National Surface Transportation Safety Center for Excellence Reports (NSTSCE, VTTI)

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  • Developing a Teen Driving Meta-Database Using Three Naturalistic Teen Driving Studies Plus Driver Coach Study
    Klauer, Charlie; Hua, Lesheng; Dingus, Thomas A. (2024-01-25)
    Motor vehicle collisions are the leading cause of death for teens aged 16 to 19. The risk of motor vehicle crashes is higher among teens aged 16 to 19 than among any other age group. Despite great interest in teen risky driving, little objective information about its prevalence is available. The Naturalistic Teenage Driving Study (NTDS), conducted at the Virginia Tech Transportation Institute (VTTI), provided a rich and powerful dataset, which permits researchers to evaluate driving performance over long periods and provide objective measures of driving risks and contributing factors. However, the NTDS only had 42 novice drivers from southwest Virginia. With the lack of other naturalistic studies of novice teenage driving for comparison, its findings are tentative and need further exploration and confirmation. More NDSs are needed to obtain additional crash data and determine what factors could lead to teen risky driving. Using the trigger thresholds from the NTDS, event databases were created from the Supervised Practice Driving Study (SPDS), the SPDS Attention Deficit and Hyperactivity Disorder (ADHD) Cohort NDS, the Second Strategic Highway Research Program (SHRP 2) NDS, and the Driver Coach Study. Similarly, a database of baseline epochs, per guidelines from the NTDS, was also developed for each of these studies. All event and baseline databases from all five studies were combined into one database to perform meta-analyses using naturalistic teenage driving data. This database is the most complete naturalistic teenage driving database in the world. Many of the key analyses that were performed on only 42 teenage drivers in the NTDS can now be performed on 489 novice drivers from seven locations around the U.S. In this report, we describe each database briefly, including the ADHD teen study, and provide notations about purpose, methods, measures, and instrumentation. We then review what have learned from each database about young driver crash risk. Studies based on the meta-database mainly focused on the prevalence of teen secondary task engagement, distraction, risky driving behavior, and progression of driving skill, as well as the associated crash risks for these behaviors. New projects and new work that this tool has already yielded are described herein, and additional work that still needs to be done is outlined.
  • Development of a Nighttime Visual Performance Model by Examining Distributions of Detection Distances
    Bhagavathula, Rajaram; Gibbons, Ronald B. (2023-12-22)
    Modeling the visual performance of drivers at night is complex. In addition to factors like luminance, contrast, observer age, and object size, research has shown that the motion of the object and the expectancy of the observer play an important role in the observer’s ability to detect an object on the roadway at night. Thus, it is important for a visual performance model to account for these factors. However, accounting for these factors could result in highly complex models, as accurately measuring driver expectancy and attention is difficult. A probabilistic approach to modeling nighttime driver visual performance could offer promise. In a probabilistic modeling approach, the variable of interest is treated as a random variable and the probability distribution of this variable is studied as a response to different conditions. In the case of night driving, we propose to use the detection distance of an object (such as a pedestrian) as the variable of interest. Detection distance is a measure of the reaction time of the driver. By studying the distribution of detection distances of objects under different lighting conditions, we can accurately understand the change in the detection probability of an object as a driver approaches an object. The current report had two goals. The first goal was to test if the detection distance distributions are accurately defined by the Weibull distribution. The second goal was to understand how different light levels affect the detection distance distributions of a child-sized mannequin. This was accomplished by performing a distribution analysis involving fitting a Weibull distribution to the detection distance data. The distribution fit will indicate how parameters like shape and scale vary across different conditions and their practical impacts on driver visual performance. The results of the study showed that the Weibull distribution could be used to fit the detection distance data, and that changing the light level definitely influenced the parameters of the distribution. An increase in light level increased the scale parameter and caused the detection distance distribution to stretch out from the pedestrian’s location. The results of the study also showed that both the scale and shape parameters could be used to compare the effectiveness of different lighting systems or interventions. The survivor functions of the detection distance data from the fitted Weibull distribution could be used to compare the effectiveness of a lighting system or a countermeasure by calculating the percentage of the population that detected the pedestrian from a distance greater than the stopping sight distance.
