Nationwide Demand Modeling for an Urban Air Mobility Commuting Mission
Mark T. Kotwicz Herniczek,
Brian J. German
Abstract:In this paper, we present a comprehensive and reproducible urban air mobility (UAM) demand model centered around publicly available data and open source tools capable of demand estimation at the national level. A discrete mode-choice demand model is developed using longitudinal origin–destination employment statistics flow data, American community survey economic data, and the Open Source Routing Machine (OSRM) to identify the utility of a UAM commuter service relative to other modes of transportation. Using t… Show more
An understanding of fleet size and vertiport size sensitivity to demand and operational parameters is necessary to quantify the scalability of urban air mobility (UAM) services. In this work, we implement a bilevel rolling window fleet scheduling formulation that includes vertiport area as a secondary objective. We also present a simple vertiport area estimation methodology that leverages the fleet scheduling results and provides a lower bound on vertiport infrastructure area requirements. Lastly, we explore the sensitivity of fleet size and vertiport infrastructure requirements to several vehicle and operational parameters, including geographical demand distribution, daily passenger volume, vehicle passenger capacity, passenger aggregation window, battery charge rate, pad separation, and pad size. We find that, although the fleet size is reasonable for a UAM commuting service scaled to serve 10,000 passengers per day, vertiport area requirements are likely problematic under current sizing guidance from the Federal Aviation Administration, particularly area requirements for vertiports that serve as workplace hubs located in dense urban centers.
An understanding of fleet size and vertiport size sensitivity to demand and operational parameters is necessary to quantify the scalability of urban air mobility (UAM) services. In this work, we implement a bilevel rolling window fleet scheduling formulation that includes vertiport area as a secondary objective. We also present a simple vertiport area estimation methodology that leverages the fleet scheduling results and provides a lower bound on vertiport infrastructure area requirements. Lastly, we explore the sensitivity of fleet size and vertiport infrastructure requirements to several vehicle and operational parameters, including geographical demand distribution, daily passenger volume, vehicle passenger capacity, passenger aggregation window, battery charge rate, pad separation, and pad size. We find that, although the fleet size is reasonable for a UAM commuting service scaled to serve 10,000 passengers per day, vertiport area requirements are likely problematic under current sizing guidance from the Federal Aviation Administration, particularly area requirements for vertiports that serve as workplace hubs located in dense urban centers.
Vertiport locations have a significant impact on the time savings provided by an urban air mobility (UAM) commuting service relative to ground transportation and, thereby, greatly affect the value proposition and demand for UAM commuting services. In this paper, we present a discrete combinatorial vertiport placement method with a flexible objective function capable of directly optimizing for commuting demand. Preprocessing and postprocessing formulations that effectively reduce problem size and increase solution quality are also described. Demand-maximizing vertiport placement results are provided for Atlanta, New York City, San Francisco, and Seattle. We also present results showing the sensitivity of potential commuting demand to number of vertiports, ticket price, and service delay. Additionally, results that illustrate the impact of limiting vertiport placement to locations with existing airport and helipad infrastructure are presented. Finally, vertiport placement results optimized using a capacitated profitability-maximization objective are outlined.
Urban Air Mobility (UAM) emerges as a transformative approach to address urban congestion and pollution, offering efficient and sustainable transportation for people and goods. Central to UAM is the Operational Digital Twin (ODT), which plays a crucial role in real-time management of air traffic, enhancing safety and efficiency. This study introduces a YOLOTransfer-DT framework specifically designed for Artificial Intelligence (AI) training in simulated environments, focusing on its utility for experiential learning in realistic scenarios. The framework’s objective is to augment AI training, particularly in developing an object detection system that employs visual tasks for proactive conflict identification and mission support, leveraging deep and transfer learning techniques. The proposed methodology combines real-time detection, transfer learning, and a novel mix-up process for environmental data extraction, tested rigorously in realistic simulations. Findings validate the use of existing deep learning models for real-time object recognition in similar conditions. This research underscores the value of the ODT framework in bridging the gap between virtual and actual environments, highlighting the safety and cost-effectiveness of virtual testing. This adaptable framework facilitates extensive experimentation and training, demonstrating its potential as a foundation for advanced detection techniques in UAM.
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