This dissertation conducted an extensive examination of dockless e-scooter dynamics using high-resolution trip data from Austin, Texas. Four studies were conducted to capture the multifaceted nature of e-scooter operations and demand. The first study aimed to identify and quantify the influence of contributing factors affecting e-scooter demand by partitioning the data by time period for weekdays and weekends. Utilizing a joint panel linear regression (JPLR) model, significant associations were observed between e-scooter demand and variables such as sociodemographic attributes, transportation infrastructure, land use, meteorological attributes, and situational factors. The second study shifted focus to shared e-scooter origin-destination (OD) flows in the urban region. By employing a joint binary logit-fractional split model, e-scooter OD flows were analyzed, emphasizing variations across distinct time periods and the subsequent implications for e-scooter deployment and rebalancing strategies. The third study delved into e-scooter utilization efficiency, introducing a time-to-book (TtB) measure. Through a Mixed Grouped Ordered Logit (MGOL) model, the study highlighted variations between regular and peak weeks, offering operators a chance to enhance fleet utilization. The final study addressed the broader context of the e-scooter industry, investigating the impact of the COVID-19 pandemic. By analyzing datasets spanning January 2019 through December 2021, a spatial approach illuminated changes in e-scooter demand patterns before, during, and after the pandemic, highlighting the effects of COVID-19-related factors and vaccine attributes on e-scooter trends. These collective insights from the four studies provide valuable contributions to understanding and enhancing e-scooter operations in urban landscapes

Mass spectrometry has frequently been employed in the analysis of biologically relevant molecules; however, mass spectrometry alone may not always be sufficient for the differentiation and characterization of isomeric and isobaric ions. In this work, infrared multiple photon dissociation (IRMPD) spectroscopy and ion mobility spectrometry (IMS) were evaluated as complementary techniques for the characterization and separation of isomeric and isobaric ions of biological relevance. In the first project, analysis of experimental IRMPD spectroscopy data shows that this technique is useful in the differentiation of hydroxyproline isomers. Absorption bands allow for the differentiation of three isomeric species: 1640 cm-1 (trans-4-hydroxyproline), 1718 cm-1 (cis-4-hydroxyproline), and 1734 cm-1 (cis-3-hydroxyproline). In the second project, theoretical CCS and IR spectroscopy predictions of isobaric modified amino acids and isomeric drugs have been carried out as predictions of IMS and IRMPD spectroscopy suitability. Preliminary IMS measurements suggest that the CCS predictions are at least qualitatively useful.

Mobility modeling is a critical step in the ground vehicle acquisition process for military vehicles. Mobility modeling tools, and in particular the NATO Reference Mobility Model (NRMM), have played a critical role in understanding the mission-level capabilities of ground vehicles. This understanding via modeling supports not only developers during early vehicle design but also decision makers in the field previewing the capabilities of ground vehicles in real-world deployments. Due to decades of field testing and operations, mobility modeling for traditional ground vehicles is well-understood; however, mobility modeling tools for evaluating autonomous mobility are sparse. Therefore, this dissertation proposes and derives a Reference Autonomous Mobility Model (RAMM). The RAMM leverages cutting-edge modeling and simulation tools to build a mobility model that serves as the mission-level mobility modeling tool currently lacking in the unmanned ground vehicle (UGV) community, thereby filling the current analysis gap in the autonomous vehicle acquisition cycle. The RAMM is built on (1) a thorough review of theories of verification and validation of simulations, (2) a novel framework for validating simulations of autonomous systems and (3) the mobility modeling framework already established by the NRMM. These building blocks brought to light the need for new, validated modeling and simulation (M&S) tools capable of simulating, at a highidelity, autonomous unmanned ground vehicle operations. This dissertation maps the derivation of the RAMM, starting with a history of verification of simulation models and a literature review of current autonomous mobility modeling methods. In light of these literature reviews, a new framework for V&V of simulations of autonomous systems is proposed, and the requirements for and derivation of the RAMM is presented. This dissertation concludes with an example application of the RAMM for route planning for autonomous UGVs. Once fully developed, the RAMM will serve as an integral part in the design, development, testing and evaluation, and ultimate fielding of autonomous UGVs for military applications.

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