  • The Influence of COVID-19 Policies on Driving Patterns
    Perez, Miguel A.; Werner, Alice (Alec) (National Surface Transportation Safety Center for Excellence, 2023-11-02)
    This study sought to investigate the impact of government-imposed COVID-19 restrictions and mandates on driving behavior and patterns during the height of the COVID-19 pandemic in Virginia by examining the pre-restriction (pre-pandemic), restriction (stay-at-home period), and post-restriction (Summer 2020) time periods. Data from 21 vehicles in the VTTI L2 NDS study shaped the investigation. The data encompassed 11,973 trips over 145,000 km (~90,000 miles) and 3,600 data-hours (~5 data-months) of driving. To facilitate the analysis, the vehicle data were split into three separate periods (i.e., pre-pandemic, stay-at-home, and Summer 2020) anchored by three key times in the pandemic’s progression within Virginia’s COVID-19 timeframe. Results showed fluctuations, primarily in the driving exposure, during the pandemic. Changes were most extreme during the stay-at-home period. Moreover, some altered behaviors, particularly those related to driving exposure and trip intent, did not entirely return to their pre-pandemic levels by COVID-19’s 1-year mark. Driving-exposure-related variables revealed the most striking effects; all driver exposure variables changed between the pre-pandemic and stay-at-home periods. Results suggested that trips taken during the stay-at-home period were shorter and briefer, were proportionately fewer in the morning or on weekends, and more commonly involved residential roads. Driving style variables showed other differences, most notably, an increased percentage of speeding mileage between the pre-pandemic and stay-at-home periods. Destination type distributions also changed significantly across the three time periods. For example, “unknown” destinations, indicative of locations with diverse arrays of business types, were more prevalent in the Summer 2020 period than during the stay-at-home period. This change was also observable across all the control periods. While the results do not identify definitive causal factors behind traffic fatality and fatality rate increases, the results inform their discussion. First, drastic changes in the driving exposure occurred during the different pandemic periods. Some of these changes were also accompanied by changes in the driving style. Second, results do not support speeding as the only risk factor for these observed safety issues. While speeds did change, the pandemic simultaneously shifted driving distributions by roadway type. These different driving environments may have played a role in increased traffic fatalities. Third, these observed changes, particularly salient between the pre-pandemic and stay-at-home periods, were not completely elastic; that is, the changes did not fully revert after restrictions were eased.
  • Koper Curve Principle for Commercial Motor Vehicle (CMV) Traffic Enforcement
    Baker, Stephanie Ann; Trimble, Tammy E. (National Surface Transportation Safety Center for Excellence, 2023-08-15)
    The Koper curve principle postulates that crime deterrence can be improved with an optimal dosage of police presence at hot spot locations. With the goal of better understanding how to reduce commercial motor vehicle (CMV) crashes, a literature review was conducted to explore whether the Koper Curve principle has ever been applied to efforts aimed at reducing CMV crashes, and if so, how it was applied. In conducting the literature review, several related domains (deterrence, evidence-based policing, and high-visibility enforcement) were also considered as they apply to the use of the Koper Curve for CMV crash reduction. The literature related to the Koper Curve focused primarily on crime deterrence (e.g., robbery), not crash reduction. The literature review revealed one ongoing study that is using the Koper Curve principle toward the goal of reducing CMV crashes on specific interstate corridors (Kentucky Research Center, 2023). Two examples, from Nashville, Tennessee, and São Paulo, Brazil, showed the Koper Curve being applied to crash reduction more generally (not specific to CMVs), which may inform how the Koper Curve could be used to reduce CMV crashes. The literature provided a few best practices that may be helpful to practitioners seeking to reduce crashes in high-risk corridors: (1) use data to target behaviors leading to crashes; (2) use data to identify hot spots where crashes are occurring; (3) provide instruction to officers on how to conduct high-visibility enforcement; and (4) evaluate the enforcement effort.
