Trajectory-based flight scheduling for AirMetro in urban environments by conflict resolution

The demand on the public transportation in many cities has been increasing with the diversity of commuters' activities that require the regular travelling across the urban and suburb areas. However, the public transport systems still suffer from the traffic congestion and detour, which will then reduce the efficiency. Accordingly, extensive efforts have been made for decades to optimize the operations of metro ground traffic with trains, subways, and buses (2D public transportation). On the other hand, unmanned aerial vehicles (UAVs) have been widely applied in the event-triggered tasks, such as delivery, rescue and surveillance in urban environments, and they have a great potential to relieve the pressure in 2D ground public transportation. Inspired from metro systems, AirMetro is a new concept proposed for future 3D public air transportation service, which carries the passengers commuting from a point to the destination, routinely and periodically. In this paper, the flight scheduling problem for AirMetro is studied based on the flight route of UAVs. The low-altitude airspace in urban environments is divided into several layers by altitude, and AirMetro is conducted in the allocated airspace as the public lanes. First, a pipeline-based route planning algorithm is proposed for the gridded urban environments to further reduce the length of a flight route generated by A* algorithm. The UAV is not required to pass through the centre of a cube in the pipeline-based algorithm when compared with the A* algorithm. This can then potentially result in a shorter flight route. As for resolving the conflict among UAVs, a minimum influence-based approach is developed to delay the flight and reduce the influence of adjustment on the follow-up flights of the UAV. By further introducing a modified simulated annealing (MSA) algorithm, the delay of flight can be further reduced by optimizing the UAV sequence conducting the conflict resolution. Results of the case studies demonstrate that the trajectory-based flight scheduling method can improve the flight efficiency by ensuring the flight safety of UAVs and reducing the average delay of UAVs as well in AirMetro.