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Advancements in computer technology are making video conferencing a viable communication medium for desktop computers. These same advancements are changing the structure and means by which information workers conduct business. From a human factors perspective, however, the study of new communication technologies and their relationships with end users presents a challenging research domain. This study employed two diverse research approaches to the problem of reduced video frame rate in desktop video conferencing. In the first study, a psychophysical method was used to evaluate video image quality as a function of frame rate for a series of different scenes. Scenes varied in terms of level of detail, velocity of panning, and content. Results indicate that for most scenes, differences in frame rate become less detectable above approximately 10 frames per second (fps), suggesting a curvilinear relationship between image quality and frame rate. For a traditional conferencing scene, however, a linear increase in frame rate produced a linear improvement in perceived image quality. High detail scenes were perceived to be of lower quality than the low detail scenes, while panning velocity had no effect. In the second study, a collection of research methods known as ethnography was used to examine long-term use of desktop video by collaborators in a real work situation. Participants from a graduate course met each week for seven weeks and worked on a class project under one of four communication conditions: face-to-face, 1 fps, 10 fps, and 25 fps. Dependent measures included interviews, questionnaires, interaction analysis measures, and ethnomethodology. Recommendations are made regarding the utility and expense of each method with respect to uncovering human factors issues in video-mediated collaboration. It is believed that this research has filled a significant gap in the human factors literature of advanced telecommunications and research methodology. / Ph. D.

Driver heterogeneity in travel behavior has repeatedly been cited in the literature as a limitation that needs to be addressed. In this work, driver heterogeneity is addressed from four different perspectives. First, driver heterogeneity is addressed by models of driver perceptions of travel conditions: travel distance, time, and speed. Second, it is addressed from the perspective of driver learning trends and models of driver-types. Driver type is not commonly used in the vernacular of transportation engineering. It is a term that was developed in this work to reflect driver aggressiveness in route switching behavior. It may be interpreted as analogous to the commonly known personality-types, but applied to driver behavior. Third, driver heterogeneity is addressed via latent class choice models. Last, personality traits were found significant in all estimated models. The first three adopted perspectives were modeled as functions of variables of driver demographics, personality traits, and choice situation characteristics. The work is based on three datasets: a driving simulator experiment, an in situ driving experiment in real-world conditions, and a naturalistic real-life driving experiment. In total, the results are based on three experiments, 109 drivers, 74 route choice situations, and 8,644 route choices. It is assuring that results from all three experiments were found to be highly consistent. Discrepancies between predictions of network-oriented traffic assignment models and observed route choice percentages were identified and incorporating variables of driver heterogeneity were found to improve route choice model performance. Variables from all three groups: driver demographics, personality traits, and choice situation characteristics, were found significant in all considered models for driver heterogeneity. However, it is extremely interesting that all five variables of driver personality traits were found to be, in general, as significant as, and frequently more significant than, variables of trip characteristics — such as travel time. Neuroticism, extraversion and conscientiousness were found to increase route switching behavior, and openness to experience and agreeable were found to decrease route switching behavior. In addition, as expected, travel time was found to be highly significant in the models that were developed. However, unexpectedly, travel speed was also found to be highly significant, and travel distance was not as significant as expected. Results of this work are highly promising for the future of understanding and modeling of heterogeneity of human travel behavior, as well as for identifying target markets and the future of intelligent transportation systems. / Ph. D.