  • Face De-identification of Drivers from NDS Data and Its Effectiveness in Human Factors
    Thapa, Surendrabikram; Cook, Julie; Sarkar, Abhijit (National Surface Transportation Safety Center for Excellence, 2023-08-08)
    Advancements in artificial intelligence (AI) and the Internet of Things (IoT) have made data the foundation for most technological innovations. As we embark on the era of big data analysis, broader access to quality data is essential for consistent advancement in research. Therefore, data sharing has become increasingly important in all fields, including transportation safety research. Data sharing can accelerate research by providing access to more data and the ability to replicate experiments and validate results. However, data sharing also poses challenges, such as the need to protect the privacy of research participants and address ethical and safety concerns when data contains personally identifiable information (PII). This report mainly focuses on the problem of sharing drivers’ face videos for transportation research. Driver video collected either through naturalistic driving studies (NDS) or simulator-based experiments contains useful information for driver behavior and human factors research. The report first gives an overview of the multitude of problems that are associated with sharing driver videos. Then, it demonstrates possible directions for data sharing by de-identifying drivers’ faces using AI-based techniques. We have demonstrated that recent developments in generative adversarial networks (GANs) can effectively help in de-identifying a person by swapping their face with that of another person. The results achieved through the proposed techniques were then evaluated qualitatively and quantitatively to prove the validity of such a system. Specifically, the report demonstrates how face-swapping algorithms can effectively de-identify faces while still preserving important attributes related to human factors research, including eye movements, head movements, and mouth movements. The experiments were done to assess the validity of GAN-based face de-identification on faces with varied anthropometric measurements. The participants used in the data had varied physical features as well. The dataset used was under lighting conditions that varied from normal to extreme conditions. This helped to check the robustness of the GAN-based techniques. The experiment was carried out for over 115,000 frames to account for most naturalistic driving conditions. Error metrics for head movements like differences in roll angle, pitch angle, and yaw angle were calculated. Similarly, the errors in eye aspect ratio, lip aspect ratio, and pupil circularity were also calculated as they are important in the assessment of various secondary behaviors of drivers while driving. We also calculated errors to assess the de-identified and original pairs more quantitatively. Next, we showed that a face can be swapped with faces that are artificially generated. We used GAN-based techniques to generate faces that were not present in the dataset used for training the model and were not known to exist before the generation process. These faces were then used for swapping with the original faces in our experiments. This gives researchers additional flexibility in choosing the type of face they want to swap. The report concludes by discussing possible measures to share such de-identified videos with the greater research community. Data sharing across disciplines helps to build collaboration and advance research, but it is important to ensure that ethical and safety concerns are addressed when data contains PII. The proposed techniques in this report provide a way to share driver face videos while still protecting the privacy of research participants; however, we recommend that such sharing should still be done under proper guidance from institutional review boards and should have a proper data use license.
  • Do Real-time and Post Hoc Feedback Reduce Teen Drivers' Engagement in Secondary Tasks?
    Hua, Lesheng; Ankem, Gayatri; Noble, Alexandria; Baynes, Peter; Klauer, Charlie; Dingus, Thomas A. (National Surface Transportation Safety Center for Excellence, 2023-08-02)
    In 2020, 2,800 teens in the United States between the ages of 13 and 19 were killed in motor vehicle crashes (Centers for Disease Control and Prevention, 2023). The purpose of this study is to assess if there is an additional benefit to the driver feedback system implemented in the Driver Coach Study (Klauer et al., 2017) on secondary task reduction and if the same trends of parental involvement are observed. The data used in this study were drawn from two previously completed naturalistic driving studies involving teenage drivers. The Driver Coach Study recruited 90 teen-parent dyads and presented the teen driver with feedback on their driving performance for the first 6 months (Klauer et al., 2017). Parents were able to review a website that provided information on the feedback that their teen received. The Driver Coach Study data were compared to the Supervised Practice Driving Study, which observed 88 teenage drivers during naturalistic driving in the same geographic location who did not receive feedback. Novice driver secondary task engagement was recorded. Parental involvement was examined by tracking which teen/parent groups checked the website and which did not. Results suggest that teen drivers who received feedback were overall less likely to engage in secondary tasks as well as less likely to multitask than those teen drivers who did not receive feedback. Additionally, females generally engaged in secondary tasks more often than males. Teen drivers whose parents logged in to the feedback website also reduced their engagement in some secondary tasks but not all. Unfortunately, no significant reduction in cell phone use was observed between teen drivers who received feedback and those who did not. Overall, the results suggest that further research should be conducted, as monitoring and feedback for teen drivers does reduce overall secondary task engagement.
