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One of the main goals of the Virginia Tech (VT) Alternative Transportation Department is encouraging the VT community (including students, faculty, and staff) to walk, use the bus, carpool, or ride bicycles for alternative transportation to decrease dependency on vehicle use and traffic around campus and increase overall safety. This project develops an intervention and education program to encourage alternative transportation to, from, and around campus to reduce campus traffic. In addition, since there is currently no standardized approach for computing the injury rates for non-vehicle roadway users, this project also refines and assesses a methodology for estimating injury rates for pedestrians and pedalcyclists, which was used to inform the developed educational alternative transportation safety course.

One of the critical circumstances in automated vehicle driving is transition of control between partially automated vehicles and drivers. One approach to enhancing the design of transition of control is to predict driver behavior during a takeover by analyzing a driver’s state before the takeover occurs. Although there is a wealth of existing driver behavior model prediction literature, little is known regarding takeover performance prediction (e.g., driver error) and its underlying data structure (e.g., window size). Thus, the goal of this study is to predict driver error after a takeover event using supervised machine learning algorithms on various window sizes. Three machine learning algorithms—decision tree, random forest, and support vector machine with a radial basis kernel—were applied to driving performance, physiological, and glance data from a driving simulator experiment examining automated vehicle driving. The results showed that a random forest algorithm with an area under the receiver operating curve of 0.72, trained on a 3 s window before the takeover time, had the highest performance for accurately classifying driver error. In addition, we identified the 10 most critical predictors that resulted in the best error prediction performance. The results of this study can be beneficial for developing driver state algorithms that could be integrated into automated driving systems.

One of the critical circumstances in automated vehicle driving is transition of control between partially automated vehicles and drivers. One approach to enhancing the design of transition of control is to predict driver behavior during a takeover by analyzing a driver’s state before the takeover occurs. Although there is a wealth of existing driver behavior model prediction literature, little is known regarding takeover performance prediction (e.g., driver error) and its underlying data structure (e.g., window size). Thus, the goal of this study is to predict driver error after a takeover event using supervised machine learning algorithms on various window sizes. Three machine learning algorithms—decision tree, random forest, and support vector machine with a radial basis kernel—were applied to driving performance, physiological, and glance data from a driving simulator experiment examining automated vehicle driving. The results showed that a random forest algorithm with an area under the receiver operating curve of 0.72, trained on a 3 s window before the takeover time, had the highest performance for accurately classifying driver error. In addition, we identified the 10 most critical predictors that resulted in the best error prediction performance. The results of this study can be beneficial for developing driver state algorithms that could be integrated into automated driving systems.

Literature supports an association between work and cardiovascular disease in adults. The objective of this study was to examine the relationship between current work status and elevated blood pressure in Hispanic adolescents. Participants were students in Hidalgo County, located along the Texas-Mexico border. Participants enrolled in the cohort study in ninth grade with assessments completed once a year for up to three years. Participants completed a self-report survey, while staff measured height, weight, waist circumference, blood pressure, and were screened for acanthosis nigricans. A generalized linear regression model with a logit link function was constructed to assess current work status and elevated blood pressure. Of the 508 participants, 29% had elevated blood pressure, which was associated with being male and other chronic disease indicators (e.g., acanthosis nigricans, overweight/obesity). The mean probability for elevated blood pressure was higher among currently working adolescents compared to those who were not. Findings were statistically significant (p < 0.05) at baseline. The findings illustrate that a large proportion of adolescents along the Texas-Mexico border may have elevated blood pressure and that working may be associated with it. Subsequent research is needed to confirm these findings, as well as to identify the mechanism for how work may increase hypertension in adolescents.

