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.

许多城市对公共交通的需求不断增加，通勤者的活动多样化，需要定期穿越市区和郊区。然而，公共交通系统仍然受到交通拥堵和绕路的影响，从而降低效率。因此，几十年来，人们已经做出了广泛的努力来优化地铁地面交通的运营，包括火车、地铁和公共汽车（二维公共交通）。另一方面，无人机(UAV)已广泛应用于城市环境中的交付、救援和监视等事件触发任务，它们在缓解二维地面公共交通压力方面具有巨大潜力。灵感来自地铁系统，AirMetro 是为未来 3D 公共航空运输服务提出的新概念，它可以定期和定期地运送乘客从一个地点到目的地的通勤。本文基于无人机的飞行路线研究了AirMetro的航班调度问题。城市环境中的低空空域按高度划分为若干层，AirMetro在分配的空域内作为公共通道进行。首先，针对网格化的城市环境提出了一种基于管道的路线规划算法，以进一步减少由A*算法生成的飞行路线的长度。与A*算法相比，基于管道的算法不需要无人机通过立方体的中心。这可能会导致更短的飞行路线。至于解决无人机之间的冲突，制定了基于最小影响的方法来延迟飞行，减少调整对无人机后续飞行的影响。通过进一步引入改进的模拟退火（MSA）算法，通过优化无人机序列进行冲突解决，可以进一步减少飞行延误。案例研究结果表明，基于轨迹的飞行调度方法可以通过确保无人机的飞行安全和降低无人机的平均延误以及 AirMetro 来提高飞行效率。通过优化无人机序列进行冲突解决，可以进一步减少飞行延误。案例研究结果表明，基于轨迹的飞行调度方法可以通过确保无人机的飞行安全和降低无人机的平均延误以及 AirMetro 来提高飞行效率。通过优化无人机序列进行冲突解决，可以进一步减少飞行延误。案例研究结果表明，基于轨迹的飞行调度方法可以通过确保无人机的飞行安全和降低无人机的平均延误以及 AirMetro 来提高飞行效率。