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

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Analyzing Pedestrian Safety Near Bus Stops in Northern Virginia
Herbers, Eileen (National Surface Transportation Safety Center for Excellence, 2026-03-06)
In the US, traffic-related pedestrian crashes have seen a significant increase, resulting in a higher rate of pedestrian fatalities. From 2012 to 2022, pedestrian fatalities rose by 56%, compared to a 26% increase in all motor vehicle deaths. In Virginia, pedestrian fatalities accounted for 18% of all traffic-related deaths in 2022, marking a 50% increase from 2017. These alarming statistics highlight the need to identify factors contributing to pedestrian crashes to implement effective countermeasures. The Virginia Department of Transportation (VDOT) has developed several projects to improve pedestrian safety, including the Pedestrian Safety Action Plan (PSAP), the Strategic Highway Safety Plan (SHSP), and the Vulnerable Road User Safety Assessment (VRUSA). These reports emphasize the importance of addressing pedestrian safety and countermeasures to reduce pedestrian fatalities. A key finding included that, in Virginia between 2018 and 2022, about 26% of pedestrian fatalities on non-limited access roads occurred within 150 ft of a bus stop. This research analyzed pedestrian crashes in Northern Virginia (NOVA) to understand factors that may contribute to pedestrian crashes near bus stops. By analyzing pedestrian crash data from 2018 to 2024, bus stop locations, and census data, the study compared crashes within 150 ft of a bus stop to those more than 500 ft away. Some key findings include that crashes within 150 ft of a bus stop were 3.1 times more likely to be near an intersection than crashes more than 500 ft from a bus stop. Crashes near bus stops were more likely to occur near traffic signals, while those farther away were more likely to occur where only traffic lanes were marked or without traffic control. Crashes near bus stops occurred more frequently on arterial roads, while those farther away from bus stops occurred more frequently on local roads. For crashes in the dark, crashes far from bus stops were less likely to have road lighting, which resulted in a higher proportion of severe or fatal crashes. Finally, crashes near bus stops were more likely to occur in areas with higher bus stops per capita, higher walkability scores, and higher population density, but lower overall social determinants of health. Some factors that were not influenced by the crash distance to the nearest bus stop included the traffic control device, weather or road conditions, or whether the crash was a hit and run. This research provides insights into the relationship between bus stop proximity and pedestrian crash risk, emphasizing the need for targeted safety improvements in NOVA’s bus system.
Evaluation of Driver Response to Arrow Boards of Varying Patterns
Williams, Brian M.; Kassing, Andrew; Robinson, Sarah (National Surface Transportation Safety Center for Excellence, 2026-03-06)
To investigate potential impacts of various arrow board display patterns on traffic behavior, a semi-naturalistic study was conducted within a live work zone in Salem, Virginia. Specifically, the arrow board displays examined in the study were flashing arrow, sequential arrow, and sequential chevron patterns. The data collection site for the study was a work zone with a semi-permanent lane closure on Wildwood Road in Salem, Virginia, underneath the I-81 overpass at Exit 137. An arrow board was placed within a taper of traffic barrels in the left northbound lane, approximately 220 ft upstream of the work area. The taper redirected northbound traffic on Wildwood Road into the right lane while the arrow board displayed an appropriate pattern indicating the merge direction to oncoming vehicles. A standard 25-light arrow board was acquired and equipped with a data collection system that included a camera and radar sensor. The arrow board was then positioned into the existing work zone. The data collection system was programmed to automatically record video and radar data twice per day: 10:00 a.m. to 11:00 a.m. (i.e., morning) and 4:00 p.m. to 5:00 p.m. (i.e., afternoon). A different display pattern was used each week, with data collection occurring for five consecutive days (Sunday through Thursday) per display pattern. A supplemental data collection system was also placed upstream of the work zone to capture baseline speed data for traffic prior to when the arrow board was visible. Video from the arrow board was reduced to determine the number of vehicles in each lane as they approached the arrow board, as well as the distance at which vehicles merged from the closed lane into the open lane relative to the arrow board. Radar data from the arrow board was also reduced to isolate speed measurements to only vehicles traveling on Wildwood Road that ultimately passed the arrow board leading into the work zone. Vehicles that turned or entered from an intersecting street were excluded from analysis. Results of the study showed that time of day (i.e., morning vs. afternoon) had a significant effect on measures of traffic volume and lane occupancy rate. Traffic volume was found to be significantly higher during the afternoon (105 vehicles per 10 min) than in the morning (64 vehicles per 10 min). In contrast, the lane occupancy rate (i.e., the rate of vehicles traveling in the closed lane) was significantly higher in the morning (7.6%) than in the afternoon (5.1%). The arrow board display pattern was not significant for either traffic volume or lane occupancy rate. Neither the display pattern nor time of day had a significant effect on the distance at which vehicles changed from the closed lane into the open lane. However, the interaction between the two was approaching statistical significance (p = 0.07). Mean lane change distances ranged between 296 ft and 343 ft across all display patterns and times of day. However, an evaluation of the vehicle merge rate by distance revealed interesting differences among the display patterns. During the morning, the flashing arrow pattern consistently had the lowest proportion of vehicles using the closed lane at all distances, while the sequential chevrons had the highest proportion. During the afternoon, the sequential arrow had a consistently lower proportion of vehicles remaining in the closed lane across all distances, while the flashing arrow and sequential chevrons performed worse but similarly to each other.
