Scholarly Works, Virginia Tech Transportation Institute

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  • An Econometric Analysis to Explore the Temporal Variability of the Factors Affecting Crash Severity Due to COVID-19
    Alrumaidhi, Mubarak; Rakha, Hesham A. (MDPI, 2024-02-01)
    This study utilizes multilevel ordinal logistic regression (M-OLR), an approach that accounts for spatial heterogeneity, to assess the dynamics of crash severity in Virginia, USA, over the years 2018 to 2023. This period was notably influenced by the COVID-19 pandemic and its associated stay-at-home orders, which significantly altered traffic behaviors and crash severity patterns. This study aims to evaluate the pandemic’s impact on crash severity and examine the consequent changes in driver behaviors. Despite a reduction in total crashes, a worrying increase in the proportion of severe injuries is observed, suggesting that less congested roads during the pandemic led to riskier driving behaviors, notably increased speed violations. This research also highlights heightened risks for vulnerable road users such as pedestrians, cyclists, and motorcyclists, with changes in transportation habits during the pandemic leading to more severe crashes involving these groups. Additionally, this study emphasizes the consistent influence of environmental and roadway features, like weather conditions and traffic signals, in determining crash outcomes. These findings offer vital insights for road safety policymakers and urban planners, indicating the necessity of adaptive road safety strategies in response to changing societal norms and behaviors. The research underscores the critical role of individual behaviors and mental states in traffic safety management and advocates for holistic approaches to ensure road safety in a rapidly evolving post-pandemic landscape.
  • Impacts of Vehicle-to-Everything Enabled Applications: Literature Review of Existing Studies
    Du, Jianhe; Ahn, Kyoungho; Farag, Mohamed; Rakha, Hesham A. (Universal Wiser Publisher, 2023-03-10)
    As communication technology is developing at a rapid pace, connected vehicles (CVs) can potentially enhance vehicle safety while reducing vehicle energy consumption and emissions via data sharing. Many researchers have attempted to quantify the impacts of such CV applications and vehicle-to-everything (V2X) communication, or the instant and accurate communication among vehicles, devices, pedestrians, infrastructure, network, cloud, and grid. Cellular V2X (C-V2X) has gained interest as an efficient method for this data sharing. In releases 14 and 15, C-V2X uses 4G LTE technology, and in release 16, it uses the latest 5G new radio (NR) technology. Among its benefits, C-V2X can function even with no network infrastructure coverage; in addition, C-V2X surpasses older technologies in terms of communication range, latency, and data rates. Highly efficient information interchange in a CV environment can provide timely data to enhance the transportation system's capacity, and it can support applications that improve vehicle safety and minimize negative impacts on the environment. Achieving the full benefits of CVs requires rigorous investigation into the effectiveness, strengths, and weaknesses of different CV applications. It also calls for deeper understanding of the communication protocols, results with different CV market penetration rates (MPRs), CV- and human-driven vehicle interactions, integration of multiple applications, and errors and latencies associated with data communication. This paper includes a review of existing literature on the safety, mobility, and environmental impacts of CV applications; gaps in current CV research; and recommended directions for future research. The results of this paper will help shape future research for CV applications to realize their full potential.