The sharing of data has become integral to advancing scientific research, but it introduces challenges related to safeguarding personally identifiable information (PII). This thesis addresses the specific problem of sharing drivers' face videos for transportation research while ensuring privacy protection. To tackle this issue, we leverage recent advancements in generative adversarial networks (GANs) and demonstrate their effectiveness in deidentifying individuals by swapping their faces with those of others. Extensive experimentation is conducted using a large-scale dataset from ORNL, enabling the quantification of errors associated with head movements, mouth movements, eye movements, and other human factors cues. Additionally, qualitative analysis using metrics such as PERCLOS (Percentage of Eye Closure) and human evaluators provide valuable insights into the quality and fidelity of the deidentified videos. To enhance privacy preservation, we propose the utilization of synthetic faces as substitutes for real faces. Moreover, we introduce practical guidelines, including the establishment of thresholds and spot checking, to incorporate human-in-the-loop validation, thereby improving the accuracy and reliability of the deidentification process. In addition to this, this thesis also presents mitigation strategies to effectively handle reidentification risks. By considering the potential exploitation of soft biometric identifiers or non-biometric cues, we highlight the importance of implementing comprehensive measures such as robust data user licenses and privacy protection protocols. / Master of Science / With the increasing availability of large-scale datasets in transportation engineering, ensuring the privacy and confidentiality of sensitive information has become a paramount concern. One specific area of concern is the protection of drivers' facial data captured by the National Driving Simulator (NDS) during research studies. The potential risks associated with the misuse or unauthorized access to such data necessitate the development of robust deidentification techniques. In this thesis, we propose a GAN-based framework for the deidentification of drivers' face videos while preserving important facial attribute information. The effectiveness of the proposed framework is evaluated through comprehensive experiments, considering various metrics related to human factors. The results demonstrate the capability of the framework to successfully deidentify face videos, enabling the safe sharing and analysis of valuable transportation research data. This research contributes to the field of transportation engineering by addressing the critical need for privacy protection while promoting data sharing and advancing human factors research.

Information visualization (info vis) deals with how to increase the bandwidth of effective communication between computer and human, enabling us to see more, understand more, and accomplish more. Traditionally, it deals with interaction and display techniques of visualizing often abstract data on the two-dimensional desktop. Immersive virtual environments (VEs) offer new, exciting possibilities for information visualization. Immersion gives an enhanced realistic effect, and can improve spatial understanding and orientation. By identifying or developing useful interaction techniques (ITs), we can develop VE systems for better information visualization. This thesis has two different experiments that were related to two different sides of the study of use of immersion for VEs. One of the experiments is related to abstract data visualization in an immersive VE. The other one was motivated by the need for enhancing a realistic VE with additional data. In our first experiment, our focus is on implementing overview+detail techniques in VEs. Our hypothesis is that VE-specific ITs should borrow from, but not copy existing 2D IT technique for overview +detail. We develop ITs for use in VEs and show that they are easy to use and useful using task-based usability evaluation. We develop the "jump" technique for use in this application, which can be generalized to numerous other applications. The tangible contribution of this research is Wizard, an application for infovis in VEs. Our second hypothesis is that if the data to be visualized has inherent spatial attributes, it can be visualized well in immersive virtual environments. We investigate the trends using an experiment that tests people's understanding of spatial attributes under immersive and desktop conditions. Although not statistically significant, we observed a moderate trend indicating that immersion decreases the time needed to perform a spatial information- gathering task. We believe that this area of research can be applied immediately to the applications currently being developed. / Master of Science

The Assistance Manager, one of the tools of the Simulation Model Development Environment (SMDE), is required to provide assistance to a user during the process of model development. This thesis describes the research effort to prototype the SMDE Assistance Manager. Requirements are set forth and a design is established for the Assistance Manager prototype. The implementation is described, and the Assistance Manager is shown to provide a highly flexible interface between the user and the database of assistance information. Assessment criteria are established, and the prototype is evaluated. Results of the evaluation indicate that the Assistance Manager incorporates the characteristics considered desirable in online assistance systems, and serves as a basis for future enhancement and development. / Master of Science