Assessment of Drowsiness Using the Johns Drowsiness Scale (JDS): An Objective, Continuous Index Using Eyelid Movement Measures
Llaneras, Robert E.; Meyer, Jason E.; Barnes, Ellen (National Surface Transportation Safety Center for Excellence, 2026-03-05)
A major issue within the automotive industry has been the development of suitable and valid measures and metrics for assessing driver impairment due to drowsiness (or fatigue) under real-world environments and operating conditions. This study evaluated assessments of the Johns Drowsiness Scale (JDS)—an algorithmic index using objective eyelid movement measures to continuously scale level of impairment resulting from drowsiness. The goal of this project was to relate JDS to other known indices of drowsiness (PERCLOS [PERcentage of eye CLOSure], Observer Rating of Drowsiness [ORD], Karolinska Sleepiness Scale [KSS], standard deviation of lane position, lane deviations, etc.) with the expectation of developing evidence-based measures, criteria, and thresholds to identify the onset and progression of drowsiness. This work also sought to assess JDS’s predictive power in order to characterize if the system provides advance notice of impairment beyond what is currently available. Data for this project was leveraged from a previously collected on-road instrumented vehicle study (unpublished), involving 61 drivers, and following an approach designed to induce drowsy driving episodes. Researchers at the Virginia Tech Transportation Institute sampled data from 25 drivers who completed that study, and their data was analyzed to derive JDS scores. The research did not target sleep-deprived individuals but rather normally rested individuals under the assumption that low-workload environments could be used to induce drowsiness. Driver workload, or task demand, was lowered by asking participants to refrain from engaging in secondary tasks (e.g., listening to the radio, interacting with their cell phone, conversing with passengers, etc.) during the trip; drivers also engaged the cruise control and were asked to minimize lane changes. The route was also specifically selected to be boring with limited traffic. The study protocol was extremely successful at inducing drowsiness under real-world driving conditions, with 82% of drivers indicating they experienced high levels of drowsiness during the trip, achieving a KSS rating of either 8 (sleepy, some effort to keep alert) or 9 (very sleepy). Early signs of drowsiness (driver KSS rating of 6) tended to occur, on average, within the first 30 minutes of the trip and fully drowsy levels (driver KSS rating of 9) within 45 minutes. Results showed that JDS scores were moderately related to PERCLOS, driver and researcher KSS ratings, as well as ORD scores. Time-to-drowsy (TTD) calculations were explored using a variety of measures, including JDS, and triggering thresholds to better understand drowsiness timelines. TTD was found to vary substantially depending on the specific measure of interest and the designated threshold value, with onset averaging as short as 13 minutes to as long as 57 minutes. JDS, PERCLOS, and driver KSS ratings tended to reach threshold drowsy levels in advance of observable driver impairment indexed by driving performance (first drowsy-related lane deviation). For instance, approximately 80% of drivers reached a JDS score of 7 in advance of their first lane departure—on average, 11.66 minutes prior to the lane departure. Similar results were found for PERCLOS, and to a lesser extent driver KSS. Both JDS scores and PERCLOS were found to have relatively high “hit” rates (correct assessments of drowsiness) when using ORD scores as ground truth. JDS yielded somewhat lower miss rates relative to PERCLOS. Study results suggest that JDS is a promising measure that may serve as a sensitive and leading indicator of drowsiness.
Investigating OpenPilot as a Research Tool
Miller, Marty (National Surface Transportation Safety Center for Excellence, 2026-03-05)
This report investigates the feasibility of using OpenPilot, an open-source driver assistance system, and a Comma 3, the hardware that runs this software, as a research tool for data collection and analysis at the Virginia Tech Transportation Institute (VTTI). The study explores OpenPilot’s data logging capabilities, camera views, and driver monitoring assessments while identifying potential applications and challenges for research integration. OpenPilot, when paired with Comma 3/3X hardware, offers real-time logging of vehicle kinematics, GPS data, and video feeds, making it a potential alternative to traditional data acquisition systems. This study successfully developed methods to extract and integrate OpenPilot data into VTTI’s research pipeline, enabling seamless analysis of driver behavior and advanced driver assistance system performance. A pilot evaluation demonstrated OpenPilot’s ability to collect high-quality data that is suitable for analyzing Level 2 systems usage, driver-initiated disengagements, and driver monitoring system (DMS) effectiveness. The study also conducted a structured assessment of OpenPilot’s DMS, revealing that while it effectively detects gross head movements, it struggles to identify subtle eye glances, limiting its reliability for distraction research. Additionally, concerns regarding data privacy and storage limitations on newer Comma devices present potential barriers to large-scale deployment. Despite these challenges, OpenPilot shows promise as a research tool with further development. Ongoing work at VTTI aims to address data privacy and storage issues while leveraging OpenPilot for the study of an intelligent speed assist system. With continued refinement, OpenPilot and Comma 3/3X devices could become valuable assets for cost-efficient driving data collection and advanced driver assistance system research.
