A reinforcement learning approach to vehicle coordination for structured advanced air mobility

Advanced Air Mobility(AAM)has emerged as a pioneering concept designed to optimize the efficacy and ecological sustainability of air transportation.Its core objective is to provide highly automated air transportation services for passengers or cargo,operating at low altitudes within urban,suburban,and rural regions.AAM seeks to enhance the efficiency and environmental viability of the aviation sector by revolutionizing the way air travel is conducted.In a complex aviation environment,traffic management and control are essential technologies for safe and effective AAM operations.One of the most difficult obstacles in the envisioned AAM systems is vehicle coordination at merging points and intersections.The escalating demand for air mobility services,particularly within urban areas,poses significant complexities to the execution of such missions.In this study,we propose a novel multi-agent reinforcement learning(MARL)approach to efficiently manage high-density AAM operations in structured airspace.Our approach provides effective guidance to AAM vehicles,ensuring conflict avoidance,mitigating traffic congestion,reducing travel time,and maintaining safe separation.Specifically,intelligent learning-based algorithms are developed to provide speed guidance for each AAM vehicle,ensuring secure merging into air corridors and safe passage through intersections.To validate the effectiveness of our proposed model,we conduct training and evaluation using BlueSky,an open-source air traffic control simulation environment.Through the simulation of thousands of aircraft and the integration of real-world data,our study demonstrates the promising potential of MARL in enabling safe and efficient AAM operations.The simulation results validate the efficacy of our approach and its ability to achieve the desired outcomes.

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 takeoff 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.

VTOL site location considering obstacle clearance during approach and departure

Urban air mobility(UAM)extends urban transportation to low-altitude airspace using electric vertical take-off and landing(eVTOL)vehicle to reduce traffic congestion.The vertical take-off and landing(VTOL)site connecting ground and air transport is the critical infrastructure of the UAM.Determining its locations is essential for the design and operation of the air route.This study focuses on the problem of the location of the VTOL site,using Shenzhen as the study area,and establishes an integer programming model with the objective of maximizing travel cost savings to identify the optimal locations of the VTOL sites.This study is different from existing ones in that it explicitly considers the three-dimensional spatial availability of VTOL sites.Geographic information system(GIS)tools are used to identify locations that satisfy two-dimensional(2D)planar availability,and an obstacle assessment model of the approach/departure and transitional surfaces of the VTOL site is built to further screen the locations.The selected potential sites are used as input to the integer programming model,ensuring that the locations identified to establish the VTOL site are optimal.The impact of the number of VTOL sites,the user's transfer time at the VTOL sites,and the eVTOL pricing on the model solution is also discussed.Although this study uses Shenzhen as a research object,the proposed methodology is generalized and applicable to any other city or region,providing recommendations and references for initial planning and related operations of the UAM in selected areas.