Zhang, YuAlmasabi, Mohammed2026-08-062026https://hdl.handle.net/20.500.14154/79776Urban Air Mobility (UAM) has emerged as an alternative urban transportation mode, characterized by its ability to bypass congestion through an environmentally friendly, safe, quiet, and on-demand electric vertical takeoff and landing (eVTOL) fleet. The success of UAM depends on strategic vertiport network design that accounts for both anticipated travel patterns and uncertain future adoption, as well as on accurate demand forecasting and pricing policies that balance affordability with system performance. This dissertation presents a comprehensive tour-based modeling framework to address these challenges and is structured as a manuscript-based dissertation comprising three self-contained yet interrelated studies. A deterministic tour-based demand and network design framework is proposed to optimally locate vertiports and estimate UAM demand. This approach improves upon trip-based models by capturing the connectivity of trips within a tour, resulting in more accurate transportation forecasting. The study employs Integer Programming (IP) to design and anticipate the UAM network and demand, with the objective of minimizing the generalized cost for travelers, a recognized strategy in transportation planning. By focusing on travelers' complete tours, the model provides a more realistic representation of actual travel experiences. A case study in the Tampa Bay Area demonstrates the application of the framework, revealing that with 10 to 100 vertiports, between 320 and 2,341 daily tours shift to UAM. Travelers exhibit strong preferences for shorter access and egress times and distances. These insights offer valuable guidance for city planners and UAM providers, indicating that UAM's success relies on vertiport placement that enhances accessibility and on strategic, system-level planning. The framework is further extended by developing a two-stage stochastic mixed-integer programming model that incorporates adoption heterogeneity. Adoption classes are defined using Rogers' diffusion of innovations framework (Innovators, Early Adopters, Early Majority, Late Majority, and Laggards), with population shares serving as scenario probabilities. Within each adoption class, travelers adopt UAM based on the class-specific switching threshold when comparing UAM with the generalized cost of ground transportation. An L-shaped Benders decomposition is implemented to ensure tractability for large-scale tour-level data. A case study in the Tampa Bay Area validates the stochastic extension and demonstrates that adoption uncertainty influences network design, resulting in broader geographic coverage and increased robustness compared to deterministic benchmarks. The findings indicate that innovators and early adopters generate the majority of UAM demand and revenue, while later adopters contribute relatively little even in dense networks. Building further upon the deterministic framework, an income-based sliding-fee policy for UAM is evaluated. Although UAM offers travel-time benefits, affordability and equitable access remain significant concerns, and structured discount instruments commonly used in public-service settings have not been systematically evaluated for UAM. An income-based sliding-fee discount schedule, linked to household income relative to the Federal Poverty Level, is embedded within the tour-based IP framework and compared to a no-discount baseline across network sizes ranging from 10 to 100 vertiports. The sliding-fee policy increases adoption at every network size, raises total fare revenue in nearly all tested conditions, and the subsidy cost per additional adopted tour declines as the network expands. The induced demand primarily originates from travelers at or below 100% of the federal poverty level, and most additional tours occur on vertiport pairs already active under the baseline, indicating that the policy broadens access within the existing network rather than altering its corridor structure. Together, these contributions advance methods for tour-based demand forecasting, robust vertiport network design under adoption uncertainty, and demand-side pricing policy for UAM. The findings emphasize that UAM viability depends on securing innovators and early adopters in the early market, that planners must design networks robust enough to accommodate uncertain long-term adoption, and that targeted pricing policies can broaden adoption while maintaining network performance and revenue. This framework offers a valuable tool for anticipating future demand and designing networks and pricing policies that remain effective across diverse adoption scenarios, from early market entry to late-stage adoption.151en-USUrban Air MobilityAdvanced Air MobilityDemand ForecastingTour-Based ModelingNetwork DesignVertiport LocationStochastic OptimizationThesis