  • Rutting Performance Evaluation of BMD Surface Mixtures with Conventional and High RAP Contents under Full-Scale Accelerated Testing
    Tong, Bilin; Habbouche, Jhony; Flintsch, Gerardo W.; Diefenderfer, Brian K. (MDPI, 2023-12-12)
    The balanced mix design (BMD) constitutes a significant step forward in the pursuit of better-performing asphalt mixtures. This approach/framework offers increased innovative opportunities for the proper design and production of engineered asphalt mixtures without the need to strictly adhere to traditional volumetric requirements. The primary objective of this paper is to conduct a comprehensive investigation of the permanent deformation (rutting) behavior of surface mixtures (SMs) with conventional and high reclaimed asphalt pavement (HRAP) contents through full-scale accelerated testing under incremental loading conditions while accounting for the environmental aging effect. HRAP SMs were designed in this study, marking the initial application of Virginia Department of Transportation (VDOT) BMD special provisions, with attempts to incorporate 45% and even 60% RAP. Results showed that all BMD HRAP mixtures exhibited higher rut depths compared to the control mixture, which can be attributed to the inclusion of high binder contents aimed at enhancing cracking resistance. The asphalt pavement analyzer (APA) rut test and the stress sweep rutting tests were performed on mixtures sampled during production. Correlation analysis revealed significant and strong positive correlations between accelerated pavement testing (APT) and the multilevel laboratory rutting performance tests considered in this study. Finally, while acknowledging the limitations and all the assumptions considered in this study, the correlation analysis recommended refining the VDOT BMD APA rut depth threshold by lowering the current limit of 8 mm to 7 mm to ensure good performing mixtures from a rutting point of view.
  • The Impact of Line-of-Sight and Connected Vehicle Technology on Mitigating and Preventing Crash and Near-Crash Events
    Herbers, Eileen; Doerzaph, Zachary; Stowe, Loren (MDPI, 2024-01-12)
    Line-of-sight (LOS) sensors developed in newer vehicles have the potential to help avoid crash and near-crash scenarios with advanced driving-assistance systems; furthermore, connected vehicle technologies (CVT) also have a promising role in advancing vehicle safety. This study used crash and near-crash events from the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP2 NDS) to reconstruct crash events so that the applicable benefit of sensors in LOS systems and CVT can be compared. The benefits of CVT over LOS systems include additional reaction time before a predicted crash, as well as a lower deceleration value needed to prevent a crash. This work acts as a baseline effort to determine the potential safety benefits of CVT-enabled systems over LOS sensors alone.
  • Exploratory Development of Algorithms for Determining Driver Attention Status
    Herbers, Eileen; Miller, Marty; Neurauter, Luke; Walters, Jacob; Glaser, Daniel (SAGE, 2023-09)
    Objective: Varying driver distraction algorithms were developed using vehicle kinematics and driver gaze data obtained from a camera-based driver monitoring system (DMS). Background: Distracted driving characteristics can be difficult to accurately detect due to wide variation in driver behavior across driving environments. The growing availability of information about drivers and their involvement in the driving task increases the opportunity for accurately recognizing attention state. Method: A baseline for driver distraction levels was developed using a video feed of 24 separate drivers in varying naturalistic driving conditions. This initial assessment was used to develop four buffer-based algorithms that aimed to determine a driver's real-time attentiveness, via a variety of metrics and combinations thereof. Results: Of those tested, the optimal algorithm included ungrouped glance locations and speed. Notably, as an algorithm's performance of detecting very distracted drivers improved, its accuracy for correctly identifying attentive drivers decreased. Conclusion: At a minimum, drivers' gaze position and vehicle speed should be included when designing driver distraction algorithms to delineate between glance patterns observed at high and low speeds. Distraction algorithms should be designed with an understanding of their limitations, including instances in which they may fail to detect distracted drivers, or falsely notify attentive drivers. Application: This research adds to the body of knowledge related to driver distraction and contributes to available methods to potentially address and reduce occurrences. Machine learning algorithms can build on the data elements discussed to increase distraction detection accuracy using robust artificial intelligence.
  • WIP: The Feasibility of High-performance Message Authentication in Automotive Ethernet Networks
    Allen, Evan; Bowden, Zeb; Marchany, Randy; Ransbottom, J. Scot (2023-02-27)
    Modern vehicles are increasingly connected systems that expose a wide variety of security risks to their users. Message authentication prevents entire classes of these attacks, such as message spoofing and electronic control unit impersonation, but current in-vehicle networks do not include message authentication features. Latency and throughput requirements for vehicle traffic can be very stringent (<0.1 ms and >100 Mbps in cases), making it difficult to implement message authentication with cryptography due to the overheads required. This work investigates the feasibility of implementing cryptography-based message authentication in Automotive Ethernet networks that is fast enough to comply with these performance requirements. We find that it is infeasible to include Message Authentication Codes in all traffic without costly hardware accelerators and propose alternate approaches for future research.