  • A Holistic Approach to Reducing Adolescent Risky Behavior: Combining Driving Performance Measures with Psychological and Neurobiological Measures of Risky Adolescent Behavior
    Novotny, Adam; Noble, Alex; Kim-Spoon, Jungmeen; Klauer, Charlie (National Surface Transportation Safety Center for Excellence, 2023-08-02)
    Adolescent drivers are one of the age groups with the highest crash risks due to factors such as inexperience and poor judgment, an increased propensity for risk-taking, and a higher likelihood to engage in secondary tasks. Previous research has indicated that there may be correlations between teen risky driving behaviors and health risk behaviors such as substance use. Therefore, it is important to understand if there is a relationship between adolescent risky behaviors and unsafe driving outcomes. To investigate this, the Virginia Tech Transportation Institute (VTTI) partnered with the Virginia Tech JK Lifespan Development Lab to conduct a pilot study. During this study, 17 novice teen drivers within 1 month of obtaining their provisional license who were also participating in the Neurobehavioral Determinants of Health-Related Behaviors (NDHRB) Study were recruited. Participants’ personal vehicles were instrumented with VTTI’s mini-data acquisition system, which collected driving performance and behavior data. Data was collected over a 6-month period and analyzed for kinematic risky driving events, eye-glance behavior, secondary task engagement, and seatbelt use. This data was combined with the psychosocial/neurobiological data collected from the surveys, questionnaires, and tests during the NDHRB study. Correlations were discovered between risky driving behaviors (kinematic risky driving events, eye-glance behaviors, secondary task engagement and cellphone use, and proper seatbelt use), and psychosocial/neurobiological measures (reported substance use, insula activation during a lottery task, general health self-assessment, Domain-Specific Risk-Taking Scale health safety risk, health risk behavior, and self-reported risk). The results from this pilot study were promising and point to the need for future research into teen risky behaviors, either driving or otherwise, to create countermeasures to reduce teen crash rates.
  • Trucking Along: Safe Drives, Healthy Lives
    Meissner, Kary; Sloss, Jolee; Mabry, Erin; Gray, April; Martin, Cindy; Levin, Jacob; Camden, Matthew (National Surface Transportation Safety Center for Excellence, 2023-08-02)
    This project was initiated to update, refresh, expand, and rebrand the Driving Healthy website and social media accounts into a comprehensive and inclusive healthy driving community platform: Trucking Along: Safe Drives, Healthy Lives. The research team added information, resources, and tools to support healthy habits, both on and beyond the road. Topics covered now include healthy eating, exercise, sleep, mental health, equity and inclusion, and safety on the road, including bringing awareness to the issue of human trafficking and how CMV drivers can help at-risk individuals. The update also added a focus on content and resources for women CMV drivers, who represent a growing but often overlooked group within the trucking industry. The team added new information, including a section dedicated to bringing awareness to sexual harassment. In addition to targeted content for new, seasoned, and prospective CMV drivers, the Trucking Along community platform added information, resources, and tips for end users, who play a critical role in providing support and encouragement to drivers within their’ social and workplace networks. The overall goal of this project was to create a comprehensive and accessible resource that could be used by drivers, from various backgrounds and walks of life, at all stages in their career, to educate them on being happy, safe, and healthy in their trucking careers. The team strives to continue growing and expanding the Trucking Along community platform to continue providing accurate, free, and relevant health information to CMV drivers from all backgrounds.
  • Slippery Road Vehicle Early Warning System: Method Augmentation
    Druta, Cristian; Alden, Andrew (National Surface Transportation Safety Center for Excellence, 2023-03-09)
    Two prior projects conducted by the Virginia Tech Transportation Institute (VTTI) for the Federal Highway Administration (FWHA) demonstrated that small but statistically significant differences in vehicle wheel rotational rates, attributed to tire microslip, can be used to quantify the changes in tire grip that occur when pavement conditions change due to road weather. The first project established proof-of-concept by demonstrating that tire microslip increased where the pavement was covered with liquid or frozen water and confirmed that OEM wheel sensors were of sufficient resolution to determine statistically different microslip rates at the driving and free-rolling wheels. The second project introduced the concept of a traction index (TI) and explored confounding factors, such as road incline and wind. Results showed that the “noise” that resulted from apparent vehicle acceleration and wind prevented accurate measurement of TI given the constraints of sensor resolution and other factors. For the current study, researchers hypothesized that instantaneous fuel consumption rate (IFCR) and engine throttle position, available from the vehicle network, might be used to correct the calculated TI to account for the confounds. The relationship between TI and IFCR and engine throttle position was analyzed using a variety of techniques. In the end, the research team was unable to demonstrate that basic TI calculated values could be corrected using vehicle dynamic data due to factors stemming from the unsuitability of the existing road friction dataset for the application intended. Over the time spanned by these three studies, some companies have begun to use microslip and other vehicle variables as a basis for dynamic assessment of road friction. Also, vehicle data, including that which might be used to assess road weather, are now available commercially. These data sources provide opportunities for future research on the safety and environmental benefits of real-time assessment and sharing of road weather information.
