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

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https://hdl.handle.net/10919/5529

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Investigating Attributes of Young, Inexperienced Commercial Motor Vehicle Drivers
Soccolich, Susan; Camden, Matthew C.; Hanowski, Richard J. (National Surface Transportation Safety Center for Excellence, 2024-04-19)
For years, the trucking industry has been concerned with a potential lack of qualified, safe drivers to meet the future demand of the supply chain. The current minimum age at which a driver with a commercial driver’s license (CDL) can operate interstate is 21 years old (49 CFR 391.11). However, recent developments have expanded driver licensing age requirements through the Federal Motor Carrier Safety Administration’s young driver apprenticeship programs and initiatives for young military veterans. The current study used the Commercial Driver Safety Risk Factors (CDSRF) study data to investigate the attributes of safe and unsafe young, inexperienced drivers (ages 21 to 25). The study compared young commercial drivers with and without carrier-recorded crashes, carrier-recorded preventable crashes, nationally recorded crashes, and moving violations for differences in demographic characteristics, driving-related factors, and health-related variables such as medical conditions and treatment status. Overall, most young drivers in the current study did not have a safety-related event. The proportion of drivers with a safety-related event included 14% with at least one carrier-recorded crash, 8% with at least one carrier-recorded preventable crash, 8% with at least one nationally recorded crash, and 10% with at least one moving violation. The study found young drivers who reported an out-of-service (OOS) placement in the past 3 years were at 3 times increased risk of nationally recorded crash involvement. Young drivers with a double/triple trailer endorsement had higher odds of both carrier-recorded and nationally recorded crash involvement compared to drivers without this endorsement. Approximately 80% of the sampled young drivers in the current study had a high school (HS) diploma or higher degree—a higher proportion than observed in an analysis of drivers of all ages in the CDSRF. Drivers showed lower odds of carrier-recorded crash involvement when their academic degree was another degree not listed compared to drivers with a HS diploma or bachelor’s degree. Finally, drivers with diagnosed and treated allergies showed higher risk of crash involvement compared to drivers without this diagnosis; however, it is important to note that very few drivers in the sample had allergies and were receiving treatment. Although the study found few statistically significant factors associated with increased safety event risk, the study did provide more insight into the typical young driver. As younger drivers have more opportunities to join the career field, it is important to better understand this driver age group, their potential risk factors, what factors need further research, and how this driver age group compares to other driver age groups in their demographics and risk.
Evaluation of Truck Parking Needs in a Changing Regulatory Environment
Bell, Stephen; Alden, Andrew (National Surface Transportation Safety Center for Excellence, 2024-03-15)
Commercial driver hours-of-service rules were created to ensure that operators of heavy vehicles on US roads have opportunities to receive adequate rest during and between trips. The use of electronic logging devices to replace handwritten logs, along with the implementation of automated vehicle tracking systems, has created a potential opportunity to track the location of truck drivers with respect to their hours-of-service status. It is envisioned that this real-world driving data can inform the siting of new facilities to address a critical, national shortage of safe and convenient truck parking. This investigation sought to provide proof-of-concept for the use of electronically logged hours-of-service data to determine where additional truck parking areas are needed. A sample of this data was purchased from a commercial telematics provider, and a trusted partner was contracted to transform the acquired raw data into a format that could be used within geographic database system to identify where drivers were located as they neared the end of their allowed driving time. This database would also include the locations of existing truck parking facilities so that gaps in coverage could be identified. Unfortunately, the native format of the hours-of-service data as collected and provided was not conducive to creating a continuous record of a driver’s trips that could be synchronized in time with location data. Also, the sample set of real driving data that was provided in line with the project budget contained too few records to be of practical use. Therefore, proof-of-concept was not validated with this effort. It is likely, though, that the evolution of telematic and electronic logging systems, and the perceived value of this type of information, will result in data quality improvements that will enable the type of analysis envisioned. Examples of the problems encountered are described, and lessons learned and suggestions for future efforts have been provided.
Temporal Patterns in U.S. Pedestrian Traffic Crashes
Witcher, Christina; Henry, Scott; Sullivan, Kaye; Laituri, Tony (2024-04-18)