  • What factors contribute to e-scooter crashes: A first look using a naturalistic riding approach
    White, Elizabeth; Guo, Feng; Han, Shu; Mollenhauer, Michael A.; Broaddus, Andrea; Sweeney, Ted; Robinson, Sarah; Novotny, Adam; Buehler, Ralph (Elsevier, 2023-06)
    Introduction: Shared dockless electric scooters (e-scooters) are a popular shared mobility service providing an accessible last-mile transportation option in urban and campus environments. However, city and campus stakeholders may hesitate to introduce these scooters due to safety concerns. While prior e-scooter safety studies have collected injury data from hospitals or riding data under controlled or naturalistic conditions, these datasets are limited and did not identify risk factors associated with e-scooter riding safety. To address this gap in e-scooter safety research, this study collected the largest naturalistic e-scooter dataset to date and quantified the safety risks associated with behavioral, infrastructure, and environmental factors. Method: A fleet of 200 e-scooters was deployed on Virginia Tech’s campus in Blacksburg, VA for a 6-month period. Fifty were equipped with a unique onboard data acquisition system, using sensors and video to capture e-scooter trips in their entirety. The resulting dataset consisted of 3,500 hours of data spanning over 8,500 trips. Algorithms were developed to identify safety critical events (SCEs) in the dataset and analyses were conducted to determine the prevalence of various SCE risk factors and associated odds ratios. Results: Results from this study indicate that infrastructure-related factors, behavior of e-scooter riders and other actors, and environmental factors all contributed to the SCE risk for e-scooter riders in Virginia Tech’s pedestrian-dense campus environment. Conclusions: To help mitigate unsafe rider behavior, educational outreach programs should quantify the significant risks associated with infrastructure, behavioral, and environmental risk factors and provide clear recommendations to riders. Improved infrastructure maintenance and design may also improve safety for e-scooter riders. Practical Applications: The infrastructure, behavioral, and environmental risk factors quantified in this study can be applied by e-scooter service providers, municipalities, and campus administrators to develop mitigation strategies to reduce the safety risks associated with e-scooter deployments in the future.
  • Comparative Analysis of Parametric and Non-Parametric Data-Driven Models to Predict Road Crash Severity among Elderly Drivers Using Synthetic Resampling Techniques
    Alrumaidhi, Mubarak; Farag, Mohamed M. G.; Rakha, Hesham A. (MDPI, 2023-06-21)
    As the global elderly population continues to rise, the risk of severe crashes among elderly drivers has become a pressing concern. This study presents a comprehensive examination of crash severity among this demographic, employing machine learning models and data gathered from Virginia, United States of America, between 2014 and 2021. The analysis integrates parametric models, namely logistic regression and linear discriminant analysis (LDA), as well as non-parametric models like random forest (RF) and extreme gradient boosting (XGBoost). Central to this study is the application of resampling techniques, specifically, random over-sampling examples (ROSE) and the synthetic minority over-sampling technique (SMOTE), to address the dataset’s inherent imbalance and enhance the models’ predictive performance. Our findings reveal that the inclusion of these resampling techniques significantly improves the predictive power of parametric models, notably increasing the true positive rate for severe crash prediction from 6% to 60% and boosting the geometric mean from 25% to 69% in logistic regression. Likewise, employing SMOTE resulted in a notable improvement in the non-parametric models’ performance, leading to a true positive rate increase from 8% to 36% in XGBoost. Moreover, the study established the superiority of parametric models over non-parametric counterparts when balanced resampling techniques are utilized. Beyond predictive modeling, the study delves into the effects of various contributing factors on crash severity, enhancing the understanding of how these factors influence elderly road safety. Ultimately, these findings underscore the immense potential of machine learning models in analyzing complex crash data, pinpointing factors that heighten crash severity, and informing targeted interventions to mitigate the risks of elderly driving.
