Today, the transport sector is responsible for nearly one-quarter of global energy-related direct carbon-dioxide (CO2) emissions and is a significant contributor to air pollution [1]. In the United States, the transportation sector has the highest share (28%) in the mix of green-house gas (GHG) sources [2]. Some of the more developed nations across the globe are now committed to improve the climate and air quality. Countries like China, Europe and the United States are front runners in introducing ambitions policies to incentivize the production and adoption of plug-in electric vehicles (PEV’s). Along with the expected benefits of PEV uptake, large scale deployment poses a challenge for the electric grid, especially at the distribution level, since the charging load of an PEV is substantial. This load is dependent not only on the characteristics of the PEV, but also on its use and charging habits of its user(s). Since a PEV can be directly plugged into the grid at any available point, which may be spatially anywhere in the utility’s service area, it is important to model its accurate use and charging behavior of the users. Having precise knowledge of the load profile, the utilities can have a better economic solution to balancing the supply and demand. In this dissertation, an agent-based model is developed that estimates the impact of charging load of PEVs on the grid. It is based on reasonably realistic diverse human behavior pertaining to day-to-day driving patterns and charging practices and their effect on each other. The model portrays the heterogenous, spatial and temporal nature of this load, which depends on the habits and the interaction among different agents. The model mimics the heterogeneity of choices made by human drivers and its effect on the charging choices of other drivers, which is an important element to consider when depicting human behavior. The model uses travel statistics of conventional personally owned vehicles (POVs) from the National Household Travel Survey (NHTS) conducted by the Federal Highway Administration (FHWA) across different states of the United States from 2016 – 2017. The travel needs are modified to incorporate the effect of EV’s limited range and charging time requirements. A modified GIS map of Collinsville, IL, is used to implement the spatial requirements of travel, with, which highlight exact load points. The agent’s travel and charging choices are modelled with heterogenous rules of engagement with the environment and other agents. Common psychological effects of limited range, long charging times, and range anticipation are applied heterogeneously to all agents to create a macro environment. The resulting charging load is superimposed on existing substation transformer load and voltage profile is analyzed to study the impact of different charging strategies and charging infrastructure availability. Different case studies are analyzed to investigate the effect of the aggregated load of multiple charging points in the respective service areas of the distribution transformers.