  • Understanding Crashes Involving Roadway Objects with SHRP 2 Naturalistic Driving Study Data
    Li, Eric; Hao, Haiyang; Gibbons, Ronald B.; Medina, Alejandra (National Surface Transportation Safety Center for Excellence, 2023-03-08)
    This project used the second Strategic Highway Research Program (SHRP 2) naturalistic driving study (NDS) data as an alternate data source to police-reported crash data to better understand roadway object crashes. The objectives included determining crash causation, recommending strategies for crash prevention, and understanding the implications for highly automated vehicles (HAVs). Researchers addressed these objectives with a three-pronged approach: (1) a detailed engineering study of roadway object events to identify and quantify effects of a large number of relevant variables; (2) a machine-vision-oriented study to document the implications of roadway object events on machine vision performance; and (3) a detailed case study analysis of representative roadway object events to provide further qualitative results on how and why roadway object crashes occur and what potential actions can prevent such events effectively.
  • Speed Management Countermeasures: Gaps and Opportunities
    Wotring, Brian; Medina, Alejandra; Antin, Jonathan F. (National Surface Transportation Safety Center for Excellence, 2023-02-01)
    The number of speeding-related crashes continues to be a major concern on U.S. highways, with 29% of roadway fatalities determined to be due to speeding (National Center for Statistics and Analysis, 2022). To reduce the number of fatalities and move towards a goal of zero roadway deaths, it is important to understand the risks and factors that lead to speeding behaviors as well as to evaluate currently deployed speeding countermeasures. In order to determine the state of speeding countermeasures, a highly targeted literature review was focused on published documentation from national agencies and the peer-reviewed literature. These specific sources were chosen to rely solely on the highest quality countermeasure information currently available. Using these sources, a gap analysis was then completed based on statements and postulations within the noted literature. Additionally, the research team engaged in a brainstorming session to determine additional gaps and potential opportunities for improved countermeasure implementation not yet represented in the formal literature. Finally, the research team also collected information on several speeding campaigns and initiatives currently in place. In order to better organize the findings, they were grouped by domain. Such groups include advanced technologies, education and outreach, enforcement, engineering, and other. Overall, the review of documentation on speeding countermeasures revealed a large number of gaps and opportunities in the current knowledge space. One benefit of housing so many in a single location is to aid in the process of hypothesis generation. This collection may prove useful in guiding or eliciting future ideas.
  • In-depth Analysis of Crash Risk Associated with Eyes-off-road Duration by Road Control Type and Intersection Type
    Han, Shu; Guo, Feng; Klauer, Charlie (National Surface Transportation Safety Center for Excellence, 2023-01-27)
    This study quantified the odds ratios (ORs) associated with eyes-off-road (EOR) durations on different road control and intersection types using the Second Strategic Highway Research Program Naturalistic Data Study (SHRP 2 NDS) dataset. The motivation of this project was to provide support for driver state monitoring systems (DMSs) regarding alert timer settings when drivers look away from the road. The main research questions addressed are: (1) Should there be a different DMS alert timer setting on controlled access roads vs. uncontrolled access roads? And (2) Should there be a different alert timer setting on intersections vs. non-intersections? It is not surprising that the longer drivers look away, the higher the resulting ORs. Overall, this study suggests that different DMS alert timer settings are needed for different road geometrical characteristics. For uncontrolled access roads, a timer with a lower threshold is recommended, and a higher threshold is recommended for controlled access roads. For intersections, a zero tolerance for vision interruption is ideal. But practically, a relative lower threshold is recommended at intersections compared with non-intersection related segments. This finding could provide critical information for advanced driver assistance system development and driver behavior education programs.
