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The air transportation system has grown significantly during the past few decades. The demand for air travel has increased tremendously as compared to the increase in the supply. The air transportation system can be divided into four subsystems: airports, airlines, air traffic control, and passengers, each of them having different interests. These subsystems interact in a very complex way resulting in various phenomena. On the airport side, there is excessive flight demand during the peak hours that frequently exceeds the airport capacity resulting in serious flight delays. These delays incur costs to the airport, passengers, and airlines. The air traffic pattern is also affected by the characteristics of the air transportation network. The current network structure of most major airlines in United States is a hub-and-spoke network. The airports are interested in reducing congestion, especially during the peak time. The airlines act as direct demand to the airport and as the supplier to the passengers. They sometimes compete with other airlines on certain routes and sometimes they collaborate to maximize revenue. The flight schedule of airlines directly affects the travel demand. The flight schedule that minimizes the schedule delay of passengers in directed and connected flights will attract more passengers. The important factors affecting the airline revenue include ticket price, departure times, frequency, and aircraft type operated on each route. The revenue generated from airline depends also on the behavior of competing airlines, and their flight schedules. The passengers choose their flight based on preferred departure times, offered ticket prices, and willingness of airlines to minimize delay and cost. Hence, all subsystems of air transportation system are inter-connected to each other, meaning, strategy of each subsystem directly affects the performance of other subsystems. This interaction between the subsystems makes it more difficult to analyze the air transportation system. Traditionally, analytical top-down approach has been used to analyze the air transportation problem. In top-down approach, a set of objectives is defined and each subsystem is fixed in the overall scheme. On the other hand, in a bottom-up approach, many issues are addressed simultaneously and each individual system has greater autonomy to make decisions, communicate and to interact with one another to achieve their goals when considering complex air transportation system. Therefore, it seems more appropriate to approach the complex air traffic congestion and airline competition problems using a bottom-up approach. In this research, an agent-based model for the air transportation system has been developed. The developed model considers each subsystem as an independent type of agent that acts based on its local knowledge and its interaction with other agents. The focus of this research is to analyze air traffic congestion and airline competition in a hub-and-spoke network. The simulation model developed is based on evolutionary computation. It seems that the only way for analyzing emergent phenomenon (such as air traffic congestion) is through the development of simulation models that can simulate the behavior of each agent. In the agent-based model developed in this research, agents that represent airports can increase capacity or significantly change landing fee policy, while the agents that represent airlines learn all the time, change their markets, fare structure, flight frequencies, and flight schedules. Such a bottom-up approach facilitates a better understanding of the complex nature of congestion and gains more insights into the competition in air transportation, hence making it easier to understand, predict and control the overall performance of the complex air transportation system. / Ph. D.

Active sensors, such as radar, lidar and sonar, emit a signal into the environment and gather information from its reflection. In contrast, passive sensors such as cameras and microphones rely on the signals emitted from the environment. In the current application of active sensors in multi-agent autonomous systems, agents only rely on their own active sensing and filter out any information available passively. However, fusing passive and active sensing information may improve the accuracy of the agents. Also, there is evidence that bats who use biosonar eavesdrop on a conspecific's echolocation sound, which shows a successful example of implementing active and passive sonar sensor fusion in nature. We studied the effect of such information fusion in the framework of two problems: the collective behavior in a multi-agent system using the Vicsek model and the canonical robotics problem of Simultaneous Localization And Mapping (SLAM). Collective behavior refers to emergence of a complex behavior in a group of individuals through local interaction. The Vicsek model is a well-established flocking model based on alignment of individuals with their neighbors in the presence of noise. We studied the aligned motion in a group in which the agents employ both active and passive sensing. Our study shows that the group behavior is less sensitive to measurement accuracy compared to modeling precision. Therefore, using measurement values of the noisier passive sonar can be beneficial. In addition, the group alignment is improved when the passive measurements are not dramatically noisier than active measurements. In the SLAM problem, a robot scans an unknown environment building a map and simultaneously localizing itself within that map. We studied a landmark-based SLAM problem in which the robot uses active and passive sensing strategies. The information provided passively can improve the accuracy of the active sensing measurements and compensate for its blind spot. We developed an estimation algorithm using Extended Kalman Filter and employed Monte Carlo simulation to find a parameter region in which fusing passive and active sonar information improves the performance of the robot. Our analysis shows this region is aligned within the common range of active sonar parameters. / Doctor of Philosophy / Group behavior is a fascinating phenomenon in animal groups such as bird flocks, fish schools, bee colonies and fireflies. For instance, many species of fireflies synchronize their flashing when they bio-luminesce. This synchronization pattern is a group behavior created as a result of local interaction formed by sensing individuals in the group. The research question for this dissertation is inspired by comes from group behavior in bats. Bats use echolocation to perceive the environment. They make a sound and listen to the echo of the sound coming back from objects and by analyzing the echo, they can get information about their surroundings. It has been observed that bats may also use the echo of other bats' sound to perceive their environment. In other words they use two different sensors, one is called active sonar since they actively make the sound and listen to its echoes, and the other one is called passive sonar since they just passively listen to the sound generated by other bats. If this information is useful, can we exploit that in design of engineered systems? We investigated these questions using numerical simulation to solve two test bed problems. The first problem is based on a mathematical flocking model in which the individuals in the group align through local interaction. We found out that eavesdropping improves the alignment of the group within a range of parameters in the model which are relevant to the sensing capabilities of the sonar sensors. The other problem is a canonical robotics problem known as the simultaneous localization and mapping (SLAM). In this problem, a robot searches an unknown environment and creates a map of the environment (mapping) and reports the path it takes within the map (localization). We found out that when the robot uses both passive and active sonar, depending on the accuracy of the two sensing approaches, it can improve the accuracy of both the generated map and the robot's path.