Physiological Sensing for Driver State Monitoring: Technology Scan and Pilot Evaluation
Jain, Sparsh; Perez, Miguel A. (National Surface Transportation Safety Center for Excellence, 2026-03-04)
As advanced driver assistance systems and automated vehicle technologies evolve, the ability to monitor and assess driver readiness remains critical. In support of that need, this report describes several efforts to evaluate the feasibility and reliability of capturing physiological signals during real-world driving. The goal was to examine whether signals from respiration, cardiac activity, and brain function, captured via wearable and non-contact sensors, could complement existing driver monitoring methods and provide useful input for future in-vehicle systems. A review of the literature supporting respiration, cardiac, and brain activity as relevant domains for driver state monitoring was the initial step in this process. These physiological channels are discussed as potentially useful complements to traditional measures like eye behavior, especially given their links to autonomic and cognitive changes under various degraded states. The literature review was complemented by a technology scan of commercial and research-grade devices capable of measuring these signals in mobile contexts. A structured protocol for exercising an initial subset of these systems during impaired driving in a closed-course environment was also developed. This protocol was then executed in a pilot test with five participants, who completed standardized baseline and post-alcohol drives while instrumented with electrocardiogram (ECG), respiration, and electroencephalography (EEG) sensors in addition to sensors capturing driving performance and glance behavior. In this pilot study, alcohol was used as a convenient physiological stressor given its well-understood dosage effects on driving and physiology. Results indicated that alcohol consumption consistently altered some behavioral physiological signals across all three domains. Physiological responses were more robust and consistent than the observable driving metrics, potentially highlighting the complementary value of these signals. The small sample size, however, resulted in a lack of power to detect statistical significance between sober and impaired driving for most metrics. Respiration and ECG signals were captured with high reliability, while EEG results provided informative patterns but suffered from variable signal quality and motion artifacts. These findings support the initial viability of cardiac and respiratory sensing in mobile in-vehicle settings and highlight practical limitations that must be addressed for EEG. Altogether, the effort demonstrates that it is technically feasible to capture and interpret physiological signals in a real-world driving context using wearable and embedded sensors. While further validation is needed, the results provide a foundation for integrating such signals into future driver state monitoring systems, not as standalone indicators, but as part of a multimodal approach that reflects the complexity of driver physiology and behavior.
Training Drivers on L2 Automated Systems: A Pilot Study for Developing Effective Training that Drivers Will Use
Linkous, Taylor Y.; Greatbatch, Richard; Hankey, Jonathan M.; Horrey, William J.; Tefft, Brian C.; Klauer, Shiela (National Surface Transportation Safety Center for Excellence, 2026-03-04)
Partially automated systems, also known as Level 2 (L2) automated systems or advanced driver assistance systems (ADAS), are becoming increasingly common in the U.S. vehicle fleet, and ubiquitous on new vehicles. The Highway Loss Data Institute estimates that more than 28% of registered vehicles in the United States in 2023 were equipped with automatic emergency braking technology and that more than 90% of model year 2023 new vehicle series included automatic emergency braking as a standard or optional feature. Considering the increasing proliferation of vehicles equipped with L2 partial driving automation, it is important to ensure that drivers sufficiently understand the systems to support safe and appropriate use. Evidence suggests that proper understanding of L2 automated system leads to safer interactions with the systems , and that formal instruction produces greater understanding than trial and error alone. Additionally, engaging adults in learning about a new technology may require distinct design considerations well as motivational frameworks. Given drivers’ clear affinity for trial and error and the importance of relevance in the motivation model, it seems plausible that drivers might be more likely to engage with formal training if it is offered inside the vehicle itself, so that they could access training at the place and time when they seek to learn/understand/use the vehicle systems. However, practically, it remains unknown whether drivers would engage in training to gain understanding of these new systems, even if it were readily available in the vehicle.
Rider Insights on Motorcycle Safety Tech: What Drives—or Blocks—Adoption of ABS, MSC, and AEB
McCall, Robert; Williams, Vicki (National Surface Transportation Safety Center for Excellence, 2026-03-04)
Advanced Rider Assistance Systems (ARAS) have multiple benefits to motorcycle riders, auto manufacturers, individual states, and any person interested in reducing roadway fatalities. ARAS technologies are a promising approach to reducing fatalities by preventing the crashes that cause them. Three technologies were selected for study utilizing a survey-based approach to understand riders’ attitudes toward their use. Anti-lock brakes (ABS) represent an existing and mature technology likely encountered by many riders. Motorcycle Stability Control (MSC) is a technology present on several high-end motorcycles and uses braking to stabilize the motorcycle in a curve. Automatic Emergency Braking (AEB) is a nascent technology in the motorcycle world but has existed since the early 2000s in the passenger vehicle domain. This study surveyed 1,391 licensed riders and sought to accomplish four objectives related to understanding attitudes toward ARAS, gaining specificity behind these attitudes, understanding barriers, and overcoming those barriers. Overall, motorcycle riders recognize that ARAS improve safety, but adoption is slowed by cost, misunderstanding, and concerns about riding identity. These barriers are solvable. Through hands-on experiences, education, and incentives, stakeholders can accelerate ARAS adoption, improve rider safety, and drive market growth.