<p>As the need for outdoor recreation grows, the profound impact of recreational activities upon wildlife is a major concern. For example, the presence of humans may increase risk-averse behavior by wildlife, restricting access to essential resources, and reducing foraging, thereby negatively impacting breeding. Ultimately, the impacts that recreationists have on wildlife include directly or indirectly altering population structure and community composition. Unfortunately, understanding the impacts of recreating humans upon wildlife is a complex challenge that is dependent upon wildlife species and human activity types. Our understanding of human-wildlife relationships can be improved by combining results from empirical studies with simulation models to extrapolate mechanisms to a broader range of circumstances and investigate their implications. Accordingly, we developed an ABM modeling framework, that enables both dynamic virtual human and wildlife agents to change their actions. These changes are based upon their state as a consequence of their interactions with their environment and other virtual agents. A unique aspect of the framework we developed is the explicit simulation of both wildlife and human agent behavior as emergent rather than imposed. We use this framework to model the disturbance of birds, in the Lawrence Creek Forest Unit (LCFU) of Fort Harrison State Park, IN, by human recreation. We parameterize the model with human recreation data collected through an intercept survey of recreationists at the park and bird data from published studies. We compare our modeling framework to a more traditional model type where human behavior is imposed while wildlife behavior is emergent. Our results indicate that the frequency of humans entering the park influences the rates of disturbance of birds more than model types. Examining simulation behavior within our new framework, the utility and off-trail options had the most influence across all scenarios. These comparisons illustrate that the use of a modeling framework that allows managers to explore factors altering wildlife disturbance rates. Despite the marginal influence of model type upon our results, our research elucidates the value of a model that allows emergent behavior for multiple agent types. The emergent human and wildlife responses of simulated interacting agents provides new insight when managing these relationships. <b></b></p>

Der Ausgangspunkt für diese Arbeit ist das Spannungsfeld zwischen der Mobilität als Grundbedürfnis der Menschen und dem durch sie verursachten Verkehr als Herausforderung bei der Erreichung von Nachhaltigkeitszielen. Um in diesem Kontext anwendungsorientierte wissenschaftliche Erkenntnisse zu generieren, wird in der vorliegenden Arbeit die Zusammenführung der für kommunale Entscheidungsträger relevanten Konkretisierungsebenen »Visionen« und »Maßnahmen« erprobt. Das Ziel ist es, Praxisakteure für die hypothetischen zukünftigen Konsequenzen heutiger Entscheidungen zu sensibilisieren. Dazu wird ein agentenbasiertes Simulationsmodell für die alltägliche Verkehrsmittelwahl entwickelt. Es basiert auf den gleichbleibenden Mobilitätsbedürfnissen sog. Mobilitätskomfort-Typen und sich wandelnden Merkmalen der Mobilitätsangebote bis 2050. Vier Zukunftsszenarien werden für die Region Stuttgart simuliert und die Ergebnisse mit Blick auf die Gestaltung von zielgruppenspezifischen Interventionsmaßnahmen interpretiert.:Vorbemerkung und Danksagung Kurzfassung Inhaltsverzeichnis Abbildungsverzeichnis Tabellenverzeichnis Abkürzungsverzeichnis EINLEITUNG 1 Ausgangssituation und Verortung der Arbeit 1.1 Grundbedürfnis Mobilität und Herausforderung Verkehr 1.2 Forschungsbedarf und Ziele der Arbeit 1.3 Aufbau der Arbeit und Einordnung der Forschungsfragen THEORETISCHE GRUNDLAGEN 2 Das Forschungsfeld Verkehrsmittelwahl 2.1 Theoretische Auseinandersetzung mit der Verkehrsmittelwahl 2.2 Ansätze zur Gestaltung von Interventionsmaßnahmen 2.3 Urbaner Mobilitätskomfort und Mobilitätskomfort-Typen EMPIRISCHE