This dissertation will focus on three research projects: Neighborhood vector auto regression in multivariate time series, uncertainty quantification for agent-based modeling networked anagrams, and a scalable algorithm for multi-class classification. The first project studies the modeling of multivariate time series, with the applications in the environmental sciences and other areas. In this work, a so-called neighborhood vector autoregression (NVAR) model is proposed to efficiently analyze large-dimensional multivariate time series. The time series are assumed to have underlying distances among them based on the inherent setting of the problem. When this distance matrix is available or can be obtained, the proposed NVAR method is demonstrated to provides a computationally efficient and theoretically sound estimation of model parameters. The performance of the proposed method is compared with other existing approaches in both simulation studies and a real application of stream nitrogen study. The second project focuses on the study of group anagram games. In a group anagram game, players are provided letters to form as many words as possible. In this work, the enhanced agent behavior models for networked group anagram games are built, exercised, and evaluated under an uncertainty quantification framework. Specifically, the game data for players is clustered based on their skill levels (forming words, requesting letters, and replying to requests), the multinomial logistic regressions for transition probabilities are performed, and the uncertainty is quantified within each cluster. The result of this process is a model where players are assigned different numbers of neighbors and different skill levels in the game. Simulations of ego agents with neighbors are conducted to demonstrate the efficacy of the proposed methods. The third project aims to develop efficient and scalable algorithms for multi-class classification, which achieve a balance between prediction accuracy and computing efficiency, especially in high dimensional settings. The traditional multinomial logistic regression becomes slow in high dimensional settings where the number of classes (M) and the number of features (p) is large. Our algorithms are computing efficiently and scalable to data with even higher dimensions. The simulation and case study results demonstrate that our algorithms have huge advantage over traditional multinomial logistic regressions, and maintains comparable prediction performance. / Doctor of Philosophy / In many data-central applications, data often have complex structures involving temporal structures and high dimensionality. Modeling of complex data with temporal structures have attracted great attention in many applications such as enviromental sciences, network sciences, data mining, neuroscience, and economics. However, modeling such complex data is quite challenging due to large uncertainty and dimensionality of complex data. This dissertation focuses on modeling and prediction of complex data with temporal structures. Three different types of complex data are modeled. For example, the nitrogen of multiple streams are modeled in a joint manner, human actions in networked group anagrams are modeled and the uncertainty is quantified, and data with multiple labels are classified. Different models are proposed and they are demonstrated to be efficient through simulation and case study.

