. Alcohol was detected in 3.96% of driver samples. THC was also found in 3.96% of driver samples. Citalopram, an SSRI, was detected in nearly 3% of driver samples. Three positive results in the urine test showed the presence of opiates oxycodone, hydrocodone, or hydromorphone above the relevant cutoff levels. No driver samples were found to have detectable levels of SCs. This may be because detecting SC use through urine tests can be difficult as SC compositions and ingredients evolve frequently. Overall, the results showed that anonymous data collection is possible and rates of positive drug use are higher than previously identified through standard driver drug testing. Although the results from the pilot test are promising, it is important to consider that driver participation was voluntary. Thus, it is possible that the sample was biased towards drivers who did not use any medications, illegal substances, or SCs.
Evaluating the Sleeper Berth Provision: Investigating Usage Characteristics and Safety-Critical Event Involvement
Soccolich, Susan A.; Blanco, Myra; Hanowski, Richard J. (National Surface Transportation Safety Center for Excellence, 2015-07-20)
Hours-of-service (HOS) regulations control the maximum daily drive time, workday hours, and work week (period) hours for commercial motor vehicle (CMV) drivers. The regulations also include periods of off-duty time that drivers must take before beginning a work shift, referred to as shift-restart methods in this study. In the 2005 regulations, the shift-restart methods included taking at least 10 consecutive hours off duty or in the sleeper berth (10+ hour restart), taking at least 34 consecutive hours off duty or in the sleeper berth (34+ hour restart), and a sleeper berth provision (SBP). The SBP requires one period of at least 8 (but less than 10) consecutive hours spent in the sleeper berth plus a period of at least 2 (but less than 10) consecutive hours spent in the sleeper berth, off duty but not in the sleeper berth, or a combination of off-duty time spent in and out of the sleeper berth. The purpose of this project was to examine the usage of shift-restart methods and the relationship between shift-restart methods and driver safety performance in a naturalistically collected driving data set. The data used for this study were collected by the Virginia Tech Transportation Institute (VTTI) in the Naturalistic Truck Driving Study (NTDS) and developed into a hybrid data set of naturalistically collected video data and activity register data that accurately detail the participating CMV drivers’ driving and non-driving activities.(7) With the activity register data, researchers determined which restart method drivers used before beginning a new work shift: 10+ hour restart, 34+ hour restart, or the SBP. The proportion of shifts preceded by SBP breaks was significantly higher for drivers who reported taking medications regularly versus those who did not and also for drivers with longer average delivery distances. The number of years of CMV driving experience had a significant inverse relationship with the proportion of total shifts with SBP breaks. A mixed-effect negative binomial model with a logarithmic link function was used to model safety-critical event (SCE) rate at the shift level, controlling for the driver. The SCE rates in shifts following an SBP break were found not to be statistically different from those in shifts following 10+ hour or 34+ hour restart breaks. Odds ratios were also used to assess the risks associated with each of the three shift-restart methods. The 10+ hour restart and 34+ hour restart methods were found not to be significantly different. However, both the 10+ hour restart and 34+ hour restart methods were associated with significantly higher risk than the SBP. This project serves to enhance the understanding of the current HOS regulations and the impact that these regulations have on drivers, a topic of significant concern in the CMV community. Drivers have different preferred break usage patterns. The use of the SBP in the current study does not appear to be associated with a decrement in safety performance. Future efforts should look into how the usage of shift-restart methods has changed under the new regulations, which went into effect on July 1, 2013, and modified the driving limits, on-duty time limits, and rest break requirements.
Evaluating the Sleeper-Berth Provision: Preliminary Investigation into Usage Characteristics and Safety-Critical Event Involvement
Soccolich, Susan A.; Blanco, Myra; Hanowski, Richard J. (2014-08-25)
Evaluation of an In-vehicle Monitoring System Among an Oil and Gas Well Servicing Fleet
Krum, Andrew; Miller, Andrew M.; Soccolich, Susan A. (National Surface Transportation Safety Center for Excellence, 2020-05-01)
A pilot study of an in-vehicle monitoring system (IVMS) was conducted among a fleet of oil and gas well servicing vehicles. Data collected from the fleet were handled anonymously across 21 IVMS-instrumented light vehicle pickup trucks. Data were also collected on a sample of four participating drivers, one manager and three site workers, whose vehicles were instrumented with an IVMS and a miniature data acquisition system (MiniDAS). Among the 21 IVMS-instrumented trucks, there was a 60% reduction in speeding events and a 50% reduction in aggressive driving events. Questionnaires on the IVMS showed that drivers remained neutral to positive after the study was completed and rated the functionality of the IVMS positively. Analysis of the driving patterns of the four participants with MiniDAS-equipped vehicles showed long hours (average daily on-duty and commute time of 15.4 hours for the three site workers) and significant driving time on unimproved roads, which offer their own sets of hazards distinct from highway driving.