ERHEBUNG 3 Ermittlung von Mobilitätsbedürfnissen 3.1 Methodisches Vorgehen: Datenerhebung und Datenauswertung 3.1.1 Auswahl der Workshop-Teilnehmer 3.1.2 Erhebung der Motive für die Verkehrsmittelwahl 3.1.3 Ermittlung des wahrgenommenen Mobilitätsangebots 3.1.4 Exemplarische Vorgehensweise bei der Datenauswertung 3.2 Reflexion der Vorgehensweise 3.3 Mobilitätsbedürfnisse der Mobilitätskomfort-Typen 3.3.1 Mobilitätsbedürfnisse der Flexiblen 3.3.2 Mobilitätsbedürfnisse der Anspruchsvollen 3.3.3 Mobilitätsbedürfnisse der Relaxer 3.3.4 Mobilitätsbedürfnisse der Eiligen 3.3.5 Zusammenfassender Vergleich der Mobilitätsbedürfnisse MODELLIERUNG DER VERKEHRSMITTELWAHL 4 Abstraktion des Sachverhalts 4.1 Einführung in die Modellierung und Einordnung der ABMS 4.2 Begründung für die Wahl von ABMS und NetLogo 4.3 Vorstellung des Modellierungsdesigns 4.3.1 Das Modellierungskonzept in Kürze 4.3.2 Modellumwelt – Die Setup-Prozedur 4.3.2.1 Aufbau des Modell-Interface 4.3.2.2 Globale Variablen 4.3.2.3 Ermittlung der Basispopulation 4.3.2.4 Erstellung des Angebotsrankings 4.3.2.5 Validierung der Setup-Prozedur mithilfe des Angebotsrankings 4.3.3 Entscheidungslogiken der Mobilitätskomfort-Typen – Der Go-Befehl 4.3.3.1 Aufforderung zur Angebotswahl 4.3.3.2 Hauptverkehrsmittel und Nutzungshäufigkeiten 4.3.3.3 Validierung des Modells über den Modal Split 4.4 Bewertung der Modellqualität SIMULATION DER ZUKUNFTSSZENARIEN 5 Der Wandel des Mobilitätssystems 5.1 Trends und Entwicklungen für die Mobilität bis 2050 5.2 Projektionsbündel – Input für die simulierten Zukunftsszenarien 5.2.1 Austausch der privaten und privatwirtschaftlichen Fahrzeugflotten 5.2.2 Austausch der ÖV-Fahrzeugflotte inklusive Taxiangebote 5.2.3 On-Demand-Angebote im ÖV und Förderung der Fahrradmobilität 5.2.4 Coopetition zwischen ÖV und privaten Mobilitätsanbietern 5.3 Vorstellung des Simulationsdesigns 5.4 Darstellung der Simulationsergebnisse 5.4.1 Szenario 1 Simulationsergebnisse 5.4.2 Szenario 2 Simulationsergebnisse 5.4.3 Szenario 3 Simulationsergebnisse 5.4.4 Szenario 4 Simulationsergebnisse 5.4.5 Ergebnisse je Mobilitätskomfort-Typ DISKUSSION DER ERGEBNISSE 6 Rückübertragung und Anwendungsbezug 6.1 Erkenntnisse aus der Synthese der Simulationsergebnisse 6.2 Schlussfolgerung und Perspektiven für die Wissenschaft 6.3 Zusammenfassung und Ausblick Literatur Anhang / Mobility is one of our basic needs and causes traffic, which challenges the achievement of sustainability goals. To develop applicable scientific insight in this context, the present thesis links the two precision levels »visions« and »measures«, which are relevant to municipal decision-makers. The goal is to raise consciousness for hypothetical future consequences of today’s actions and enable informed decision-making. Therefore, an agent-based simulation model is developed for daily travel mode choice. It is based upon the unchanging mobility needs of four mobility comfort user types as well as altering features of travel options until 2050. Four future scenarios are simulated for the Stuttgart region. The results are interpreted with respect to the design of target group specific intervention measure.:Vorbemerkung und Danksagung Kurzfassung Inhaltsverzeichnis Abbildungsverzeichnis Tabellenverzeichnis Abkürzungsverzeichnis EINLEITUNG 1 Ausgangssituation und Verortung der Arbeit 1.1 Grundbedürfnis Mobilität und Herausforderung Verkehr 1.2 Forschungsbedarf und Ziele der Arbeit 1.3 Aufbau der Arbeit und Einordnung der Forschungsfragen THEORETISCHE GRUNDLAGEN 2 Das Forschungsfeld Verkehrsmittelwahl 