Status and Challenges of Level 3 Automated Driving Systems
Hua, Lesheng; Antona-Makoshi, Jacobo; Neurauter, Luke (National Surface Transportation Safety Center for Excellence, 2025-08-12)
This report presents an assessment of the current state of SAE Level 3 (L3) Automated Driving Systems (ADS), highlighting key global regulatory developments, market deployments, and public attitudes, while critically examining the latest safety-relevant scientific evidence and its implications for system design, human interaction, and the continued evolution and deployment of these technologies. The report covers the global regulatory landscape, commercially available L3 systems, user trust and acceptance, human-machine interfaces, transition of control, driver situation awareness, and reasonably foreseeable driver misuse of L3 systems.
Data-Driven Characterization of Motorcycle Riders’ Kinematics and Crash Risk
Terranova, Paolo; McCall, Robert; Perez, Miguel A. (National Surface Transportation Safety Center for Excellence, 2025-08-05)
This effort was successful in exploring different motorcycle riding styles using multiple, commonly available sensors, ultimately associating those results with potential crash events. More specifically, the analysis carried out in this work provides novel insights into real-world motorcyclist behavior by identifying three distinct riding profiles characterized by unique kinematic patterns. Furthermore, several potential kinematic indicators that may predict crash risk were identified. This enhanced characterization of motorcycle rider capabilities could enable more realistic crash scenario simulations, inform evidence based safety policies, and support the design of advanced rider assistance systems that leverage real-world parameters. In this study, riding styles and their relationship with crash and near-crash (CNC) risks were investigated using naturalistic riding study data from 155 participants over an average period of 11 months. The data, predominantly from southern California, includes approximately 400,000 miles of riding, providing extensive insight into real-world motorcycle riding behaviors. The research addresses two main questions: 1. What defines the normal riding behavior of a motorcycle? Can it be categorized in terms of riding style? 2. What measures of rider performance are associated with crash risk? Kinematic data collected from the instrumented motorcycles was processed to remove artifacts and noise prior to analysis. The analytical approach identified three distinct riding style clusters through principal component analysis and K-means clustering. The first cluster of 24 participants was primarily composed of younger participants using sport motorcycles. These subjects exhibited higher accelerations, abrupt braking, and substantially higher roll angles more frequently than other clusters. Additionally, this group showed significantly higher CNC rates than other clustered riders. Cluster 2 exhibited typical riding behavior, with moderate acceleration, braking, and roll rate values across a mix of motorcycle types and rider ages. In contrast, the riding behavior of the third cluster was smoother, with less aggressive maneuvers and safer distances during car-following scenarios. This third cluster was mainly composed of older participants primarily riding cruiser motorcycles. Statistical analysis revealed that age and motorcycle type significantly differentiated these clusters, whereas gender, riding experience, and formal training had minimal impact. A bootstrap analysis comparing CNC trips against trips without a CNC (i.e., baseline trips) identified abrupt initial jerk during braking and accelerating maneuvers as a significant indicator of elevated crash risk, underlining the critical role of anticipatory and reactive behaviors in rider safety. These findings offer critical insights for targeted rider education, motorcycle design, safety policy formation, and evaluation and design of advanced rider assistance systems. This enhanced understanding of motorcycle kinematic metrics and their linkage with crash risk can also inform insurers as they develop more precise, data-driven telematics-based rate structures.
New Tech Educational Outreach
Baker, Stephanie Ann; Levin, Jacob; Trimble, Tammy E.; Giurintano, Amelia; Bell, Stephen (National Surface Transportation Safety Center for Excellence, 2025-07-10)
The impacts of transportation on human health and safety may be addressed at least in part by new vehicle technologies such as advanced driver assistance systems (ADAS) and electric vehicles (EVs). To reap the safety and health benefits of these technologies and overcome barriers to adoption, the driving public needs to understand the benefits of these technologies, their limitations, and how to properly use them. One way to address barriers to proper use and adoption of these new vehicle technologies is through educational outreach. The objectives of this project were to gather information and materials from current EV educational outreach efforts and the National Highway Traffic Safety Administration’s (NHTSA’s) ADAS educational outreach program to inform the development of a New Tech Outreach (NTO) educational outreach program and to identify potential partners for a future effort. The approach to conducting the project involved two steps. As a first step, the project team reviewed materials from NHTSA’s ADAS educational campaign among others and conducted a scan of current literature on EV educational outreach. The second step was reviewing three EV educational outreach programs being conducted by public-facing organizations to include as examples of how to conduct EV educational outreach. As part of the program review, the project team reviewed program websites and key reports, attended events, and interviewed a program lead from each program. Interviews, which were conducted between May 1 and November 14, 2024, covered a range of topics including program goals, target audiences, topics, approaches, funding, partnerships, implementation barriers, and lessons learned. Each interview also explored future opportunities for collaboration and partnership not only with the Virginia Tech Transportation Institute (VTTI) but for the types of organizations represented by the National Surface Transportation Safety Center for Excellence. A set of key takeaways was identified, including potential partnership opportunities. The key takeaways are not an exhaustive list of everything reviewed or generalizable findings on this topic; rather, they are meant to serve as a summary of what the project team considers to be potential inputs to the design of a future NTO program. Throughout the takeaway discussion, resources that the project team may use or reference in a future NTO program are highlighted. In addition, an important aspect of this project is how VTTI can partner with organizations such as those included here in a future NTO program. Working with universities in general and VTTI specifically was discussed during each program review, and numerous suggestions were made for how such collaboration can occur in the future. As a next step, the report concludes with a suggestion and basic outline for an NTO pilot project.