  • Crosswalk Lighting Using Narrow Beam Illuminator
    Palmer, Matthew; Bhagavathula, Rajaram; Kassing, Andrew (National Surface Transportation Safety Center for Excellence, 2022-12-09)
    This project’s main objective was to collect and analyze preliminary data regarding the safety benefits of additional narrow beam crosswalk lighting in a naturalistic environment. Experiment participants operated vehicles while confederate pedestrians (child-sized mannequins) were staged at various positions with or without overhead lighting and crosswalk lighting that used a commercially available narrow beam LED (light emitting diode) luminaire. Salex loaned the crosswalk lighting illuminator (CWI) luminaires to the Virginia Tech Transportation Institute for the experiment. When used with overhead lighting, the CWI increased the detection distance of the confederate pedestrians in the crosswalk to 297 m while only increasing the power consumption by 5%. This was nearly double the 160-m detection distance for the highest illuminance overhead only baseline condition. The experiment showed no benefit to using the CWI lighting alone. The results reaffirm that the direction of lighting is significant, but it is only one factor. Merely increasing light levels may not increase visual performance, just as changing the direction may not increase performance. The difference in the location of the illuminators and the overhead lights results in the light coming from different directions and illuminating the pedestrians and the background (roadway) differently than either alone. The effect on pedestrian contrast should be investigated further before setting illuminance levels for CWI lighting. Even with that caveat, the addition of a narrow beam CWI improves driver visual performance at detecting pedestrians in a midblock crosswalk by 88%. This is a powerful finding that should be considered as a safety treatment for midblock crosswalks.
  • A Catalog of Health and Wellness Programs for Commercial Drivers
    Glenn, T. Laurel; Mabry, J. Erin; Hickman, Jeffrey S. (National Surface Transportation Safety Center for Excellence, 2022-12-08)
    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.
  • Camera-based Feature Identification for EasyMile Operation
    Sarkar, Abhijit; Sundharam, Vaibhav; Manke, Aditi; Grove, Kevin (National Surface Transportation Safety Center for Excellence, 2022-11-15)
    The EasyMile deployment studied in this work included cameras that captured the 360 degrees of roadway environment around the vehicle. We developed a scene perception algorithm using computer vision technology to track other roadway agents like cars, pedestrians, and bicyclists around the EasyMile LSAV. We used object detection and tracking algorithms to track the trajectories of each of the roadway agents. Then we used perspective geometry and camera specifications to find the relative distances and speeds of these agents with respect to the EasyMile. This helped us understand the configurations of the traffic around the LSAV and study other drivers’ temporal behavior. For example, the collected data shows the approach of any vehicle towards the EasyMile. Finally, we used this information to study other vehicles’ maneuvers and show how the information from the cameras can be used to study simple maneuvers of other vehicles such as cut-ins, lane changes, and following behavior. Through these camera-based tools, we have demonstrated examples from the real-world deployment. We studied following behavior characteristics that show the relative distance and speed of other vehicles’ following behavior. We have also demonstrated cut-in behaviors through the longitudinal and lateral trajectories of cut-in vehicles. We also showed how abrupt cut-ins may lead the EasyMile to apply its brakes, leading to safety critical events for following vehicles. Finally, we demonstrated how pedestrian behavior can be studied via these camera-based methods.