  • Infrastructure-Based Performance Evaluation for Low-Speed Automated Vehicle (LSAV)
    Klauer, Charlie; Hong, Yubin; Mollenhauer, Mike; Vilela, Jean Paul Talledo (MDPI, 2023-05-05)
    This study assessed the limitations of the EasyMile EZ10 Gen 3 low-speed automated vehicle (LSAV) while operating on public roadways. The primary interest was to evaluate the infrastructure elements that posed the greatest challenges for the LSAV. A route was chosen that would satisfy a legitimate transit need. This route included more operational complexity and higher traffic volumes than a typical EasyMile LSAV deployment. The results indicate that the LSAV operated at a lower-than-expected speed (6 to 8 mph), with a high frequency of disengagements, and a regular need for safety operator intervention. Four-way stop-sign controlled intersections, three-lane roads with a shared turning lane in the middle, open areas, and areas without clear markings were the most challenging for the LSAV. Some important considerations include the need to have LSAVs operate on roadways where other vehicles may pass more safely, or on streets with slower posted speed limits. Additionally, the low passenger capacity and inability to understand where passengers are located onboard make it hard for the LSAV to replace bus transits. Currently, the LSAV is best suited to provide first/last-mile services, short routes within a controlled access area, and fill in gaps in conventional transits.
  • The Surface Accelerations Reference— A Large-Scale, Interactive Catalog of Passenger Vehicle Accelerations
    Ali, Gibran; McLaughlin, Shane; Ahmadian, Mehdi (IEEE, 2023-04)
    There is a need for a large-scale, real world, diverse, and context rich vehicle acceleration catalog that can be used to design, analyze, and compare various intelligent transportation systems. This paper fulfills three primary objectives. First, it provides such a catalog through the Surface Accelerations Reference, which is openly available as an interactive analytics tool as well as an open and downloadable dataset. The Surface Accelerations Reference statistically describes the driving profiles of about 3,500 individuals contributing 34 million miles of continuous driving data collected in the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS). These profiles were created by summarizing billions of longitudinal and lateral acceleration epochs experienced by the participants. Second, this paper introduces a standardized methodology for creating such a catalog so that similar acceleration profiles can be produced for other human cohorts or automated driving systems. Finally, the data are used to analyze the effect of roadway speed category on the rates of lateral and longitudinal acceleration epochs at various thresholds. It is observed that, for the median driver, the rates of epochs are up to three orders of magnitude higher on low-speed roads as compared to high-speed roads. This catalog will facilitate intelligent vehicle system designers to compare and tune their systems for safer driving experiences. It will also allow agencies with similar data to create comparable catalogs facilitating safety and behavioral comparisons between populations. Datasets:
  • Simple Diesel Train Fuel Consumption Model for Real-Time Train Applications
    Ahn, Kyoungho; Aredah, Ahmed; Rakha, Hesham A.; Wei, Tongchuan; Frey, H. Christopher (MDPI, 2023-04-20)
    This paper introduces a simple diesel train energy consumption model that calculates the instantaneous energy consumption using vehicle operational input variables, including the instantaneous speed, acceleration, and roadway grade, which can be easily obtained from global positioning system (GPS) loggers. The model was tested against real-world data and produced an error of −1.33% for all data and errors ranging from −12.4% to +8.0% for energy consumption of four train datasets amounting to a total of 5854 km trips. The study also validated the proposed model with separate data that were collected between Valencia and Cuenca, Spain, which had a total length of 198 km and found that the model was accurate, yielding a relative error of −1.55% for the total energy consumption. These results show that the proposed model can be used by train operators, transportation planners, policy makers, and environmental engineers to evaluate the energy consumption effects of train operational projects and train simulation within intermodal transportation planning tools.
