Browsing by Author "McClafferty, Julie A."

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    100-car reanalysis: summary of primary and secondary driver characteristics
    McClafferty, Julie A.; Hankey, Jonathan M. (National Surface Transportation Safety Center for Excellence, 2010-08-27)
    This project's goal was to build a complete trip file inventory for the 100-Car data set... Data points collected for each file include Driver ID (with new IDs created as new secondary drivers were found), Ambient Lighting, Driver Seatbelt Usage, and an assessment of video operations/quality. --executive summary.
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    An Assessment of the Biological and Socioeconomic Feasibility of Elk Restoration in Virginia
    McClafferty, Julie A. (Virginia Tech, 2000-02-09)
    The biological and socioeconomic feasibility of restoring elk (Cervus elaphus) to Virginia was assessed. Biological feasibility was determined by evaluating habitat suitability for elk while considering potential impacts of elk on existing fauna and flora in Virginia. Suitability was assessed by creating a habitat suitability index (HSI) model that measured the availability and accessibility of open foraging areas and forested cover areas, the availability of permanent water sources, and the degree of fragmentation by roads. Eight areas were identified as potential elk habitat: 1 in Southwest Virginia, 4 in the Shenandoah Mountains (Shenandoah, Highland, Big Meadows, Peaks of Otter), and 3 in the Southern Piedmont (Danville, Brookneal, Rehobeth). The highest potentials for supporting an elk herd were found in the Highland and Big Meadows study areas, medium biological feasibilities were found in the Southwest, Shenandoah, and Brookneal study areas, and low biological feasibilities were found in the Peaks of Otter, Danville, and Rehobeth study areas. A restored elk herd could negatively affect indigenous fauna and flora by changing the structure and diversity of existing forested ecosystems, but impacts can be minimized by maintaining elk populations at or below cultural carrying capacity. The introduction of diseases during restoration and possible transmission of those diseases from elk to humans, livestock, and other wildlife also are concerns, but these issues can be addressed by following a risk minimization protocol. Socioeconomic feasibility was assessed with a statewide mail survey of Virginia residents, 4 regional stakeholder workshops, an analysis of economic costs and benefits associated with elk restoration, and an assessment of the risks of elk-human conflicts in each of the 8 study areas. Overall, most (61%) respondents agreed that elk restoration would be good for Virginia. However, the low response rate (30%) and low confidence among respondents (49%) in their knowledge about elk indicated that most residents do not have the interest and/or necessary information to form a definitive opinion. Residents believe that the greatest benefits of restoration would be the value-based and indirect ecological benefits, such as returning an extirpated species to its native range, whereas the greatest perceived costs were the economic impacts to property, crop depredation, and public safety hazards. In contrast, local stakeholder representatives identified economic returns from increased tourism due to the presence of elk and the creation of new recreational opportunities as the most anticipated benefits; important concerns were the potential for property damage by elk, the potential impacts on local ecosystems, and the costs of implementing and administering an elk restoration program and subsequent elk management. Proposed resolutions for these issues varied by region. Representatives from the Southwest and northern Shenandoah Mountain (Shenandoah and Big Meadows study site) Regions preferred not to restore elk, whereas those from the southern Shenandoah Mountain (Highland and Peaks of Otter study site) and the Southern Piedmont Regions preferred to start out small with a carefully controlled and monitored "experimental" population. Economic benefits of elk restoration, as determined through analysis of data from other eastern states currently managing elk populations, are associated with tourism and the revenues brought to the community during elk hunting seasons, whereas economic costs are associated with crop damage, elk-vehicle collisions, and the administrative costs of managing an elk herd. Although the initial costs of transporting, releasing, and monitoring a founder population likely will exceed immediate benefits, once an elk population is established, benefits likely will exceed costs. However, an equitable distribution of costs and benefits must be devised so that the individuals who bear the costs are afforded a comparable or greater set of benefits. Risk of landowner elk-conflicts was examined by comparing human population densities and growth rates, percent private versus public land, and agricultural trends across the 8 study areas. Highest risk for elk-human conflicts was identified in the Southern Piedmont Region and in the Shenandoah study site, risk was moderate in the Southwest, Big Meadows, and Peaks of Otter study sites, and risk in the Highland study site was low. Overall, the Highland study site had the highest feasibility for elk restoration of all study areas examined; the Big Meadows and Southwest study sites both demonstrated moderate feasibility. Restoration in these areas is possible so long as management objectives remain flexible, plans are made in advance to address potential concerns, and the public is involved in the decision-making processes both before and after elk are released.