The number of pedestrian traffic fatalities in the U.S. has been increasing since 2009, despite a general decline during the preceding decades (Figure ES1). In contrast, changes in the number of non-fatal injured pedestrians in traffic crashes were less pronounced during the same period. Our 2022 pedestrian-centric study, funded by the National Surface Transportation Safety Center for Excellence, focused on the differences between those two populations (i.e., fatal and non-fatal injured pedestrians). This analysis extended that study by exploring fatal and non-fatal injured pedestrians from the perspective of temporal characteristics (e.g., year, month, day of week, hour of day), collected from U.S. national datasets for police-reported traffic crashes maintained by the National Highway Traffic Safety Administration. Fatal pedestrian crash data from the Fatality Analysis Reporting System for the 2010–2019 calendar years were examined. Non-fatal injured pedestrian crash data were weighted estimates from two national data sources: the General Estimates System for calendar years 2010–2015 and the Crash Report Sampling System for calendar years 2016–2019. The findings of this temporal analysis can be used to identify potential factors influencing the continued increase in fatalities and the differences between fatal and non-fatal pedestrian crashes. In addition to providing distributions for each temporal characteristic, the ratio of fatal to non-fatal injured pedestrians in traffic crashes was used to identify “peaks” during which fewer pedestrian-involved crashes occurred and/or the injuries were more severe. This ratio was also used to develop categories that typify weekly driving operations. These measures showed distinct differences for fatal and non-fatal injured pedestrians. Fatal pedestrians occurred more often during early hours, weeknights, and weekend-nights, with peaks at night. Non-fatal injured pedestrians occurred more often during weekdays, evening commutes, and weeknights, with peaks during the day. There were no notable differences observed in the 2020 calendar year temporal patterns for fatal and non-fatal injured pedestrians compared with the period 2017–2019. 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.
Methods to Encourage Slow-moving Trucks to Travel in Designated Lanes
Manke, Aditi; Ridgeway, Christie; Bell, Stephen "Roe" (National Surface Transportation Safety Center for Excellence, 2024-03-20)
As the volume of traffic on highways increases, particularly heavy truck traffic, states throughout the United States are exploring innovative methods to enhance driver comfort, operational efficiency, and road safety. Instead of expanding roadways physically, more organizations are adopting a managed-lanes strategy. This approach assigns specific lanes with unique operational conditions to boost overall roadway performance in efficiency and safety. One popular application of this concept is lane restrictions for trucks. While drivers of smaller vehicles generally welcome these restrictions, research has shown mixed outcomes regarding safety and efficiency improvements. This project aimed to investigate new methods to improve the lane compliance of heavy vehicles, especially slow-moving trucks, on highways. Additionally, existing strategies for enforcement were explored, and new avenues were discussed for improving current restrictions. Six interviews with state Department of Transportation representatives, academic researchers, law enforcement officers, and truck drivers focused on three key areas: policy and enforcement, technological interventions, and effectiveness of interventions. In addition to the interviews, the Virginia 511 real-time traffic information system camera was observed to explore lane compliance violations in Virginia and the number of vehicles impeded due to the violations. Based on the results, 10 recommendations were identified to improve the operations and safety surrounding trucks, especially slow-moving trucks, on the highways.
Streamlining Drowsiness Assessment: An In-Depth Review of ORD and PERCLOS Methods
Soccolich, Susan; Hammond, Rebecca; Camden, Matt; Walker, Stuart (National Surface Transportation Safety Center for Excellence, 2024-03-15)
Every year, drowsy and fatigued driving contributes to thousands of crashes and their resulting injuries and fatalities. Naturalistic driving data allows researchers an opportunity to better understand drowsy driving through review of driver-facing video capturing the driver’s behavior and eyes. Two drowsiness measures that have been successfully used in naturalistic driving data are Observer Rating of Drowsiness (ORD) (Wiegand, McClafferty, McDonald, & Hanowski, 2009) and manual percentage of eye closure (PERCLOS) (Wierwille & Ellsworth, 1994). The current study explored how different drowsiness measures impact fatigue determination for an event and study estimates of fatigue prevalence, risk, and secondary task association for truck and motorcoach drivers. Analyses investigated PERCLOS scores using 1 minute of data (PERCLOS 1) versus 3 minutes of data (PERCLOS 3). The study found the sample size of events with PERCLOS data increased by 8.94% when PERCLOS 1 criteria were used. Overall, matching fatigue determination (whether fatigue was observed) in PERCLOS 3 and PERCLOS 1 scores was found for between 95.89% and 99.48% of truck and motorcoach baselines (BLs) and safety-critical events (SCEs). The risk of SCE involvement when driving while fatigued was consistent for truck drivers when using PERCLOS 1 or PERCLOS 3 to determine fatigue. However, for motorcoach drivers, the risk of SCE involvement when driving while fatigued depended on the PERCLOS measure used. The study also aimed to determine how to potentially lessen the effort of fatigue data reduction in future studies and obtain the most valuable dataset at the lowest cost to time and budget. The single fatigue reduction approach with the lowest time and cost budget was PERCLOS 1 for all events. However, a targeted fatigue reduction approach that includes ORD for all events and targeted PERCLOS 3 or PERCLOS 1 reduction for events that meet or exceed an ORD threshold can reduce the cost of fatigue reduction while maintaining the advantage of ORD reduction.
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.

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