  • Development and Evaluation of a Cellular Vehicle-to-Everything Enabled Energy-Efficient Dynamic Routing Application
    Farag, Mohamed M. G.; Rakha, Hesham A. (MDPI, 2023-02-19)
    Cellular vehicle-to-everything (C-V2X) is a communication technology that supports various safety, mobility, and environmental applications, given its higher reliability properties compared to other communication technologies. The performance of these C-V2X-enabled intelligent transportation system (ITS) applications is affected by the performance of the C-V2X communication technology (mainly packet loss). Similarly, the performance of the C-V2X communication is dependent on the vehicular traffic density which is affected by the traffic mobility patterns and vehicle routing strategies. Consequently, it is critical to develop a tool that can simulate, analyze, and evaluate the mutual interactions of the transportation and communication systems at the application level to quantify the benefits of C-V2X-enabled ITS applications realistically. In this paper, we demonstrate the benefits gained when using C-V2X Vehicle-to-Infrastructure (V2I) communication technology in an energy-efficient dynamic routing application. Specifically, we develop a Connected Energy-Efficient Dynamic Routing (C-EEDR) application using C-V2X as a communication medium in an integrated vehicular traffic and communication simulator (INTEGRATION). The results demonstrate that the C-EEDR application achieves fuel savings of up to 16.6% and 14.7% in the IDEAL and C-V2X communication cases, respectively, for a peak hour demand on the downtown Los Angeles network considering a 50% level of market penetration of connected vehicles. The results demonstrate that the fuel savings increase with increasing levels of market penetration at lower traffic demand levels (25% and 50% the peak demand). At higher traffic demand levels (75% and 100%), the fuel savings increase with increasing levels of market penetration with maximum benefits at a 50% market penetration rate. Although the communication system is affected by the high density of vehicles at the high traffic demand levels (75% and 100% the peak demand), the C-EEDR application manages to perform reliably, producing system-wide fuel consumption savings.The C-EEDR application achieves fuel savings of 15.2% and 11.7% for the IDEAL communication and 14% and 9% for the C-V2X communication at the 75% and 100% market penetration rates, respectively. Finally, the paper demonstrates that the C-V2X communication constraints only affect the performance of the C-EEDR application at the full demand level when the market penetration of the connected vehicles exceeds 25%. This degradation, however, is minimal (less than a 2.5% reduction in fuel savings).
  • Lighting Strategies to Increase Nighttime Pedestrian Visibility at Midblock Crosswalks
    Bhagavathula, Rajaram; Gibbons, Ronald B. (MDPI, 2023-01-12)
    In the last decade, pedestrian fatalities at night, especially at midblock locations, have been increasing at an alarming rate. Lighting is an effective countermeasure in reducing nighttime crashes. However, few studies have evaluated the effects of crosswalk lighting on pedestrian visibility at midblock locations. There is an existing need to develop lighting designs that increase pedestrian visibility. Further, the safety effects of lighting have never been directly compared to other pedestrian-crossing treatments (such as flashing signs, rectangular rapid flashing beacons (RRFBs), etc.). Thus, in order to make effective recommendations for increasing nighttime pedestrian visibility, it is important to compare the visibility benefits of crosswalk lighting designs with and without pedestrian-crossing treatments. This study evaluated the visual performance of five midblock crosswalk lighting designs along with two pedestrian safety countermeasures at three light levels on a realistic midblock crosswalk. Visual performance was measured by calculating the distance at which the participants could detect a child-sized mannequin under the evaluated conditions. The results showed that midblock crosswalks should be illuminated to an average vertical illuminance of 10 lux to ensure optimal pedestrian visibility. Lighting designs that render the pedestrian in positive contrast (area in front of the crosswalk is illuminated) are recommended to increase pedestrian visibility. It is also recommended that pedestrian-crossing treatments, such as RRFBs and flashing signs, should be used with lighting to increase nighttime visibility.