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    Automated Vehicle Crash Rate Comparison Using Naturalistic Data
    Blanco, Myra; Atwood, Jon; Russell, Sheldon M.; Trimble, Tammy E.; McClafferty, Julie A.; Perez, Miguel A. (Virginia Tech Transportation Institute, 2016-01-08)
    This study assessed driving risk for the United States nationally and for the Google Self-Driving Car project. Driving safety on public roads was examined in three ways. The total crash rates for the Self-Driving Car and the national population were compared to (1) rates reported to the police, (2) crash rates for different types of roadways, and (3) scenarios that give rise to unreported crashes. First, crash rates from the Google Self-Driving Car project per million miles driven, broken down by severity level were calculated. The Self-Driving Car rates were compared to rates developed using national databases which draw upon police-reported crashes and rates estimated from the Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study (NDS). Second, SHRP 2 NDS data were used to calculate crash rates for three levels of crash severity on different types of roads, broken down by the speed limit and geographic classification (termed “locality” in the study; e.g., urban road, interstate). Third, SHRP 2 NDS data were again used to describe various scenarios related to crashes with no known police report. This analysis considered whether such factors as driver distraction or impairment were involved, or whether these crashes involved rear-end collisions or road departures. Crashes within the SHRP 2 NDS dataset were ranked according to severity for the referenced event/incident type(s) based on the magnitude of vehicle dynamics (e.g., high Delta-V or acceleration), the presumed amount of property damage (less than or greater than $1,500, airbag deployment), knowledge of human injuries (often unknown in this dataset), and the level of risk posed to the drivers and other road users (Antin, et al., 2015; Table 1). Google Self-Driving Car crashes were also analyzed using the methods developed for the SHRP 2 NDS in order to determine crash severity levels and fault (using these methods, none of the vehicles operating in autonomous mode were deemed at fault in crashes).
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    Comparing Three Methods of Eye-Glance Transition Coding
    McClafferty, Julie A.; Hankey, Jonathan M. (National Surface Transportation Safety Center for Excellence, 2016-09-30)
    In order for researchers to analyze driver glance behavior and compare their results to other studies, glance evaluation methodologies must be comparable in terms of how specific terms are defined. To this end, the International Organization for Standardization (ISO) developed the ISO 15007 standard, which was adopted by the Society of Automotive Engineers (SAE) as SAE J2396. This report describes an experiment designed to test the validity and flexibility of one key aspect of these standards with respect to how transitions between glances may be assessed. Three glance evaluation methodologies were tested: the Destination Method (DM), the Origin Method (OM), and the Transition Method (TM). Ninety epochs were selected from the Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study data set. The epochs were divided into three sets of 30 epochs with similar complexity. The three sets were then assigned to one of the three eye-glance methods. Five trained eye-glance analysts and one expert rater coded the three sets of epochs using the assigned method, and the resulting glance data were then analyzed to compare the time required to code for each method, the inter- and expert-rater reliability of the three methods, and the effect of coding method on key glance metrics. The results show that the OM was the fastest manual eye-glance coding method (with a recommended time multiplier of 10:1), followed by the DM (12:1), and then the TM (14:1). All three methods produced similar inter-rater and expert-rater reliability scores, averaging about 88%. The three methods of coding also yielded similar key glance metrics, such as average percentage of time not forward, mean forward glance duration, and mean not forward glance duration.
