Outcome measurement has been demonstrated to be beneficial when used as a routine part of psychological practice (Lambert et al., 2002), improving both treatment time and outcome of patients, and helping therapists determine which of their patients are not responding to their current treatments (Hannan et al., 2005; Whipple et al., 2003). Despite these proven benefits, many therapists are reluctant to implement and use outcome measures in their routine practice (Hatfield & Ogles, 2007). In addition, the research demonstrating the benefits of these outcome measures has used randomized experimental design to examine the effects of outcome feedback to therapists. The current study focused on examining the benefits of naturalistic usage of outcome measurement feedback in a setting where such measures are a part of routine practice. Therapists' regular use of the feedback from the Outcome Questionnaire-45 was found to have statistical but not clinical significance in the prediction of a patient's symptom change. Additionally, the regularity with which a therapist views a patient's outcome measure feedback was not significantly correlated with that patient's treatment length, overall change in symptoms, or symptom level at termination. Implications regarding these findings are discussed and recommendations regarding the future study of outcome measures are provided.

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Driver distraction has become a major concern for the U.S. Department of Transportation (US DOT).  Performance decrements are typically the result of driver distraction because attentional resources are limited, which are limited; fatigue and drowsiness limit attentional resources further.  The purpose of the current research is to gain an understanding of the relationship between mobile device use (MDU), fatigue, through driving time and time on duty, and drowsiness, through time of day and amount of sleep, for commercial motor vehicle drivers.  A re-analysis of naturalistic driving data was used to obtain information about the factors, MDU, safety-critical events (SCE), and normal driving epochs.  Odds ratios were used to calculate SCE risk for 6 mobile device use subtasks and each of the factors, which were divided into smaller bins of hours for more specific information.  A generalized linear mixed model and chi-square test were used to assess MDU for each factor and the associated bins.  Results indicated visually demanding subtasks were associated with an increase in SCE risk, but conversation on a hands-free cell phone decreased SCE risk.  There was an increase in SCE risk for visual manual subtasks for all bins in which analyses were possible.  Drivers had a higher proportion of MDU in the early morning (circadian low period) than all other times of day that were analyzed.  These results will be used to create recommended training and evaluate policy and technology and will help explain the relationship between MDU, fatigue, and drowsiness. / Master of Science

This research effort aims to create a new car-following model that accounts for the effects of emotion on driver behavior. This research effort is divided into eight research milestones: (1) the development of a segmentation and clustering algorithm to perform new investigations into driver behavior; (2) the finding that driver behavior is different between drivers, between car-following periods, and within a car-following period; (3) the finding that there are patterns in the distribution of driving behaviors; (4) the finding that driving states can result in different driving actions and that the same driving action can be the result of multiple driving states; (5) the finding that the performance of car-following models can be improved by calibration to state-action clusters; (6) the development of a psychophysiological driving simulator study; (7) the finding that the distribution of driving behavior is affected by emotional states; and (8) the development of a car-following model that incorporates the influence of emotions. / Ph. D.

This research effort aims to compare car-following models when the models are calibrated to individual drivers with the naturalistic data. The models used are the GHR, Gipps, Intelligent Driver, Velocity Difference, Wiedemann, and the Fritzsche model. This research effort also analyzes the Wiedemann car-following model using car-following periods that occur at different speeds. The Wiedemann car-following model uses thresholds to define the different regimes in car following. Some of these thresholds use a speed parameter, but others rely solely upon the difference in speed between the subject vehicle and the lead vehicle. This research effort also reconstructs the Wiedemann car-following model for truck driver behavior using the Naturalistic Truck Driving Study's (NTDS) conducted by Virginia Tech Transportation Institute. This Naturalistic data was collected by equipping 9 trucks with various sensors and a data acquisition system. This research effort also combines the Wiedemann car-following model with the GHR car-following model for trucks using The Naturalistic Truck Driving Study's (NTDS) data. / Master of Science

There is extensive evidence indicating that teenaged drivers are over-represented in automobile crashes. Motor vehicle crashes are the leading cause of death for 15-20 year olds, accounting for over 40% of all fatalities for this age group. Although teen drivers account for only 6.3% of the driving population, they account for 14% of all traffic fatalities (TSF, 2004). Currently there is a lack of continuous and naturalistic data in the field of teenaged driving. The purpose of this study was to obtain continuous performance data from a naturalistic setting by equipping the personal vehicles of newly licensed teenaged drivers with a data collection system for the first six months of driving. Specifically, this study examined the eye scanning patterns of newly licensed teenaged drivers and experienced parent drivers on straight road segment. This study provides insight into the development and change of eye-glance behaviors over the first six months of driving, the differences between novice teenaged drivers and experienced parent drivers, and how passenger presence affects eye scanning patterns. Results from this study found significant differences between novice teenaged drivers and experienced adult drivers. The results showed that teenaged drivers had much shorter glance durations away from the forward roadway and allocated a higher percentage of their glances to locations that were considered driving-related when compared to the experienced adult group. Results from the study also showed when one passenger was present in the vehicle teenaged drivers tended to have a higher percentage of time spent with their eyes off of the forward roadway. / Master of Science

In the area of transportation policy, congestion pricing has been used to alleviate traffic congestion in metropolitan areas. The focus of Chapter 1 is to examine drivers’ perceived risk of traffic delay as one determinant of reactions to congestion pricing. The experiment reported in this essay recruits commuters from the Atlanta and Orlando metropolitan areas to participate in a naturalistic experiment where they are asked to make repeated route decisions in a driving simulator. Chapter 1 examines belief formation and adjustments under an endogenous information environment where information about a route can be obtained only conditional on taking the route. If the subjects arrive to the destination late, i.e. beyond an assigned time threshold, they are faced with a discrete (flat) penalty. In contrast, Chapter 2 examines subjective beliefs in a setting where the penalty for a late arrival is continuous, such that a longer delay incurs additional penalty on the driver. The primary research question is: does belief formation differ when the late penalty is induced as a continuous amount compared to when it is induced as a discrete amount? In particular, will we observe a difference in learning across the range of congestion probabilities under different penalty settings? In the continuous penalty setting, we do not observe a difference in learning across the range of congestion probabilities. In contrast, in the discrete penalty setting we observe significant belief adjustments in the lowest congestion risk scenario. In Chapter 3 the “source method” is used to examine how uncertainty aversion differs across events that have the same underlying objective probabilities but are presented under varying degrees of uncertainty. Subjects are presented with three lottery tasks that rank in order of increasing uncertainty. Given the choices observed in each task a source function is estimated jointly with risk attitudes under different probability weighting specifications of the source function. Results from the Prelec probability weighting suggest that, as the degree of uncertainty increases, subjects display increased pessimism; in contrast, the Tversky-Kahneman (1992) and the Power probability weightings detect no such difference. Thus, the conclusion regarding uncertainty aversion are contingent on which probability weighting specification is assumed for the source function.