Parents’ Usage of Commercially Available Mobile Phone Applications for Teen Drivers: What Is Working?
Young, Taylor C.; Bedwell, Kaitlyn E.; Anderson, Gabrial T.; Klauer, Sheila G. (National Surface Transportation Safety Center for Excellence, 2025-07-09)
Recent years have shown increasing popularity in a particular type of mobile phone application, or app: parent-teen driver performance monitoring apps. A growing body of research suggests that feedback and post hoc intervention of teen driving can be used as a tool to influence behavioral changes in driving. Commercially available apps for smartphones that incorporate telematics data (i.e., vehicle speed, hard braking, and GPS location) are a trending way for parents to be able to track their teens’ driving. Previous research provides many insights into how teens perceive driver monitoring apps and what role these apps play in improving driving behavior (Gesser-Edelsburg & Guttman, 2013; Peer et al., 2020). Teen driver monitoring apps can provide teens with tangible proof of their driving behavior that either demonstrates positive safe driving behavior or targets areas where constructive criticism on risky behavior is merited. Teens often feel driver monitoring apps are a more objective and unbiased way to monitor their driving with evidence compared to their parents’ perceptions. On the other hand, these apps can also be viewed by teens as an extension of parental supervision, as well as an invasion of privacy and a restriction of their independence. Parents greatly influence their teens’ behavior whether they are behind the wheel or not. Parents have a unique role as the main enforcers for what the states may require for licensing rules. Parents have their own views regarding the driving risks that their teens face and may use these driver monitoring apps in different ways. This project aimed to understand what information parents of teen drivers want to see and use on a driving monitoring app and what they find useful for enabling the most effective feedback relationship with their teen. The research team worked with stakeholders to develop survey tools to help better understand parents’ and teens’ attitudes, preferences, and needs regarding app-based driving feedback. The survey was administered by State Farm using their survey software (Suzy) to collect data nationally, including research participants from U.S. territories. Suzy can filter by gender, age, employment, education, income, and location. The questions took different forms, including multiple choice, Likert scales, open ended, and ranking questions. A total of 649 responses were received. It was found that parents generally check monitoring apps the most during the following conditions: locating their teen, situations at certain times of the day, or when they know their teen is driving through bad weather. Parents are using monitoring apps to know where their teen is located, to see if their teen is speeding while driving, and to see the location where the teen is driving. Parents responded that the alerts they prefer from the monitoring apps include crash alerts, speeding alerts, and safe arrival notifications.
Weather Characterization
Palmer, Matthew; Stowe, Loren (National Surface Transportation Safety Center for Excellence, 2025-07-03)
The Virginia Tech Transportation Institute (VTTI) successfully developed and deployed a mobile weather characterization system aimed at enhancing transportation safety research at the Virginia Smart Roads facility. This sensor was used to characterize a sampling of the water-based, VTTI simulated weather at the Virginia Smart Roads Facility. A Parsivel2 disdrometer was mounted on a vehicle to measure precipitation particle size distribution and falling velocity. The mobile nature of the system enables efficient data collection along the entire roadway section. Using the sensor, a rain characterization study revealed that the rain produced by the facility showed variability in droplet size distribution, with deviations from natural rain patterns. The limited fall height (10 meters) led to lower terminal velocities than naturally occurring rainfall, which usually fits the Gunn-Kinzer relationship. With respect to the Marshall-Palmer relationship, the VTTI rain represents stratiform rain distribution more than convective rain. Wind was found to have a bigger effect on measurement accuracy due to the sensitivity of the sensor. A snow characterization study revealed challenges in correlating liquid water equivalents measured to actual snow depth due to variability in snow density and particle orientation of the VTTI-produced, water-based snow. The disdrometer software assumes the snow density to calculate the liquid water equivalent. The addition of a heated precipitation gauge could enhance accuracy. Operationally, the study found that calibrating weather towers by pressure, rather than visual estimation, improved the consistency of rainfall production. However, issues such as hose kinks impacted flow rates, indicating areas for infrastructure improvements. Recommendations for future work include enhancements such as wind sensors, articulating mounts, and longer duration testing under various wind conditions are recommended to improve weather characterization fidelity.