Examining the Relationship Between CMV Driver Retention and Safety
Camden, Matthew C.; Soccolich, Susan A.; Hickman, Jeffrey S.; Walker, Martin; Hanowski, Richard J. (National Surface Transportation Safety Center for Excellence, 2020-08-13)
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.
Examining the Relationship between Drug Use and Involvement in a Safety-Critical Event
Camden, Matthew C.; Soccolich, Susan A.; Hickman, Jeffrey S.; Hanowski, Richard J. (2014-08-25)
Fatigue and Distraction in Occupational Light Vehicle Drivers
Soccolich, Susan A.; Hickman, Jeffrey S.; Hanowski, Richard J. (National Surface Transportation Safety Center for Excellence, 2017-10-25)
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.
Identification of Cognitive Load in Naturalistic Driving
Angell, Linda; Perez, Miguel A.; Soccolich, Susan A. (National Surface Transportation Safety Center for Excellence, 2015-07-28)
Over the last decade, naturalistic driving studies have provided significant insight into issues pertaining to the roles of distraction, inattention, and drowsiness in crash risk. New techniques have been developed for coding naturalistic data, mining it, and analyzing it. The application of these techniques has begun to deepen our understanding of how different types of secondary tasks may vary in their effects on crash risk. However, one methodological gap that still remains in the field concerns techniques for identifying periods of cognitive load within streams of naturalistic driving data. Cognitive load is a critical element of current concerns about driver distraction, particularly with regards to intentional tasks of listening and conversing while driving, but also as related to spontaneously occurring processes like daydreaming and becoming lost in thought, which may also take place while driving. The research reported here was undertaken to develop a methodology for identifying epochs of cognitive activity occurring while driving by using indicators of cognitive load that are based on eye behavior. It furthermore set out to test the ability of these metrics, when used together as part of a model or algorithm, to discriminate pre-identified epochs of cognitive task load from “just driving” baselines drawn from naturalistic driving data. To accomplish these goals, the project mined an existing naturalistic driving database to extract the following types of driving epochs: those containing conversation, which was operationally defined as involving cognitive load; those containing visual-manual interaction; and those containing just driving. Data from these epochs were augmented with new measures related to three hypothesized cognitive load indicators. The measures which were explored as behavioral markers of cognitive load included the following and were derived from the acquired naturalistic data: (a) very long glances to the forward road, coupled with (b) increased concentration of glances to the forward road, (c) reduced breadth of active scanning, and (d) changes in blink rate relative to other types of task loads as well as just driving baselines. Results of mixed-model analyses confirmed that cognitive epochs corresponded with the hypothesized patterns on two of the four indicators. However, the analysis revealed a surprising finding on all metrics: the “just driving” baseline epochs behaved as if they, too, contained significant amounts of cognitive load, perhaps in the form of daydreaming or mind-wandering. This finding complicated the modeling effort, which, although completed, yielded inconclusive results. The logistic regression approach showed much promise as a technique, but the predictor variables in the final model were difficult to interpret, and again seemed consistent with the notion that the just driving baselines, which were intended to serve as comparison epochs, were instead similarly composed of cognitive load periods. These latter epochs’ cognitive loads were simply of a different type—namely, daydreaming, thinking, or mind-wandering activity. This finding, while unexpected, has several important implications, both for future efforts toward the development of algorithms used to identify types of task load from naturalistic data, and for the use of baselines in epidemiological risk estimation.
Identifying Equipment Factors Associated with Snowplow Operator Fatigue
Camden, Matthew C.; Hickman, Jeffrey S.; Soccolich, Susan A.; Hanowski, Richard J. (MDPI, 2019-09-01)
A recent body of research in fatigue management indicates that other factors, including in-cab and external equipment, contribute to operator fatigue. The goal of this project was to identify winter road maintenance equipment (in-cab and external) that may increase or mitigate snowplow operator fatigue. To accomplish this goal, questionnaires from 2011 snowplow operators were collected from 23 states in the U.S. Results confirmed previous research that fatigue is prevalent in winter road maintenance operations. Winter road maintenance equipment that produced excessive vibrations, noise, reduced visibility, and complex task demands were found to increase snowplow operators’ self-reported fatigue. Similarly, equipment that reduced vibrations and external noise, improved visibility, and limited secondary tasks were found to reduce snowplow operator’s self-reported fatigue. Based on the questionnaire responses and the feasibility of implementation, the following equipment may help to mitigate or prevent snowplow operator fatigue: dimmable interior lighting, LED bulbs for exterior lighting, dimmable warning lights, a CD player or satellite radio in each vehicle, heated windshield, snow deflectors, narrow-beam auxiliary lighting, and more ergonomically designed seats with vibration dampening/air-ride technology.
Identifying high-risk commercial truck drivers using a naturalistic approach
Soccolich, Susan A.; Hickman, Jeffrey S.; Hanowski, Richard J. (National Surface Transportation Safety Center for Excellence, 2011-06-30)
The 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.

Browsing by Author "Soccolich, Susan A."

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