Transportation Engineers have recently begun to incorporate statistical and machine learning approaches to solving difficult problems, mainly due to the vast quantities of data collected that is stochastic (sensors, video, and human collected). In transportation engineering, a transportation system is often denoted by jurisdiction boundaries and evaluated as such. However, it is ultimately defined by the consideration of the analyst in trying to answer the question of interest. In this dissertation, a transportation system located in Jackson, Wyoming under the jurisdiction of the Grand Teton National Park and recognized as the Moose-Wilson Corridor is evaluated to identify transportation-related factors that influence its operational performance. The evaluation considers its unique prevalent conditions and takes into account future management strategies. The dissertation accomplishes this by detailing four distinct aspects in individual chapters; each chapter is a standalone manuscript with detailed introduction, purpose, literature review, findings, and conclusion. Chapter 1 provides a general introduction and provides a summary of Chapters 2 – 6. Chapter 2 focuses on evaluating the operational performance of the Moose-Wilson Corridor's entrance station, where queueing performance and arrival and probability mass functions of the vehicle arrival rates are determined. Chapter 3 focuses on the evaluation of a parking system within the Moose-Wilson Corridor in a popular attraction known as the Laurance S. Rockefeller Preserve, in which the system's operational performance is evaluated, and a probability mass function under different arrival and service rates are provided. Chapter 4 provides a data science approach to predicting the probability of vehicles stopping along the Moose-Wilson Corridor. The approach is a machine learning classification methodology known as "decision tree." In this study, probabilities of stopping at attractions are predicted based on GPS tracking data that include entrance location, time of day and stopping at attractions. Chapter 5 considers many of the previous findings, discusses and presents a developed tool which utilizes a Bayesian methodology to determine the posterior distributions of observed arrival rates and service rates which serve as bounds and inputs to an Agent-Based Model. The Agent-Based Model represents the Moose-Wilson Corridor under prevailing conditions and considers some of the primary operational changes in Grand Teton National Park's comprehensive management plan for the Moose-Wilson Corridor. The implementation of an Agent-Based Model provides a flexible platform to model multiple aspects unique to a National Park, including visitor behavior and its interaction with wildlife. Lastly, Chapter 6 summarizes and concludes the dissertation. / Doctor of Philosophy / In this dissertation, a transportation system located in Jackson, Wyoming under the jurisdiction of the Grand Teton National Park and recognized as the Moose-Wilson Corridor is evaluated to identify transportation-related factors that influence its operational performance. The evaluation considers its unique prevalent conditions and takes into account future management strategies. Furthermore, emerging analytical strategies are implemented to identify and address transportation system operational concerns. Thus, in this dissertation, decision support tools for the evaluation of a unique system in a National Park are presented in four distinct manuscripts. The manuscripts cover traditional approaches that breakdown and evaluate traffic operations and identify mitigation strategies. Additionally, emerging strategies for the evaluation of data with machine learning approaches are implemented on GPS-tracks to determine vehicles stopping at park attractions. Lastly, an agent-based model is developed in a flexible platform to utilize previous findings and evaluate the Moose-Wilson corridor while considering future policy constraints and the unique natural interactions between visitors and prevalent ecological and wildlife.

Linkages between Complex Adaptive Systems (CAS) thinking and efficiency analysis remain in their infancy. This research associates the basic building blocks of the CAS 'flocking' metaphor with the essential building block concepts of Data Envelopment Analysis (DEA). Within a proposed framework DEA "decision-making units" (DMUs) are represented as agents in the agent-based modeling (ABM) paradigm. Guided by simple rules, agent DMUs representing business units of a larger management system, 'align' with one another to achieve mutual protection/risk reduction and 'cohere' with the most efficient DMUs among them to achieve the greatest possible efficiency in the least possible time. Analysis of the resulting patterns of behavior can provide policy insights that are both evidence-based and intuitive. This research introduces a consistent methodology that will be called here the Complex Adaptive Productive Efficiency Method (CAPEM) and employs it to bridge these domains. This research formalizes CAPEM mathematically and graphically. It then conducts experimentation employing using the resulting CAPEM simulation using data of a sample of electric power plants obtained from Rungsuriyawiboon and Stefanou (2003). Guided by rules, individual agent DMUs (power plants) representing business units of a larger management system,'align' with one another to achieve mutual protection/risk reduction and 'cohere' with the most efficient DMUs among them to achieve the greatest possible efficiency in the least possible time. Using a CAS ABM simulation, it is found that the flocking rules (alignment, cohesion and separation), taken individually and in selected combinations, increased the mean technical efficiency of the power plant population and conversely decreased the time to reach the frontier. It is found however that these effects were limited to a smaller than expected sub-set of these combinations of the flocking factors. Having been successful in finding even a limited sub-set of flocking rules that increased efficiency was sufficient to support the hypotheses and conclude that employing the flocking metaphor offers useful options to decision-makers for increasing the efficiency of management systems. / Ph. D.