  • An Exploration of the Decline in E-Scooter Ridership after the Introduction of Mandatory E-Scooter Parking Corrals on Virginia Tech's Campus in Blacksburg, VA
    Buehler, Ralph; Broaddus, Andrea; White, Elizabeth; Sweeney, Ted; Evans, Chris (MDPI, 2022-12-23)
    We report shared e-scooter ridership and rider perceptions on Virginia Tech’s Blacksburg campus before and after introduction of mandatory e-scooter parking corrals in January 2022. The analysis relies on a panel of 131 e-scooter riders surveyed in Fall 2021 and Spring 2022. Although parking corrals were perceived favorably prior to implementation, perceptions became more negative afterwards. Respondents said corrals were not located where needed, difficult to find, fully occupied, and took too much extra time to use. After parking corrals were introduced, ridership declined 72% overall and also fell for all socio-economic subgroups. The heaviest user groups, like undergraduate males, were most likely to quit. The first study identifying desired and actual egress times for e-scooters, we found that roughly two-thirds of riders desired egress times under 2 min and one quarter under 1 min. Prior to the introduction of parking corrals, 82% of riders reported actual egress times under 2 min, and 43% under 1 min. Those who kept riding after the introduction of e-scooter corrals reported longer actual egress times and a stronger stated desire for egress times under 2 min. Communities should be careful when imposing e-scooter parking restrictions to ensure that e-scooter egress time is sufficiently low—ideally within an easy 2 min walk of popular origins and destinations.
  • Impact of Temporary Browsing Restrictions on Drivers' Situation Awareness When Interacting with In-Vehicle Infotainment Systems
    Meyer, Jason; Llaneras, Eddy; Fitch, Gregory M. (MDPI, 2022-12-07)
    Looking away from the road during a task degrades situation awareness of potential hazards. Long glances back to the road rebuild this awareness and are thought to be critical for maintaining good vehicle control and recognizing conflicts. To further investigate the importance of rebuilding situation awareness, a controlled test-track study was performed that evaluated drivers’ hazard awareness and response performance to a surprise event after completing a task that involved pausing partway through it to look back at the road. Thirty-two drivers completed a visual-manual infotainment system secondary task. Half of the drivers were instructed to pause their browsing mid-task, while the others were not. While the task was being performed, a lead vehicle activated its hazard lights. It then unexpectedly dropped a fake muffler once drivers completed the task. Drivers’ visual attention to the road and their ability to respond to the muffler were measured. The drivers that paused their browsing were more aware of the lead vehicle’s hazard lights, showed less surprise to the dropped muffler, and executed more measured avoidance maneuvers compared to the drivers that did not pause their browsing. These findings suggest that drivers’ situation awareness can be better maintained when task interactions are paced, allowing for longer monitoring of the environment. Mechanisms that encourage drivers to take restorative on-road glances during extended browsing may be a key aspect of an overall approach to mitigating driver distraction.
  • In-Depth Evaluation of Association between Crash and Hand Arthritis via Naturalistic Driving Study
    Almannaa, Mohammed H.; Bareiss, Max G.; Riexinger, Luke E.; Guo, Feng (MDPI, 2022-11-25)
    Severe arthritis can limit a driver’s range of motion and increase their crash risk. The high prevalence of arthritis among the US driver population, especially among senior drivers, makes it a public safety concern. In this study, we evaluate the impact of arthritis on driving behavior and crash risk using the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS), which collected continuous driving data through data acquisition systems installed on participant’s vehicles. A detailed questionnaire survey was administered on demographic, health conditions, and personality information at the time of recruitment. The dataset includes 3563 participants. Among them, 78 drivers were identified to have severe arthritis, and they contributed to 414 out of 1641 crashes. We systematically evaluated the impact of severe arthritis on crash risk, secondary task engagement, and fitness-to-drive metrics. The results show there is a significant relationship between arthritis and crash risk, with an odds ratio of 1.99 with adjustment for age effects, which indicates that individuals with arthritis are twice as likely to be involved in a crash. There is no statistically significant association between arthritis and secondary task engagement, as well as the sensation-seeking scores, a personality trait.