Naturalistic Driving Study on Cannabis Use in Washington and Virginia
Bedwell, Kaitlyn E.; Jain, Sparsh; Young, Taylor C.; Perez, Miguel A.; Hankey, Jonathan M. (National Surface Transportation Safety Center for Excellence, 2025-07-11)
This study examined the consumption behavior of cannabis users and its influence on driving performance through a comprehensive naturalistic driving study (NDS) conducted in Washington and Virginia. The study aimed to address gaps in research by leveraging real-world data to evaluate how cannabis consumption impacts driver behavior, safety-critical events, and crash risk. The study’s objectives included assessing the prevalence of driving under the influence of cannabis (DUIC), examining variations in impairment across different consumption methods and doses, and exploring the relationships between self-reported intoxication levels and objective performance metrics. Participants were selected based on their regular cannabis use and self-reported DUIC history. Data was collected via in-vehicle instrumentation, a smartphone-based journal app, breathalyzer readings, and oral fluid tests. The study offered key insights into the impact of self-reported cannabis consumption on driving behavior, with cannabis trips occurring alongside sober trips with similar frequency and temporal distributions. Self-reported substance use data revealed that cannabis consumption methods differed between regions, with dabs being the preferred form in Washington and smoking cannabis flower (e.g., joints, pipes, bowls) dominating in Virginia. Polysubstance use, particularly with alcohol, was prevalent, with 13.7% of Washington and 20.1% of Virginia journal entries involving multiple substances. Breathalyzer data showed that 20% of Washington’s and 14.5% of Virginia’s alcohol-positive trips exceeded the 0.08% blood alcohol concentration (BAC) limit. Quantisal oral fluid tests highlighted variations in tetrahydrocannabinol (THC) levels, with mean delta-9 THC levels significantly higher in Washington (1,662 ng/ml) compared to Virginia (260.9 ng/ml). While 85% of Quantisal tests were successfully submitted, challenges such as outlier THC readings due to participant noncompliance with testing protocols were noted, indicating the complexity of linking subjective impairment levels to objective performance metrics. The findings highlight the complexity of DUIC and the need for further research to inform public policy, law enforcement practices, and safety guidelines. The dataset provides a valuable resource for understanding cannabis-related driving risks and developing targeted interventions. Further analyses should explore the nuanced effects of cannabis potency, user tolerance, and polysubstance interactions on driving performance. Enhanced data collection techniques could improve the reliability of future studies.
Pilot In-Vehicle Carbon Monoxide Detector Study
Manke, Aditi; Hicks, Pat; Hankey, Jonathan M. (National Surface Transportation Safety Center for Excellence, 2025-03-28)
This study addresses the critical issue of carbon monoxide (CO) exposure in truck cabins, particularly in vehicles used for work zones. The research explores the levels of CO within these confined environments, with the objective of identifying factors that could contribute to increased CO levels. Two Truck Mounted Attenuators equipped with CO sensors and data acquisition systems were monitored under real-world operational conditions from July to December 2023. The study shows that average in-cabin CO levels across the two vehicles were generally low, 1.22 ppm in Truck 1 and 1.61 ppm in Truck 2. There were occasional spikes, with levels reaching 10.05 ppm in Truck 1 and 8.59 ppm in Truck 2. These peaks occur during specific operational scenarios, such as prolonged idling, open windows, and acceleration near traffic congestion. The findings highlight the significance of both environmental factors (e.g., proximity to exhaust sources, ventilation efficiency) and operational behaviors in influencing CO exposure. The analysis showed some patterns: CO levels were lowest during motion (1.14 ppm in Truck 1, 1.43 ppm in Truck 2), attributed to improved air circulation. But when parked on the road, levels rose to 1.63 ppm and 1.98 ppm, likely from idling and nearby traffic emissions. In controlled environments, such as parking facilities, CO levels stayed consistently low. These findings support prior studies that emphasize the impact of ventilation settings and driver practices on air quality (Dirks et al., 2018; Marinello et al., 2023). The study highlights the role of vehicle maintenance and design in mitigating CO exposure. Older vehicles with compromised exhaust systems and poor ventilation settings worsen the in-cabin pollution levels. To minimize risks, real-time CO monitoring and regular maintenance are essential. Additionally, educating drivers on best practices, such as limiting idling and optimizing ventilation modes, can significantly reduce exposure. While the study provides valuable insights, it is limited by its sample size (two vehicles) and duration (39 operational days per truck), which may not capture seasonal variations or represent broader fleet conditions. Future research should include more vehicle types, longer study periods, and additional factors like weather and window positioning to provide a more complete picture. Overall, the research highlights the need for targeted interventions in truck cabin air quality management. Practical steps include upgrading ventilation systems, integrating CO detection technology, and implementing urban planning measures to cut down on traffic-related exposure. By focusing on these strategies, industry leaders can enhance driver safety and well-being while also contributing to broader public health improvements.
