“…Most of these estimate the AAM demand for specific regions to design a suitable network. The regions are the USA [14,15] (New York [16], Tampa [17], San Fran-cisco [18][19][20]), Asia (Seoul [21,22]), and Europe (Paris [20], Munich [20,23,24], Zurich [25]). Most of these regions can be characterized by smaller areas with a high population density to overcome the crowded transport situation on the ground.…”
Introducing Advanced Air Mobility (AAM) as a novel transportation mode poses unique challenges due to limited practical and empirical data. One of these challenges involves accurately estimating future passenger demand and the required number of air taxis, given uncertainties in modal shift dynamics, induced traffic patterns, and long-term price elasticity. In our study, we use mobility data obtained from a Dresden traffic survey and modal shift rates to estimate the demand for AAM air taxi operations for this regional use case. We organize these operations into an air taxi rotation schedule using a Mixed Integer Linear Programming (MILP) optimization model and set a tolerance for slight deviations from the requested arrival times for higher productivity. The resulting schedule aids in determining the AAM fleet size while accounting for flight performance, energy consumption, and battery charging requirements tailored to three distinct types of air taxi fleets. According to our case study, the methodology produces feasible and high-quality air taxi flight rotations within an efficient computational time of 1.5 h. The approach provides extensive insights into air taxi utilization, charging durations at various locations, and assists in fleet planning that adapts to varying, potentially uncertain, traffic demands. Our findings reveal an average productivity of 12 trips per day per air taxi, covering distances from 13 to 99 km. These outcomes contribute to a sustainable, business-focused implementation of AAM while highlighting the interaction between operational parameters and overall system performance and contributing to vertiport capacity considerations.
“…Most of these estimate the AAM demand for specific regions to design a suitable network. The regions are the USA [14,15] (New York [16], Tampa [17], San Fran-cisco [18][19][20]), Asia (Seoul [21,22]), and Europe (Paris [20], Munich [20,23,24], Zurich [25]). Most of these regions can be characterized by smaller areas with a high population density to overcome the crowded transport situation on the ground.…”
Introducing Advanced Air Mobility (AAM) as a novel transportation mode poses unique challenges due to limited practical and empirical data. One of these challenges involves accurately estimating future passenger demand and the required number of air taxis, given uncertainties in modal shift dynamics, induced traffic patterns, and long-term price elasticity. In our study, we use mobility data obtained from a Dresden traffic survey and modal shift rates to estimate the demand for AAM air taxi operations for this regional use case. We organize these operations into an air taxi rotation schedule using a Mixed Integer Linear Programming (MILP) optimization model and set a tolerance for slight deviations from the requested arrival times for higher productivity. The resulting schedule aids in determining the AAM fleet size while accounting for flight performance, energy consumption, and battery charging requirements tailored to three distinct types of air taxi fleets. According to our case study, the methodology produces feasible and high-quality air taxi flight rotations within an efficient computational time of 1.5 h. The approach provides extensive insights into air taxi utilization, charging durations at various locations, and assists in fleet planning that adapts to varying, potentially uncertain, traffic demands. Our findings reveal an average productivity of 12 trips per day per air taxi, covering distances from 13 to 99 km. These outcomes contribute to a sustainable, business-focused implementation of AAM while highlighting the interaction between operational parameters and overall system performance and contributing to vertiport capacity considerations.
This study investigates the integration of Control Moment Gyroscopes (CMGs) to enhance the comfort of human occupants in electric Vertical Take-Off and Landing (eVTOL) aircraft. Our study encompasses not only the development of a dynamic model for the eVTOL by integrating the CMGs but also the implementation of backstepping sliding mode-based controllers for translation and attitude control. To simulate realistic disturbance scenarios, wind disturbance models and a motor dynamics model are considered to replicate practical rotor responses. To validate the performance of the proposed approach, comprehensive Monte-Carlo simulations under varying wind conditions are performed. In particular, the aircraft oscillations are analyzed in the frequency domain to focus on a specific frequency that causes human discomfort. The simulation results demonstrate that the use of CMGs not only alleviates oscillations induced by wind disturbances with low power consumption but also significantly enhances passenger comfort.
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