Urban Air Mobility

Over the past several years, Urban Air Mobility (UAM) has galvanized enthusiasm from investors and researchers, marrying expertise in aircraft design, transportation, logistics, artificial intelligence, battery chemistry, and broader policymaking. However, two significant questions remain unexplored: (1) What is the value of UAM in a region’s transportation network?, and (2) How can UAM be effectively deployed to realize and maximize this value to all stakeholders, including riders and local economies? To adequately understand the value proposition of UAM for metropolitan areas, we develop...

This paper explores the addressable market for Urban Air Mobility (UAM) as a multi-modal alternative in a community. To justify public investment, UAM must serve urban mobility by carrying a significant portion of urban traffic. We develop a traffic demand analysis method to estimate the maximum number of people that can benefit from UAM, for a given use case, in a metropolitan region. We apply our method to about three hundred thousand cross-bay commute trips in the San Francisco Bay Area. We estimate the commuter demand shift to UAM under two criteria of flexibility to travel time...

Environmental factors pose a significant challenge to the operational efficiency and safety of advanced air mobility (AAM) networks. This paper presents a simulation-optimization framework that dynamically integrates wind variability into AAM operations. We employ a nonlinear charging model within a multi-vertiport environment to optimize fleet size and scheduling. Our framework assesses the impact of wind on operational parameters, providing strategies to enhance the resilience of AAM ecosystems. The results demonstrate that wind conditions exert significant influence on fleet size even...

This paper investigates the simultaneous optimization of flight schedule and charging policy of electric vertical takeoff and landing aircraft for Urban Air Mobility operations. An optimization model is developed for a two-vertiport system to design an efficient flight schedule, including re-balancing flights, and a charging policy that serves a given demand with the objective of minimizing the required fleet size. The results highlight that charging aircraft to a lower State of Charge level during peak demand periods and to a higher level during off-peak times effectively reduces the...