Information diffusion is significant in fields such as propagation prediction and influence maximization, with applications in viral marketing and rumor control. Despite conceptual differences, existing diffusion models may not represent identical underlying generative structures. A classification of diffusion of information models is developed based on infection requirements and stochasticity. The study involves analyzing seven existing DOI models on directed scale-free networks. The distinctive properties of each model are identified through simulations and analysis of experimental results. Our analysis reveals that similarity in conceptual design does not imply similarity in behavior concerning speed, the final state of nodes and edges, and sensitivity to parameters. Therefore, we highlight the importance of considering the unique behavioral characteristics of each model when selecting a suitable information diffusion model for a particular application. We further investigate how the network structure and clustering affect the diffusion of information. Our findings reveal that clustering does not consistently accelerate the spread of information. Instead, the extent to which clustering facilitates the dissemination of information is influenced by the interplay between the specific network structure types and the information diffusion model employed. Another significant aspect of information diffusion is the effect of influential nodes. Identifying highly influential nodes is of great interest for strategic targeting in various applications such as viral marketing and information campaigns. Our follow-up study aims to identify influential nodes using a transfer entropy-based method. In this work, we use our method to identify influential users in Twitter data and compare the results against other existing methods. Finally, we developed a methodology based on Transfer Entropy to evaluate influence in the context of information diffusion. This methodology demonstrated its superiority in predicting user adoption against retweet-based metrics, marking it as a direct and reliable metric for understanding influential users and information diffusion trends.

Present-day Agent Based Modelling is used to simulate complex systems in which agents are explicitly heterogeneous. Researchers within the field of ABM have a set of tools at their disposal, yet little is known about the usability and learnability of these systems. Information Architecture establishes a set of guidelines for constructing digital spaces that facilitate the fulfilment of the user’s goal; these guidelines are expressed as Principles of Information Architecture and categories of user behaviour. The purpose of this paper is to determine the needs of ABM researchers and explore how scientific software can be improved to better support them in their work. A System Usability Scale questionnaire quantifies the current level of usability on ABM frameworks while semi-structured interviews with six expert modellers provide data on user needs and user behaviour. The participants are allowed to review more than one ABM framework by means of questionnaires and a cognitive walkthrough that exposes GUI elements and other framework features linked to procedural steps of modelling. Information Architecture principles are exposed in each interface along with user behaviour categories. Albeit limited in its scope of participants, the survey with in-depth interviews provides valuable information on the needs of domain experts. Data is analysed both quantitatively and qualitatively; the paper follows, therefore, a mixed-method approach. It is proven that, at the present moment, most ABM frameworks fail to meet established standards for usability and learnability. User needs are exposed through an analysis of the data reported by experts. Finally, considerations are presented upon the impact of implementing Information Architecture guidelines within ABM frameworks.

This thesis introduces a new model to the field of social dynamics in which each node in a network moves to the mass center of the opinions in its neighborhood weighted by the changing certainty each node has in its own opinion. An upper bound of O(n) is proved for the number of timesteps until this model reaches a stable state. A second model is also analyzed in which nodes move to the mass center of the opinions of the nodes in their neighborhood unweighted by the certainty those nodes have in their opinions. This second model is shown to have a O(d) time complexity, where d is the diameter of the network, on a tree and is compared with a very similar model presented in 2013 by Frischknecht, Keller, and Wattenhofer who found a lower bound on some networks of Ω(3). 2 / Graduate

The modeling of financial risk, whose shortcomings came to the fore during the financial crisis, generally understands risk from the history of prices and returns. However, the state space of risk is not fully revealed from the history of prices and returns. In this dissertation, certain cognitive biases were modeled, and the simulation results were quantitatively characterized to reveal risk not revealed from the history of prices and returns. This contribution adds to the extant literature on the modeling of financial risk by showing how to reveal parts of the state space of risk not revealed from other methods in use today.