Effectiveness of Wearable Devices to Study Driving Stress of Long-haul Truck Drivers in Naturalistic Driving Systems
Thapa, Surendra Bikram; Sarkar, Abhijit (National Surface Transportation Safety Center for Excellence, 2025-03-21)
Advancements in wearable technology, driven by innovations in artificial intelligence and the Internet of Things, have significantly expanded our ability to monitor health and safety in various domains, including transportation. In this age of big data, the continuous collection and analysis of physiological data from wearable devices has opened new avenues for enhancing road safety and driver well-being. This report investigates the feasibility and effectiveness of using wearable technology to monitor fatigue and stress levels among long-haul commercial motor vehicle drivers. The goal of this research is to reduce risks associated with drowsy driving, which is a significant contributor to road accidents worldwide (M. Islam & Mannering, 2023). Wearable technologies, such as the Empatica EmbracePlus smartwatch, offer a promising approach to real-time health monitoring by providing continuous insights into drivers’ physiological states. This study was designed to evaluate the capability of these devices to detect early signs of fatigue and stress, understand the various factors affecting a driver’s well-being, and identify strategies to manage these issues effectively. A repeated measures study design was implemented, collecting comprehensive health data from a sample of 10 long-haul drivers over a 5-day period (i.e., 1 work week), with one driver providing additional data over an extended 4-week period. Data collection involved continuous monitoring of physiological signals, such as heart rate variability and electrodermal activity, supplemented by self-reported information on stress levels, traffic conditions, diet, and other relevant variables through daily questionnaires. The findings from this study highlight the potential of wearable technology to transform driver safety and health management practices. The data collected provided valuable insights into the drivers’ daily experiences and behaviors, revealing patterns related to stress levels, dietary habits, hydration practices, and coping mechanisms. Most participants experienced mild to moderate stress, influenced significantly by traffic conditions and driving durations. The report indicates that wearable technology can provide key insights by enabling continuous monitoring of fatigue and stress levels; this then suggests a potential for early alerts for necessary breaks and prevention of accidents due to drowsy driving. Furthermore, the data generated by these devices can be used to develop personalized interventions that can improve drivers’ health and work conditions. For successful implementation, it is important to address concerns regarding data privacy and usability while creating an environment that encourages the adoption of such technology. Encouraging awareness about the applications of wearable devices and their capabilities in monitoring health information could create such an environment. Future research should focus on refining wearable technologies to enhance user comfort, maintain data security, and explore broader applications within transportation safety related to long-haul drivers.
Pediatric Vehicular Hyperthermia Injury: Feasibility of Data Collection
Glenn, Laurel; Glenn, Eric; Perez, Miguel A. (National Surface Transportation Safety Center for Excellence, 2025-02-07)
Pediatric vehicular hyperthermia (PVH) remains a critical public health issue, characterized by the rapid and dangerous increase in a child’s body temperature when left in a hot vehicle. Despite public awareness campaigns and legislative efforts, PVH continues to account for an average of 37 fatalities annually in the United States. PVH cases are a combination of complex situations involving the unique vulnerability of children to hyperthermia and caregiver memory lapses, intentionally leaving a child unattended, and children gaining access to vehicles. The research conducted aimed to assess the feasibility of collecting detailed data on non-fatal PVH cases, which are currently underreported and poorly understood. This investigation utilized interviews with personnel from a variety of organizations likely to be involved in PVH incidents, such as police departments, fire departments, emergency medical services (EMS), and hospitals. The findings revealed critical gaps in the existing data collection systems that impede accurate tracking and reporting of PVH events. None of the interviewed organizations had specific data fields to capture PVH cases, leading to the reliance on narrative fields, which are inconsistent and subjective. This research hence highlights the need for the implementation of required, standardized data fields across national databases, such as the National EMS Information Systems (NEMSIS) and the National Fire Incident Reporting System (NFIRS), as well as within hospital coding systems. Furthermore, the addition of a specific International Classification of Diseases (ICD) code for PVH is recommended to facilitate more accurate case tracking once medical organizations are involved. Improved data collection and reporting would provide a clearer understanding of the prevalence of PVH and guide more effective public health interventions and legislative actions.
Preparing First Responder Stakeholders for ADAS and ADS Deployments
Trimble, Tammy E.; Faulkner, Daniel (National Surface Transportation Safety Center for Excellence, 2024-12-16)
Previous research has found that public safety providers are unclear about the capabilities associated with advanced driver assistance systems (ADAS)- and Automated Driving System (ADS)-related technologies. Providing outreach to this population will reduce uncertainty regarding these technologies, which in turn will lead to improved safety and interactions, including crash documentation, while in the field. A training curriculum was developed that consisted of two parts: (1) a classroom portion which can be delivered in-person or online and (2) a hands-on experiential portion. Two training options were presented to local agencies: (1) an approximately 1-hour online session, to be held at the agency’s convenience, which covers the prepared training materials; and (2) an in-person, half-day session which covers the prepared training materials and provides exposure to ADAS- and ADS-equipped vehicles. Recruitment efforts resulted in five in-person and six online attendees. In-person attendees represented three separate organizations, with one organization being represented by officers from three locations. The online attendees represented six separate organizations. Only one organization had an attendee in both the in-person and online options. To better understand the time to be allotted for the online training, the in-person training was held first. As a result, the online training was ultimately extended to 1.5 to 2 hours, which allowed time for discussion throughout the training. Feedback received directly from the participants at the conclusion of the training and via the online questionnaires was overwhelmingly positive. Moving forward, the training materials will need to be updated on a continual basis to ensure the ongoing timeliness of information shared. To share the materials with a wider range of individuals, the training could be developed and shared in a manner like the Virginia Tech Transportation Institute’s (VTTI’s) Sharing the Road program, where VTTI representatives visit schools to provide information and hands-on encounters to promote safely sharing the road with large trucks. A key to success will be employing individuals with first responder experience to provide the training. Feedback suggested that those with hands-on experience combined with their ties to VTTI resulted in perceived credibility. Also, providing hands-on opportunities to see variations in technologies across vehicle models and applications was considered beneficial. Working with VTTI partners, it may be possible to obtain demonstration vehicles for this purpose. Through this development process, the team can work towards accreditation and providing the training as part of academy, in-service, or regional training days.