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    Description of the SHRP 2 Naturalistic Database and the Crash, Near-Crash, and Baseline Data Sets
    Hankey, Jonathan M.; Perez, Miguel A.; McClafferty, Julie A. (Virginia Tech Transportation Institute, 2016-04)
    The focus of this project was to identify and prepare crash, near-crash, and baseline data sets extracted from the Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study (NDS) trip files, then to make that information available to researchers for use in their analysis projects. A dozen trigger algorithms were executed on 5,512,900 trip files in the SHRP 2 NDS, and a manual validation of these algorithms identified 1,549 crashes and 2,705 near-crashes. A longitudinal deceleration-based algorithm produced the highest percentage of valid crashes and near-crashes. Baselines were selected via a random sample stratified by participant and proportion of time driven. Triggered epochs and the resulting crashes and near-crashes were reviewed and analyzed by a large team of data reductionists and quality control coordinators following a rigorous training, testing, and monitoring protocol. As a result, 20,000 baselines, including all drivers in the SHRP 2 NDS, were prepared and are recommended for researchers using a case-cohort design. An additional 12,586 baselines are also available for researchers who may require more power in their analyses but are able to forego a fully proportional representation of all drivers in the study. Researchers using this data set are encouraged to review the data dictionaries on the InSight website prior to doing analysis and to be particularly careful in selecting the best subset of crashes, near-crashes, and baselines that informs their research questions.
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    Development and evaluation of a naturalistic observer rating of drowsiness protocol : final report
    Wiegand, Douglas M.; McClafferty, Julie A.; McDonald, Shelby E.; Hanowski, Richard J. (National Surface Transportation Safety Center for Excellence, 2009-02-25)
    VTTI researchers have developed a method for rating driver drowsiness based on the evaluation of naturalistic video footage of the driver's face and upper torso. This measure, referred to as the Observer Rating of Drowsiness (ORD) is based on subjective assessments of the driver's facial tone, behavior, and mannerisms, and is set to a 100-point continuous scale. ORD is assessed based on the 60 seconds of video prior to a trigger event (or baseline epoch). Therefore, ORD is a relatively quick/efficient method for assessing one's drowsiness level, which can then be used to describe a driver's state and investigate whether drowsiness was a contributing factor to a safety-critical event.
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    Development of a Naturalistic Observer-Based Rating of Near-Crash Severity in Naturalistic Driving Data
    McClafferty, Julie A.; Walker, Stuart (National Surface Transportation Safety Center for Excellence, 2024-09-04)
    The analysis of safety-critical events, including crashes and near-crashes, from naturalistic driving studies has proven extraordinarily useful in guiding transportation safety policies, transportation technology, and transportation infrastructure. Near-crashes, which are much more common than crashes, have the potential to answer many research questions. However, they are difficult to define, and their severity is difficult to rate. By definition, there is no impact to measure in a near-crash and therefore no injury or property damage to assess. Near-crashes cover a range of scenarios, and perceptions of severity can vary greatly depending on the person experiencing or perceiving them. From a research perspective, this variability makes near-crashes challenging. Severe near-crashes may be considered most similar to crashes and serve as better surrogates than less severe near-crashes, but less severe near-crashes are still very different from “normal” driving and are still relevant to policy, technology, and infrastructure development research. In this effort, an observer-based, naturalistic near-crash severity rating protocol was developed and tested to help researchers produce near-crash event data effectively and reduce associated variability. Goals included producing a protocol that can (1) produce consistent and meaningful ratings, (2) be incorporated effectively and efficiently into the standard primary event assessment, (3) be implemented by trained data reductionists with access to video and basic kinematic data charts, (4) be applied without complex models, calculations, or statistical modeling, and (5) mirror the existing crash severity scale in implementation and conceptualization. A key concept in this work was that of conflict urgency. There is no clear answer about how urgency can or should be observed or measured in naturalistic data, especially in non-crash scenarios. It is clear, however, that the concepts of collision imminence (a sense of conflict timing) and potential crash severity (related to possible damage and injury) are important factors. Thus, an additional goal was to achieve a balance between actual kinematics, predictive outcomes, and perceived subjective risks. Operational definitions, associated research protocols, and reference guides were developed for four levels of near-crash severity ranging from Critical Severity to Lower Severity. These are documented in the appendices. At their core, the definitions are based on objective metrics such as relative speed, time-to-collision, and type of conflict, but with room for subjective assessments. An iterative approach was used in the development of these definitions, and this included assessments to evaluate interrater reliability. Results indicated that reference materials and training improve interrater reliability.
