Capacity model and collaborative scheduling of droneports for drone regional logistics

Droneport terminal airspace faces the critical issues of scarce airspace resources and insufficient operational efficiency. A series-coupled queuing model and a static-dynamic collaborative scheduling strategy are established to achieve accurate capacity assessment and effective regional collaborative scheduling, taking into account the lack of collaborative scheduling mechanisms for multiple droneports and the need for load balancing in regional logistics areas. First, a differentiated queuing model is constructed to characterize blockage effects in each operational link, breaking through the limitations of traditional independent analysis. Second, a non-preemptive priority strategy is introduced into the landing system to safeguard high-priority tasks. Finally, a "static flow pre-allocation + dynamic capacity-flow regulation" mechanism is designed to achieve multi-droneport collaboration through dual-path decision-making based on queuing theory. The static results show that the overall capacity assessment deviation of the proposed series-coupled queuing model is controlled within 6%, which is significantly superior to the traditional independent analysis model. Moreover, in the capacity assessment of the waiting layer airspace, it also significantly outperforms the conflict threshold-based model. Under the non-preemptive priority strategy, the average delay time of high-priority tasks consistently stays below 30 seconds. However, with the increase in their proportion of total tasks, the system’s average delay rises significantly. Under surge traffic conditions, the cooperative scheduling approach lowers overall drone delay by 39.49% on average and increases regional load balance to 95.9%. The research results can provide theoretical support for low-altitude logistics network planning and real-time dispatch.