2.1 Theoretische Auseinandersetzung mit der Verkehrsmittelwahl 2.2 Ansätze zur Gestaltung von Interventionsmaßnahmen 2.3 Urbaner Mobilitätskomfort und Mobilitätskomfort-Typen EMPIRISCHE ERHEBUNG 3 Ermittlung von Mobilitätsbedürfnissen 3.1 Methodisches Vorgehen: Datenerhebung und Datenauswertung 3.1.1 Auswahl der Workshop-Teilnehmer 3.1.2 Erhebung der Motive für die Verkehrsmittelwahl 3.1.3 Ermittlung des wahrgenommenen Mobilitätsangebots 3.1.4 Exemplarische Vorgehensweise bei der Datenauswertung 3.2 Reflexion der Vorgehensweise 3.3 Mobilitätsbedürfnisse der Mobilitätskomfort-Typen 3.3.1 Mobilitätsbedürfnisse der Flexiblen 3.3.2 Mobilitätsbedürfnisse der Anspruchsvollen 3.3.3 Mobilitätsbedürfnisse der Relaxer 3.3.4 Mobilitätsbedürfnisse der Eiligen 3.3.5 Zusammenfassender Vergleich der Mobilitätsbedürfnisse MODELLIERUNG DER VERKEHRSMITTELWAHL 4 Abstraktion des Sachverhalts 4.1 Einführung in die Modellierung und Einordnung der ABMS 4.2 Begründung für die Wahl von ABMS und NetLogo 4.3 Vorstellung des Modellierungsdesigns 4.3.1 Das Modellierungskonzept in Kürze 4.3.2 Modellumwelt – Die Setup-Prozedur 4.3.2.1 Aufbau des Modell-Interface 4.3.2.2 Globale Variablen 4.3.2.3 Ermittlung der Basispopulation 4.3.2.4 Erstellung des Angebotsrankings 4.3.2.5 Validierung der Setup-Prozedur mithilfe des Angebotsrankings 4.3.3 Entscheidungslogiken der Mobilitätskomfort-Typen – Der Go-Befehl 4.3.3.1 Aufforderung zur Angebotswahl 4.3.3.2 Hauptverkehrsmittel und Nutzungshäufigkeiten 4.3.3.3 Validierung des Modells über den Modal Split 4.4 Bewertung der Modellqualität SIMULATION DER ZUKUNFTSSZENARIEN 5 Der Wandel des Mobilitätssystems 5.1 Trends und Entwicklungen für die Mobilität bis 2050 5.2 Projektionsbündel – Input für die simulierten Zukunftsszenarien 5.2.1 Austausch der privaten und privatwirtschaftlichen Fahrzeugflotten 5.2.2 Austausch der ÖV-Fahrzeugflotte inklusive Taxiangebote 5.2.3 On-Demand-Angebote im ÖV und Förderung der Fahrradmobilität 5.2.4 Coopetition zwischen ÖV und privaten Mobilitätsanbietern 5.3 Vorstellung des Simulationsdesigns 5.4 Darstellung der Simulationsergebnisse 5.4.1 Szenario 1 Simulationsergebnisse 5.4.2 Szenario 2 Simulationsergebnisse 5.4.3 Szenario 3 Simulationsergebnisse 5.4.4 Szenario 4 Simulationsergebnisse 5.4.5 Ergebnisse je Mobilitätskomfort-Typ DISKUSSION DER ERGEBNISSE 6 Rückübertragung und Anwendungsbezug 6.1 Erkenntnisse aus der Synthese der Simulationsergebnisse 6.2 Schlussfolgerung und Perspektiven für die Wissenschaft 6.3 Zusammenfassung und Ausblick Literatur Anhang

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第18255号 / 工博第3847号 / 新制||工||1590(附属図書館) / 31113 / 京都大学大学院工学研究科都市社会工学専攻 / (主査)教授 小林 潔司, 准教授 宇野 伸宏, 准教授 松島 格也 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Includes supplemental material. / Based on public law and Presidential mandate, ballistic missile defense development is a front-burner issue for homeland defense and the defense of U.S. and coalition forces abroad. Spearheaded by the Missile Defense Agency, an integrated ballistic missile defense system was initiated to create a layered defense composed of land-, air-, sea-, and space-based assets. The Ship Anti-Ballistic Response (SABR) Project is a systems engineering approach that suggests a conceptualized system solution to meet the needs of the sea portion of ballistic missile defense in the 2025-2030 timeframe. The system is a unique solution to the sea-based ballistic missile defense issue, combining the use of a railgun interceptor and a conformable aperture skin-of-the-ship radar system.