Using Artificial Intelligence/Machine Learning Tools to Analyze Safety, Road Scene, Near-Misses and Crashes
Yang, Gary; Sarkar, Abhijit; Ridgeway, Christie; Thapa, Surendrabikram; Jain, Sandesh; Miller, Andrew M. (National Surface Transportation Safety Center for Excellence, 2024-11-18)
Artificial intelligence (AI) and machine learning technologies have the potential to enhance road safety by monitoring driver behavior and analyzing road scene and safety-critical events (SCEs). This study combined a detailed literature review on the application of AI to driver monitoring systems (DMS) and road scene perception, a market scan of commercially available AI tools for transportation safety, and an experiment to study the capability of large vision language models (LVLMs) to describe road scenes. Finally, the report provides recommendations, focusing on integrating advanced AI methods, data sharing, and collaboration between industry and academia. The report emphasizes the importance of ethical considerations and the potential of AI to significantly enhance road safety through innovative applications and continuous advancements. Future research directions include improving the robustness of AI models, addressing ethical and privacy concerns, and fostering industry-academic collaborations to advance AI applications in road safety.
Assessing Factors Leading to Commercial Driver Seat Belt Non-Compliance
Camden, Matthew C.; Soccolich, Susan A.; McSherry, Thomas; Ridgeway, Christie; Stapleton, Steven (National Surface Transportation Safety Center for Excellence, 2024-10-24)
The current research study utilized a literature review and analysis of two data sources to determine situational factors associated with reduced seat belt usage among CMV drivers. The literature review identified characteristics of seat belt use, reasons drivers may or may not use seat belts, methods to improve seat belt use rates, and important gaps in the literature. The data analysis used data collected in two separate studies to assess seat belt use rates and explore the relationship between seat belt use and environmental, roadway, vehicle, and driver factors. The first study collected observational data in 2015 from multiple sites in Michigan with high rates of truck/bus-involved crashes. The second study collected naturalistic driving data during the Federal Motor Carrier Safety Administration’s Advanced System Testing Utilizing a Data Acquisition System on Highways (FAST DASH) second Safety Technology Evaluation Project (commonly referred to as FAST DASH 2). The naturalistic driving data set included safety-critical events (SCEs), which were reduced for driver behaviors and environmental and roadway information. In the current study, driver seat belt use was observed in 93% of the FAST DASH 2 naturalistic driving SCEs and in 81% of SCEs in the observational data set. The analysis of observational and FAST DASH 2 naturalistic driving study data identified several factors where seat belt use patterns changed significantly across the factor levels; however, the analyses for each data set did not show consistency in statistical significance. The observational data showed seat belt use to be associated with day of week, time of day, road type, truck type, and fleet type. Little correlation was found between seat belt use and other driver behaviors. The analysis of observational study data did find seat belt use to be significantly higher in observations where drivers were using a hands-free cell phone with earpiece compared to drivers not using a cell phone or talking on a handheld cell phone. The naturalistic driving data showed that drivers operating on divided highways had higher seat belt use compared to those driving on non-physically divided roadways.
Equity in Transportation Safety
Robinson, Sarah; Medina, Alejandra; Gibbons, Ronald B.; Kassing, Andrew; Myers, Bradley (National Surface Transportation Safety Center for Excellence, 2024-09-24)
Equity in transportation is a key issue for the Federal Highway Administration (FHWA), as well as state departments of transportation. Equitable transportation ensures safety for all road users across all modes of transportation for all communities. FHWA recommends the adoption and equitable application of a safe system approach to achieve Vision Zero objectives to eliminate traffic fatalities and severe injuries. A safe system fundamentally recognizes human error and accounts for it when designing systems and operations. Incorporating equity into roadway safety data is critical for conducting data-driven safety analysis. FHWA recommends collaboration with underserved communities through a process of collecting and analyzing data, engaging community representatives, implementing improvements, and evaluating impacts. Ensuring robust and accurate data is critical. State programs have worked to incorporate a wide variety of data into their crash models. Social and demographic data such as race, ethnicity, gender, age, education, employment status, income level, disability status, among many other variables, have been evaluated and demonstrated to be factors in the frequency of crashes. States have published mapping tools to visualize data trends and identify locations for targeted implementation efforts in conjunction with scoring metrics for evaluating proposed solutions.

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