Integrated Network Design and Demand Forecast for On-Demand Urban Air Mobility

Urban air mobility (UAM) is an emerging concept proposed in recent years that uses electric vertical take-off and landing vehicles (eVTOLs). UAM is expected to offer an alternative way of transporting passengers and goods in urban areas with significantly improved mobility by making use of low-altitude airspace. In addition to other essential elements, ground infrastructure of vertiports is needed to transition UAM from concept to operation. This study examines the network design of UAM on-demand service, with a particular focus on the use of integer programming and a solution algorithm to determine the optimal locations of vertiports, user allocation to vertiports, and vertiport access- and egress-mode choices while considering the interactions between vertiport locations and potential UAM travel demand. A case study based on simulated disaggregate travel demand data of the Tampa Bay area in Florida, USA was conducted to demonstrate the effectiveness of the proposed model. Candidate vertiport locations were obtained by analyzing a three-dimensional (3D) geographic information system (GIS) map developed from lidar data of Florida and physical and regulation constraints of eVTOL operations at vertiports. Optimal locations of vertiports were determined to achieve the minimal total generalized cost; however, the modeling structure allows each user to select a better mode between ground transportation and UAM in terms of generalized cost. The outcomes of the case study reveal that although the percentage of trips that switched from ground mode to multimodal UAM was small, users choosing the UAM service benefited from significant time saving. In addition, the impact of different parameter settings on the demand for UAM service was explored from the supply side, and different pricing strategies were tested that might influence potential demand and revenue generation for UAM operators. The combined effects of the number of vertiports and pricing strategies were also analyzed. The findings from this study offer in-depth planning and managerial insights for municipal decision-makers and UAM operators. The conclusion of this paper discusses caveats to the study, ongoing efforts by the authors, and future directions in UAM research.