<p dir="ltr">Timely hemorrhage control is critical for preventing fatalities during active shooter incidents, particularly when interventions occur before hemorrhagic shock. Agent-Based Modeling (ABM) has been widely used to inform safety policies in such scenarios and is a well-researched domain. However, a major limitation in existing studies is the assumption that all injuries are fatal, oversimplifying the complex dynamics of injury outcomes. Real-world data paints a more nuanced picture: for example, a study of 1,226 gunshot wound patients in St. Louis emergency departments reported a 93% survival rate when victims received timely care (de Anda et al., 2018). In specific incidents, such as the 2019 Gilroy Garlic Festival shooting, rapid on-site response resulted in only 3 fatalities, while in the 2016 Orlando Pulse Nightclub shooting, studies suggest that 16 lives could have been saved with timely medical intervention (C. P. Smith et al., 2020a; E. R. Smith et al., 2018).</p><p dir="ltr">This study employed ABM to explore strategies for minimizing casualties by analyzing factors such as the spatial distribution and quantity of Hemorrhage Control Kits (HCKs), the number of trained responders, and the impact of their placement—in corridors, classrooms, or go-bags—on accessibility during lockdowns. It seeks to address the following research questions: How can survival rates in active shooter situations be improved? What impact do hemorrhage control training and the use of HCKs have on outcomes? How does the availability and distribution of medical supplies influence outcomes?</p><p dir="ltr">By incorporating models of injury progression and the stabilizing effects of timely treatment, the research revealed that increasing the number of trained responders significantly improves survival rates, with optimal results achieved when all teachers are trained in hemorrhage control. The most effective strategy combined HCKs in go-bags or on responders’ persons with well-distributed corridor stations, ensuring compatibility with lockdowns and faster response times. External responders, including tactical medics and EMTs, also benefited from these strategies due to the accessibility of medical supplies. Conversely, placing kits in individual rooms was less effective, as accessibility challenges often delayed critical interventions. This study underscores the importance of strategic planning in hemorrhage control and provides actionable insights and simulation tools to guide tailored emergency preparedness and response planning.</p>

<p dir="ltr">Understanding disease transmission is a complex and challenging task as it encompasses a wide range of intricate interactions involving pathogens, hosts, and the environment. Numerous factors, including genetics, behavior, immunity, social dynamics, and environmental conditions, contribute to the complexity. Furthermore, diseases exhibit significant variability in transmission patterns, including variations in the mode of transmission (e.g., respiratory, oral, touch-based, vector-borne), incubation period, and infectiousness. The dynamic nature of disease transmission compounds the existing challenges by introducing temporal variability and environmental variations, thereby intensifying the complexity of the study. Therefore, understanding disease transmission requires comprehensive research, integrated models, and a multidisciplinary approach to decipher the intricate web of interactions and factors involved. This dissertation aims to bridge the use and scalability gap between different levels of transmission models through the utilization of multi-paradigm modeling methods, incorporating varying levels of abstraction, to gain comprehensive insights into disease transmission. The first goal focuses on enhancing pandemic resiliency by analyzing the impact of varying parameters of heating, ventilation, and air conditioning (HVAC) on the dynamics of exhaled droplets and aerosols in the indoor environment using computational fluid dynamics (CFD) modeling. This goal operates at a micro-level of modeling, examining the detailed fluid dynamics and particle dispersion within indoor spaces. By simulating the movement of droplets under different HVAC configurations, this goal provides insights into the effectiveness of ventilation systems and optimizes parameter configurations in controlling disease transmission. The second goal of this dissertation is to aid organizations in evaluating potential policies to mitigate contact-caused risks in indoor spaces during a pandemic. This goal utilizes an ensemble of agent-based simulation (ABS) models, which operate at a higher level of abstraction. These models consider the behaviors and interactions of individuals within indoor environments, such as classrooms or meeting rooms, while incorporating physical distancing guidelines and seating policies. The third goal aims to improve pandemic prediction capabilities by developing a multivariate, spatiotemporal, deep-learning model that predicts COVID-19 hospitalization based on historical cases and evaluates the impact of state-level policy changes. This goal operates at the highest level of abstraction by utilizing deep learning techniques to analyze large-scale, publicly available data. The model captures temporal dependencies using long short-term memory (LSTM) networks and spatial dependencies using graph convolutional networks (GCN), graph attention networks (GAT), and graph transformer networks (GTN). By considering variables such as daily hospitalization and various policy changes, this approach provides a comprehensive framework for forecasting hospitalization cases and assessing policy impacts at the state level. This integrated, abstraction-based approach provides a more holistic understanding of disease transmission, allowing for the exploration of complex scenarios and the assessment of intervention impacts across different scales. This integrated architecture enables policymakers and public health professionals to develop targeted, effective strategies to mitigate the spread of diseases, allocate resources efficiently, and minimize the overall impact on public health. </p>