  • Effectiveness of Lighted Work Zone Apparel: Effects on Visibility
    Bhagavathula, Rajaram; Kassing, Andrew; Gibbons, Ronald B.; Medina, Alejandra (National Surface Transportation Safety Center for Excellence, 2022-11-11)
    In United States, collisions between vehicles and workers in a work zone are a major problem. In 2020, there were 157 worker fatalities in work zone in the United States. Increasing worker conspicuity has the potential to reduce to fatalities by making them more visible to motorists. Retroreflective vests (Class 3) and trousers (Class E) worn by workers in a nighttime work zone are passive in nature; i.e., they require light from oncoming vehicle headlamps to work. The advancement of LED technology has made it easy to install them on retroreflective vests and hard hats to increase their conspicuity. Multiple configurations of LEDs and flash patterns installed on vests and hard hats could be used to increase worker conspicuity. Further, equipment manufacturers are now offering work zone apparel and head protection which incorporate lights into portions of the retroreflective material, or adds light to a specific piece of equipment (hard hats). One of the major benefits is that these do not require external light sources for activation whereas retroreflective material relies on an eternal light source. According to manufacturers, the new apparel and equipment improve visibility, and the pieces are washable. There is also the potential for lighted apparel that uses colors or operating features (such as flash patterns) to further increase worker conspicuity. However, a typical work zone is a visually cluttered with flashing lights on work vehicles. Therefore, it is important that the selected configuration of lights on workers apparel are not masked by the visual clutter in the work zone. The conspicuity of passive (retroreflective material only) and active (both retroreflective and LEDs) apparel in a work zone will help in determining the apparel that would increase the conspicuity of the workers in the work zone. The goal of the current study is to evaluate effectiveness of lighted work zone apparel under realistic conditions. More specifically, the goal is to compare the effectiveness of various kinds of lighted worker apparel (colors, flash patterns, lighted hard hat, etc.) to that of standard retroreflective material under varying visually cluttered conditions. In the current study, the effects of worker apparel and scene clutter on driver visual performance were evaluated under realistic work zone conditions. Driver visual performance was measured indirectly using the detection distance of work-zone workers as indicated by participants as they drove through the simulated work-zone environment. The results of the current study show that lighted worker vests and helmet-mounted lights plays a critical role in increasing the conspicuity of workers in active nighttime work-zone environments with visually cluttered environments. Lighted work-zone vests with white-colored LEDs paired with helmet-mounted LEDs (also white colored), either in flashing or in a steady-on condition, had the longest detection distances. Standard Class 3 retroreflective vests had the lowest detection distances among all the garments evaluated. When workers wore the lighted apparel with red and white LEDs without the lighted helmet, the detection distances were shorter than with the lighted helmet but longer than with the retroreflective vest alone. Based on these results, a combination of lighted garments along with a lighted helmet, preferably in a flashing pattern or steady-on, are recommended to increase the conspicuity of workers in active nighttime work-zone environments.
  • Exploration of U.S. National Pedestrian Traffic Crashes
    Witcher, Christina; Henry, Scott; Sullivan, Kaye (National Surface Transportation Safety Center for Excellence, 2022-11-07)
    The number of pedestrian traffic fatalities in the U.S. has been increasing since 2009, following a general decline for several decades, while the number of non-fatally injured pedestrians in traffic crashes remained relatively consistent during the same period. Despite this disparity, there are few traffic safety studies on the differences between fatal and non-fatal injured pedestrians. This exploratory analysis provides a general landscape of the characteristics of recent police-reported crashes for fatally and non-fatally injured pedestrians, including environment, vehicle, driver, and pedestrian characteristics, as well as a comparison of those characteristics for fatal versus non-fatal injured pedestrians. Pedestrian cases were extracted from the U.S. national datasets of police-reported traffic crashes maintained by the National Highway Traffic Safety Administration (NHTSA). Fatal pedestrian crash data were collected from the Fatality Analysis Reporting System for calendar years (CYs) 2010–2019. Non-fatal injured pedestrian crash data were collected from the National Automotive Sampling System General Estimates System for CYs 2010–2015 and the Crash Report Sampling System for CYs 2016–2019. The findings of this landscape analysis can be used to identify potential factors influencing the continued increase in fatalities and the differences between fatal and non-fatal pedestrian crashes. This information is important for determining areas of further study needed to develop or refine vehicle and infrastructure countermeasures and public campaigns to improve pedestrian traffic safety.
  • Safety and Crash Risks with Vehicle Strings
    Li, Eric; Gibbons, Ronald B.; Kim, Bumsik (National Surface Transportation Safety Center for Excellence, 2022-11-03)
    In the course of many previous studies on nighttime safety, the research team came to believe that there is a potential safety benefit of cars traveling in stabilized strings during free-flow conditions. Recent technical advances in vehicle-to-vehicle communications open the opportunity for vehicle safety systems to share information about driving conditions that can be used to improve safety. This project seeks to understand the safety implications of vehicles operating in strings to inform how effective cooperative driving might be at improving driving safety. In terms of driver behavior, the study revealed the following by comparing vehicles with and without a leading vehicle. For driver behavioral differences at freeway ramp locations, overall, the study showed that drivers following other vehicles tended to travel at lower speeds than those who did not follow another vehicle. In addition to the lower speeds, however, vehicles with a leading vehicle frequently showed higher acceleration activity, seemingly suggesting that they were adjusting speed relative to the leading vehicles. In addition, the study also revealed that, during daytime, significant driver behavioral differences between vehicles with leading vehicles and those without leading vehicles were more evident at entrance ramp locations. Significant differences during nighttime tended to be more evident at both entrance and exit ramp locations but only for the analysis segments that are farther away from the ramp junction. For driver behavior differences at intersections, similarly, the intersection analysis showed that drivers following other vehicles tended to travel slower, but with higher acceleration variance and jerk. These behaviors are likely due to the drivers needing to adjust speed more when following other vehicles. Drivers might also switch lanes and/or turn faster at intersections due to greater confidence about the roadway condition and/or fewer interactions with other vehicles when traveling in strings.