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    Development of a Protocol to Classify Drivers’ Emotional Conversation
    Fitch, Gregory M.; Russell, Sheldon M.; McClafferty, Julie A. (National Surface Transportation Safety Center for Excellence, 2017-05-05)
    To facilitate future analyses of emotion in naturalistic driving study (NDS) data, a protocol was developed to rate the emotional content of video samples collected during NDS. The protocol required data reductionists to observe video footage of the driver’s face and rate their emotional demeanor in a reasonable amount of time. The Facial Action Coding System (FACS; Ekman, 1978) was used to guide the development of the emotion reduction protocol. Similar to FACS, the protocol instructed reductionists how to classify the driver’s emotion into one of six categories: Neutral/No Emotion Shown, Happy, Angry/Frustrated, Sad, Surprised, and Other. Once reductionists rated the type of emotion expressed by a driver, they then indicated the intensity of the emotion expression, using a four-point scale derived from the five-point scale used in FACS. Although FACS was used to guide development, the protocol was developed to capture the overall emotion of the driver, not necessarily specific facial muscle activations on a frame-by-frame basis. Seventy-two cases for reduction were selected from previously collected NDS data drawn from studies of light vehicle drivers and heavy-truck drivers (Blanco et al., in press; Fitch et al., 2013; Hanowski et al., 2008). Each case was categorized by the experimenters for its specific emotion and intensity level content. The protocol was applied by two groups of reductionists, experienced and novice, in order to determine if training level would impact ratings. Results showed that both experienced and novice reductionists rated cases with similar levels of reliability. Furthermore, both groups of reductionists exhibited inter-rater reliability that was significantly different than chance for all rating types. For both experienced and novice reductionists, accuracy was moderate to good; however, there was evidence of confusion for certain cases. Specifically, confusion existed when a driver exhibited low-intensity emotion. Rescoring the accuracy results to estimate if emotional content was presented by a driver (originally rated as marked or severe emotion present) and or not presented by the driver (originally rated as no emotion or slight emotion) further improved the reductionists’ accuracy. Accuracy using rescored data was 85%, suggesting a high degree of accuracy for detecting emotion reaction. It is expected that future iterations of the protocol will show improved accuracy with slight modifications. Future work applying the protocol to other NDS data sets can support the investigation of emotional cell phone conversation while driving. With further development, the protocol will ultimately be used to shed additional insight into the safety-critical event (SCE) risk of cell phone conversations while driving, and has the potential to be developed for use as a generic and standardized means of classifying the emotions experienced by drivers not only in naturalistic driving studies, but also in driving studies using other methods, including simulation.
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    Estimating Crash Consequences for Occupantless Automated Vehicles
    Witcher, Christina; Henry, Scott; McClafferty, Julie A.; Custer, Kenneth; Sullivan, Kaye; Sudweeks, Jeremy D.; Perez, Miguel A. (Virgina Tech Transportation Institute, 2021-02)
    Occupantless vehicles (OVs) are a proposed application of automated vehicle technology that would deliver goods from merchants to consumers with neither a driver nor passengers onboard. The purpose of this research was to understand and estimate how the increased presence of OVs in the United States fleet may influence crash risk and associated injuries and fatalities. The approach used to estimate potential modifications in crash risk consequences was a counterfactual simulation, where real-world observations were modified as if alternate events had occurred. This analysis leveraged several U.S. national crash databases, along with the Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study (NDS) dataset. The analysis required the derivation of parameters that could be used to modify existing crash estimates as OVs enter the fleet in greater numbers. The team estimated benefit parameters pertaining to (1) the crashes that could be ultimately avoided altogether based on the OV’s smaller size, (2) benefits that could be obtained from the improved crashworthiness characteristics of the OV, and (3) benefits due to the lack of occupants in the OV. Results showed that of the 58,852 fatalities in the national databases examined, a full-scale market penetration of OVs was estimated to reduce fatalities by 34,284, a reduction of 58.2%. Most of this reduction (83%) would come from the lack of occupants in the OVs. Similarly, of the 6,615,117 injured persons in the national databases examined, a full-scale penetration of OVs was estimated to reduce injured persons by 4,088,935, a reduction of 61.8%. As was observed for fatalities, most of this reduction (72.1%) would come from the lack of occupants in the OVs. The results of this investigation, however, should not be taken as definitive benefit estimates. There are important assumptions inherent in the parameters that were used, and some of these assumptions may not be immediately realized. Rather, the results are meant to support critical thinking into how innovative technologies such as OVs may offer benefits that transcend the typical approaches used in vehicle safety, including passive and active safety measures.