<p dir="ltr">Organizational newcomers strive to adjust when they start new jobs, yet little is known about how remote and hybrid work structures influence this process. Previous research has adopted variable-centered approaches to explore the frequency with which newcomers engage in dozens of proactive behaviors in traditional, face-to-face work environments. The current study builds upon this work to capture the socialization process in modern work environments. Adopting a person-centered approach, this study 1.) identifies profiles of newcomers’ perceptions of their work environments, 2.) identifies profiles of newcomer proactive behaviors, 3.) examines the effect of virtuality on these two sets of profiles, and 4.) explores which behavior profiles are associated with the most favorable changes in adjustment.</p><p dir="ltr">A sample of 464 organizational newcomers responded to a survey about their environment perceptions, the frequency with which they engaged in a variety of proactive behaviors, and their feelings of adjustment. Latent profile enumeration resulted in three environment profiles (Average Environment, Peer-Centric Environment, and Favorable Environment) and seven behavior profiles (Low Frequency Seeker, Moderate Frequency Seeker, High Frequency Seeker, Direct Solo Seekers, Typical Limit Testers, Social Limit Testers, and Balanced Limit Testers). While the hypothesized virtual environment and behavior profiles were not found, one environment profile (Average Environment) and two behavior profiles (Low Frequency Seekers; Direct Solo Seekers) were significantly and positively associated with virtuality. Further, newcomers in the Average Environment profile tended to be Low Frequency Seekers, while newcomers in the Favorable Environment profile tended to be Moderate or High Frequency Seekers. Additionally, the Direct Solo Seekers were more likely to perceive their environment as Favorable, relative to the Low Frequency Seekers.</p><p dir="ltr">A total of 173 of the newcomers completed a follow-up survey about their feelings of adjustment. Significant differences across behavior profiles in changes in adjustment (while controlling for newcomer tenure) demonstrate the complex dynamics of the newcomer adjustment process. Specifically, the two highly virtual behavior profiles – Low Frequency Seekers and Direct Solo Seekers – showed different relationships with outcomes over time, suggesting that some behavioral repertoires are more effective than others for adjusting in virtual environments. Supplemental analyses further showed that the behavior profiles offered incremental validity over a main effects model of proactive behaviors in predicting feelings of social acceptance. Additionally, supplemental analyses demonstrated that jointly accounting for environment and behavior profiles can meaningfully explain differences in adjustment across newcomers. Collectively, this study shows the utility of using person-centered approaches for understanding how configurations of environment perceptions and proactive behaviors shape newcomers’ experiences in modern work environments.</p>

Over many decades, vigilance research has consistently found that performance on vigilance tasks decline over time (i.e., the vigilance decrement; Davies & Parasuraman, 1982), and that performing a vigilance task is both mentally demanding and stressful (Warm et al., 2008). Researchers have subsequently implemented a wide range of interventions to both understand and attempt to attenuate the vigilance decrement and its associated affective effects. Among these efforts, little attention has been devoted to understanding the effects of the social environment on vigilance. Nonetheless, a handful of studies have indeed suggested that the presence of others can affect performance, workload, and stress in vigilance. The present dissertation sought to extend these findings by examining the effect of social support on vigilance, a novel form of social presence in vigilance research, based on findings that the provision of social support may improve performance on cognitive tasks. 236 participants were randomly assigned to complete a cognitive vigilance task either alone or in one of seven social presence conditions: supportive or non-supportive observer, supportive or non-supportive co-actor, independent co-actor, evaluative observer, or merely present observer. Regarding the novel supportive and non-supportive manipulations, results indicated that receiving non-supportive statements resulted in a more conservative response bias than supportive statements, but that receiving supportive statements resulted in higher perceived effort. Additionally, receiving statements from a co-actor, regardless of the type of statement, resulted in higher median response times. In comparing the novel manipulations to existing manipulations of social presence in vigilance, participants in the non-supportive observer condition outperformed those in the independent co-actor and mere presence conditions. The results of this dissertation thus imply that verbal interactions during vigilance tasks – and the supportive or non-supportive nature of those interactions – can affect performance and workload differently than non-verbal forms of social presence.