  • Initial Investigation of Intersection Lighting
    Bhagavathula, Rajaram; Gibbons, Ronald B. (National Surface Transportation Safety Center for Excellence, 2022-10-31)
    Nighttime crashes at intersections are a major traffic safety concern in the United States. Although providing lighting at intersections has proved to be a successful intervention against night crashes, current approaches to designing lighting at intersections are relatively simplistic, based on recommending light levels. These light levels stem from research that evaluated the effect of intersection lighting on night crashes, which does not account for the role of a driver’s visual performance or the effects of vehicle headlamps. For effective lighting design at intersections, empirical research is required to evaluate the effects of intersection lighting design on a driver’s visual performance as well as perceived visibility and glare. The current study had two goals. The first was to quantify visual performance in three lighting configurations (illuminating the intersection box, approach, or both). The second was to determine what lighting levels within each lighting configuration support the best visual performance. The study involved a target detection task, completed at night on a realistic roadway intersection. Twenty-four participants completed the study, with equal numbers of younger (18–35 years) and older (65+) individuals. Illuminating the intersection box led to superior visual performance, as indicated by longer target detection distances, fewer missed targets, and more targets identified within a safe stopping distance. For this lighting configuration, visual performance plateaued between an illuminance level of 8 and 12 lux. Visual performance was inferior in lighting configurations in which only the approach to the intersection or both the approach to the intersection and the intersection box were illuminated, and there was not consistent plateauing of visual performance in either condition. Increased performance with box lighting was likely due largely to the rendering of targets involved. Visual performance was reduced among older participants, though age-related differences were consistent across lighting configurations. These results have important implications for the design of intersection lighting at isolated or rural intersections. Specifically, results indicated that illuminating the intersection box is an effective strategy to increase nighttime visual performance for a wider range of driver ages and could also be an energy-efficient solution.
  • The Assessment of New Roadway Lighting in Rain and Fog
    Williams, Brian; Gibbons, Ronald B. (National Surface Transportation Safety Center for Excellence, 2022-10-27)
    This study sought to determine if different types of roadway lighting performed differently in rain or fog. The performance of the lighting was determined by participants’ ability to detect different objects along the shoulder of the road as they drove an experimental vehicle through simulated rain and fog at night on the Virginia Smart Roads Highway. Twenty-seven participants took part in this study, which consisted of three sessions: one for consenting and screening, one for performing the study in clear weather, and one for performing the study in rain and fog conditions.. As participants drove along the Smart Roads Highway, they looked for and verbally identified two types of objects that appeared on the right shoulder of the road. These included pedestrians wearing red, blue, or gray clothing, or small 7-inch square wooden targets painted red, blue, or gray. Participants also identified the color of the object. Participants performed these tasks under three different types of roadway lighting: traditional high-pressure sodium (HPS) lamps and two types of light-emitting diode (LED) lamps with different color temperatures (3500K and 6000K). Presentation orders of the lights and objects were counter-balanced to reduce learning effects. The performance of each light was determined by the distance at which participants could identify objects (detection distance) and the distance at which they could recognize the color of the objects (recognition distance). For each object and weather condition, a 2 (age) × 3 (lighting) × 3 (color) analysis of variance (ANOVA) was conducted with an alpha of 0.05. No significant difference was found among light types on participants’ ability to detect pedestrians in any weather condition. Results showed that all three light types performed equally for the detection of pedestrians in all weather conditions and for the detection of targets in clear and fog conditions. A significant difference was only found for the detection of targets in rain. However, there was no clear best performer as each light type performed well for the detection of some colors of targets and poorly for others. On average, detection distances for targets in the rain were approximately 10 m longer under the LED lights compared to the HPS.