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    Identification of Consented Driver Trips in the SHRP 2 Naturalistic Driving Study Data Set
    McClafferty, Julie A.; Perez, Miguel A.; Hankey, Jonathan M. (Virginia Tech Transportation Institute, 2015-02)
    The Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study (NDS) data set contains approximately 5.51 million naturalistic driving trip files across thousands of consented drivers, making it the largest data set of its kind. Before these data can be used to answer research questions, we need to ensure that only consented driver data will be used and that non-consented data are removed from the database. Virginia Tech Transportation Institute (VTTI) research staff performed a Rapid Driver Identification (RDI) task to identify trip files with and without consented drivers. An initial driver recognition step was performed on a small sample of files from each vehicle, followed by a review of the full data set using a recently developed rapid assessment tool that permitted files to be reviewed approximately 25 times faster than before. In all, 6.48 million files were coded, of which 5.51 million are associated with a consented driver during a consented time period. These files represent 3,353 vehicles and 3,546 unique participants, of which 3,241 were primary drivers and 305 were secondary drivers. The products of this task will serve future researchers using the SHRP 2 NDS website for years to come, as well as provide tools and expertise for the rapid assessment of Driver ID in future large-scale naturalistic driving studies.
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    Pedestrian-Vehicle Interaction Data Dictionary and Analysis Protocol: Design, Development, and Pilot Application on Two Naturalistic Driving Databases
    Mabry, J. Erin; Wotring, Brian; McClafferty, Julie A.; Soccolich, Susan A.; Boucher, Ben (2024-08-09)
    Pedestrian conflicts with vehicles continue to be a serious problem in the United States. Unlike vehicle occupants, pedestrians do not have the protection of airbags, a steel structure surrounding them, or other vehicle safety technologies; their resultant vulnerability places them at higher risk of injury when involved in a traffic crash or conflict. Examining pedestrian behavior in a variety of settings and interaction severity levels supports research goals to improve pedestrian safety. The goals of this study were to: 1. Create an inclusive dictionary of video data analysis variables that details and describes interactions between pedestrians and motorized vehicles; and 2. Develop and pilot test a Pedestrian-Vehicle Interaction Data Reduction Protocol (PVIP) using existing naturalistic driving datasets. Implementing the PVIP confirmed that coding elements related to the interaction response between the pedestrian and vehicle from each perspective, and according to the three epoch stages (i.e., leading up to, during, and following the interaction), was critical for characterizing the entire interaction with consideration of all viewpoints. Pedestrian behaviors, locations, communication strategies, distractions, impairments, and glance behaviors were observed and coded at each stage of the epoch to account for behavioral, sensory-related, and positional changes of the pedestrian occurring over the course of the interaction that could impact the outcome. Similarly, coding the vehicle maneuver, driver behaviors, driving-related tasks, and glance behavior across the interaction epoch may be important elements to consider for pedestrian safety. Pedestrian location across the epoch was also an important variable in the pilot analyses. This study is the first of its kind to design, develop, and systematically apply a comprehensive, video-based, and pedestrian-centric data reduction protocol to NDS data to explore and describe interactions between pedestrians and vehicles for better understanding of pedestrian safety. The output of this project is a comprehensive and systematic PVIP that can be used to characterize pedestrian-vehicle interactions and behaviors. The protocol is organized so that researchers may select questions or groups of questions that are applicable for their specific research objectives in an à la carte fashion to create a focused protocol that fully explores a pedestrian-vehicle scenario using available data.