By definition, a system is comprised of hardware, software and "liveware". It also interacts with other systems composed themselves of those elements. However, the "human" element tends to be neglected in many projects, leading to unsafe or inefficient systems. Although some studies have shown that sociotechnical approaches to project management can generate economic gains of 20%, not to mention social gains, in practice, few projects integrate human factors correctly. Many reasons can explain this lack of integration. Humans alone are much more difficult to model and understand than technology. When considering groups or organizations, the problem increases exponentially. Hence, traditional engineering and risk management methods cannot be used to address the human side of a system. There exist approaches and methods to use our current understanding of human behavior, however these tend to be understood and used only by a small number of specialists. Most project managers, designers and engineers have insufficient knowledge of their existence or do not understand how to make good use of them. There are two major challenges in the integration of human factors. The first one is to justify an interest in such an approach. Given the educational background and experience of many engineers, this is no easy task. The SNCF (French Railways) has chosen to face this challenge and achieved quite good results. However, this does not solve the problem, as project managers and engineers then request tools and methods. Fulfilling this need represents the second challenge. This is the subject of this study: to make a shift from technology-centered approaches to design and risk management to a more sociotechnical approach thanks to a macroergonomics project framework. Human factors engineering and ergonomics is a multi-disciplinary domain. It goes from human resources management to physical ergonomics and integrates such subjects as psychology, sociology and human reliability. To improve the reliability or efficiency of systems, one approach is to develop a single tool addressing one aspect of human factors or integrating it with one kind of activities. However, many of those tools already exist, even if they have remained at the state of research results yet or been applied only in some very specific sectors. Hence, for this research, it was decided to develop a method that covers the whole process of a project and contains the different considerations related to human factors as well as the activities required to ensure the safety of the system. Recent research led by the US Army and adapted by the UK and Canadian Armies as well as Eurocontrol have lead to the emergence of a new discipline called Human Factors Integration (HFI). This discipline proposes a project management process that covers different domains of human factors: manpower, personnel, training, ergonomics, safety, health and hazards, survivability. HFI is a good starting point but it responds only partly to our expressed need. Indeed, the SNCF requires a more general approach, easily accessible, with a greater emphasis on organization and risk management. During this study, the HFI method was extended based on recent research results, especially in human and organizational reliability. The main improvements made are the addition of the "organization" domain and the development of safety-related activities. Many other principles were also integrated including barriers, prescribed vs. real tasks, redundancy, recovery, degraded situation, system dynamics and measurement. Some interests of this method are its inheritance from systems engineering, its capacity to be utilized by users from different cultures and experience, and its independence from specific models of human behavior or task processing. The main output of the study is a documentation of this method defining the activities and tasks for each phase of the project as well as the composition of the team. The method was evaluated based on its application on the "Sécurité des Travaux Organisation Réalisation Préparation" (STORP) project. This project aims at redesigning the infrastructure maintenance system of the SNCF, modifying the concepts, principles, guidelines and documentations, in order to improve its efficiency and safety. This application enabled to test the coherence and usability of the method, as well as highlight its main advantages, while underlining and improving the human factors integration in STORP. Through this evaluation, this study constitutes one of the first attempts to apply HFI to a non-military domain and to non-specific projects. / Master of Science

The safety of nuclear power plants must be proved, certified and improved. Probabilistic safety assessments are used to estimate the core meltdown risk, by means of sequential analyses of accidents. In order to assess probabilities of the appearance of these sequences, it is necessary to specifically assess probabilities of operation failures accomplished by human operators in a degraded mode. For this purpose, EDF, the French producer of electricity, developed a method that models failures of human actions, by means of a systematic determination of scenarios corresponding to different failure modes. This method, called MERMOS, has been used for several probabilistic safety assessments. In order to increase its reproducibility and to make it more robust, example missions and scenarios will be built. This set of example analyses will be used by experts assessing human reliability: they will develop studies and deduce results more easily. The purpose of this study involves the creation of a methodology to model existing analyses and human reliability data used in MERMOS. This study consists of optimizing a second generation human reliability assessment method in order to overpass its current weaknesses in an operational context by means of the identification of a set of example analyses. / Master of Science