Literature supports an association between work and cardiovascular disease in adults. The objective of this study was to examine the relationship between current work status and elevated blood pressure in Hispanic adolescents. Participants were students in Hidalgo County, located along the Texas-Mexico border. Participants enrolled in the cohort study in ninth grade with assessments completed once a year for up to three years. Participants completed a self-report survey, while staff measured height, weight, waist circumference, blood pressure, and were screened for acanthosis nigricans. A generalized linear regression model with a logit link function was constructed to assess current work status and elevated blood pressure. Of the 508 participants, 29% had elevated blood pressure, which was associated with being male and other chronic disease indicators (e.g., acanthosis nigricans, overweight/obesity). The mean probability for elevated blood pressure was higher among currently working adolescents compared to those who were not. Findings were statistically significant (p < 0.05) at baseline. The findings illustrate that a large proportion of adolescents along the Texas-Mexico border may have elevated blood pressure and that working may be associated with it. Subsequent research is needed to confirm these findings, as well as to identify the mechanism for how work may increase hypertension in adolescents.

The question of whether driver behavior, and speeding in particular, differs based on passenger(s) presence requires the use of large amounts of data, some of which may be difficult to accurately obtain. Traditional methods of obtaining driver behavior information result in datasets that either lack passenger information altogether (i.e., insurance companies using telematics) or rely on rough estimates of passenger age and gender obtained from blurred photos (i.e., naturalistic driving studies like the Second Strategic Highway Research Program). This research project represents a novel, data-driven approach to assessing passenger impact on speeding. Household travel survey demographic information and GPS traces were linked to HERE network speed limit to study the impact of vehicle occupancy on speeding. Survey responses from 11 study areas were cleaned, merged, and ultimately used in developing binomial logistic regression models. Of particular interest were the following driver groups: teenagers, adults driving with child passenger(s), and older drivers. The models suggest that drivers speed less when there is a passenger in the vehicle, particularly adult drivers with a child passenger(s).

The question of whether driver behavior, and speeding in particular, differs based on passenger(s) presence requires the use of large amounts of data, some of which may be difficult to accurately obtain. Traditional methods of obtaining driver behavior information result in datasets that either lack passenger information altogether (i.e., insurance companies using telematics) or rely on rough estimates of passenger age and gender obtained from blurred photos (i.e., naturalistic driving studies like the Second Strategic Highway Research Program). This research project represents a novel, data-driven approach to assessing passenger impact on speeding. Household travel survey demographic information and GPS traces were linked to HERE network speed limit to study the impact of vehicle occupancy on speeding. Survey responses from 11 study areas were cleaned, merged, and ultimately used in developing binomial logistic regression models. Of particular interest were the following driver groups: teenagers, adults driving with child passenger(s), and older drivers. The models suggest that drivers speed less when there is a passenger in the vehicle, particularly adult drivers with a child passenger(s).

The question of whether driver behavior, and speeding in particular, differs based on passenger(s) presence requires the use of large amounts of data, some of which may be difficult to accurately obtain. Traditional methods of obtaining driver behavior information result in datasets that either lack passenger information altogether (i.e., insurance companies using telematics) or rely on rough estimates of passenger age and gender obtained from blurred photos (i.e., naturalistic driving studies like the Second Strategic Highway Research Program). This research project represents a novel, data-driven approach to assessing passenger impact on speeding. Household travel survey demographic information and GPS traces were linked to HERE network speed limit to study the impact of vehicle occupancy on speeding. Survey responses from 11 study areas were cleaned, merged, and ultimately used in developing binomial logistic regression models. Of particular interest were the following driver groups: teenagers, adults driving with child passenger(s), and older drivers. The models suggest that drivers speed less when there is a passenger in the vehicle, particularly adult drivers with a child passenger(s).

69A3551747115/05-008, One of the main goals of the Virginia Tech (VT) Alternative Transportation Department is encouraging the VT community (including students, faculty, and staff) to walk, use the bus, carpool, or ride bicycles for alternative transportation to decrease dependency on vehicle use and traffic around campus and increase overall safety. This project develops an intervention and education program to encourage alternative transportation to, from, and around campus to reduce campus traffic. In addition, since there is currently no standardized approach for computing the injury rates for non-vehicle roadway users, this project also refines and assesses a methodology for estimating injury rates for pedestrians and pedalcyclists, which was used to inform the developed educational alternative transportation safety course.