  • Impact of Solid State Roadway Lighting on Melatonin in Humans
    Gibbons, Ronald B.; Bhagavathula, Rajaram; Warfield, Benjamin; Brainard, George C.; Hanifin, John P. (MDPI, 2022-11-18)
    Introduction: In 2009, the World Health Organization identified vehicle crashes, both injury-related and fatal, as a public health hazard. Roadway lighting has long been used to reduce crashes and improve the safety of all road users. Ocular light exposure at night can suppress melatonin levels in humans. At sufficient light levels, all visible light wavelengths can elicit this response, but melatonin suppression is maximally sensitive to visible short wavelength light. With the conversion of roadway lighting to solid state sources that have a greater short wavelength spectrum than traditional sources, there is a potential negative health impact through suppressed melatonin levels to roadway users and those living close to the roadway. This paper presents data on the impact of outdoor roadway lighting on salivary melatonin in three cohorts of participants: drivers, pedestrians, and those experiencing light trespass in their homes. Methods: In an outdoor naturalistic roadway environment, healthy participants (N = 29) each being assigned to a cohort of either pedestrian, driver, or light trespass experiment, were exposed to five different solid state light sources with differing spectral emissions and one no lighting condition. Salivary melatonin measurements were made under an average roadway luminance of 1.0 cd/m2 (IES RP-18 Roadway Lighting Requirements for expressway roads) with a corneal melanopic Equivalent Daylight Illuminances (EDI) ranging from 0.22 to 0.86 lux. Results: The results indicate that compared to the no roadway lighting condition, the roadway light source spectral content did not significantly impact salivary melatonin levels in the participants in any of the cohorts. Conclusions: These data show that recommended levels of street lighting for expressway roads do not elicit an acute suppression of salivary melatonin and suggest that the health benefit of roadway lighting for traffic safety is not compromised by an acute effect on salivary melatonin.
  • Adaptive Control of the Atmospheric Plasma Spray Process for Functionally Graded Thermal Barrier Coatings
    Guduri, Balachandar; Batra, Romesh C. (Hindawi, 2022-11-23)
    Functionally graded coatings (FGCs) have a material composition continuously varying through the thickness but uniform in the surface parallel to the coated substrate. When used as a thermal barrier on a metallic substrate, the coating composition varies from an almost pure metal near the substrate to a pure ceramic adjacent to the outer surface exposed to a hot environment. Challenging issues in producing high quality FGCs in the presence of external disturbances with an atmospheric plasma spray process (APSP) include controlling the mean temperature, the mean axial velocity, and the positions of the constituent material particles when they arrive at the substrate to be coated. The unavoidable disturbances include fluctuations in the arc voltage and clogging of the powder in the delivery system. For a two-constituent coating, this work proposes using three modified robust model reference adaptive controllers based on the σ-modified laws and low frequency learning. One controller adjusts the current and flow rates of argon and hydrogen into the torch. The other two controllers adjust the distance of the two powder injector ports from the plasma jet axis and the average injection velocity of each powder. It is shown through numerical experiments that the three controllers implemented in an APSP consistently produce high-quality FGCs.
  • Unravelling the Complexity of Irregular Shiftwork, Fatigue and Sleep Health for Commercial Drivers and the Associated Implications for Roadway Safety
    Mabry, J. Erin; Camden, Matthew; Miller, Andrew; Sarkar, Abhijit; Manke, Aditi; Ridgeway, Christiana; Iridiastadi, Hardianto; Crowder, Tarah; Islam, Mouyid; Soccolich, Susan; Hanowski, Richard J. (MDPI, 2022-11-10)
    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
    Soccolich, Susan; Ridgeway, Christie; Mabry, J. Erin; Camden, Matthew C.; Miller, Andrew; 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.