Efficient and fair traffic flow management for on-demand air mobility

Abstract

Abstract The increased use of drones and air-taxis is expected to make airspace resources more congested, necessitating the use of unmanned aircraft systems traffic management (UTM) initiatives to ensure safe and efficient operations. Typically, strategic UTM involves solving an optimization problem that ensures that proposed flight schedules do not exceed airspace and vertiport capacities. However, the dynamic nature and low lead-time of applications such as on-demand delivery and urban air mobility traffic may reduce the efficiency and fairness of strategic UTM. We first discuss the adaptation of three fairness metrics into a traffic flow management problem (TFMP). Then, with computational simulations of a drone package delivery scenario in Toulouse, we evaluate trade-offs in the TFMP between efficiency and fairness, as well as between different fairness metrics. We show that system fairness can be improved with little loss in efficiency. We also consider two approaches to the integrated scheduling of both high lead-time flights (i.e., flights with a schedule known in advance) and low lead-time flights in a rolling horizon optimization framework. We compare the performance of both approaches for different horizon lengths and under varying proportions of high and low lead-time flights.