The Federal Aviation Administration introduced the concept of urban air mobility (UAM), a new three-dimensional transport system that operates with a fusion of manned/unmanned aerial vehicles on an urban or intercity scale. The rapid development of UAM has brought innovation and dynamism to many industries, especially in the field of logistics. Various types of unmanned aerial vehicles (UAVs) for use in transport logistics are being designed and produced. UAV logistics refers to the use of UAVs, usually carrying goods and parcels, to achieve route planning, identify risk perception, facilitate parcel delivery, and carry out other functions. This research provides a method for assessing the operational capacity of a UAV logistics route network. The concept of “logistics UAV route network operation capacity” is defined, and a bi-objective optimization model for assessing the route network’s operating capacity is developed. The first objective is to maximize the number of UAV logistics delivery plans that can be executed in a fixed operation time. The second objective is to minimize the total operational impedance value in a fixed operation time. To solve the bi-objective optimization model, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is utilized. A UAV logistics route network with 62 nodes is developed to assess the rationale and validity of the proposed concept. The experiments show that with an increase in operation time, the route network’s optimal operational capacity gradually increases, the convergence speed of the algorithm slows down, and the optimization magnitude gradually reduces. Two key parameters—operational safety interval and flight speed—are further analyzed in the experiments. According to the experiments, as the safety interval increases, the route network’s average operational capacity steadily diminishes, as does its sensitivity to the safety interval. The average operational capacity steadily rose with the rise in flight speed, especially when the UAV logistics flight speed was between 10 m/s and 10.5 m/s. In that range, the operational capacity of the route network was substantially impacted by the flight speed.

Unmanned aerial vehicle (UAV) formation flying is an efficient and economical operation mode for air transportation systems. To improve the effectiveness of synergetic formation control for UAVs, this paper proposes a pairwise conflict resolution approach for UAV formation through mathematical analysis and designs a dynamic pairing and deep reinforcement learning framework (P-DRL formation control framework). Firstly, a new pairwise UAV formation control theorem is proposed, which breaks down the multi-UAVs formation control problem into multiple sequential control problems involving UAV pairs through a dynamic pairing algorithm. The training difficulty of Agents that only control each pair (two UAVs) is lower compared to controlling all UAVs directly, resulting in better and more stable formation control performance. Then, a deep reinforcement learning model for a UAV pair based on the Environment–Agent interaction is built, where segmented reward functions are designed to reduce the collision possibility of UAVs. Finally, P-DRL completes the formation control task of the UAV fleet through continuous pairing and Agent-based pairwise formation control. The simulations used the dynamic pairing algorithm combined with the DRL architectures of asynchronous advantage actor–critic (P-A3C), actor–critic (P-AC), and double deep q-value network (P-DDQN) to achieve synergetic formation control. This approach yielded effective control results with a strong generalization ability. The success rate of controlling dense, fast, and multi-UAV (10–20) formations reached 96.3%, with good real-time performance (17.14 Hz).

City air traffic as a new transportation mode has gradually attracted attention in recent years which will bring endless vitality to future urban development. An objective and accurate assessment of the vertiport capacity for UAVs (Unmanned Aerial Vehicles) is the basis for implementing air traffic flow management for UAVs, which is also a prerequisite for improving the efficiency of urban airspace resources used. Firstly, new topology designs are proposed and named as connected and compact topology designs based on the existing central airport topology design. Subsequently, three modes of operation are summarized for vertiports with multiple TLOF pads: independent operation, dependent operation, and segregated operation. In the next place, the overall traffic flow of the vertiport model is established based on AnyLogic while analyzing the logic of UAV operation in three modes as mentioned above. Eventually, according to the simulation results, the vertiport operation capacity, the UAVs delay, and surface area utilization under different operation modes and topology designs are analyzed. The simulation result shows that the overall average delay time of UAVs for independent operation mode is about 100 s less than that of segregated operation and it also shows that the utilization rate of independent operation mode under central design is as high as 54.42% while the utilization rate of TLOF pads of other design is less than 50%, and its vertiport capacity is the largest, so the independent operational mode under central configuration is the optimal combination.

With the development of modern military ship equipment, the airspace operation environment of shipboard aircraft is becoming increasingly complex. The objective and accurate evaluation of ship-terminal capacity is the basis of air traffic flow management for shipboard aircraft, and it is also the premise of improving the efficiency of airspace resources. First of all, this paper divides the airspace and ship terminal areas and realizes the route network planning for the arrival and departure of shipboard aircraft. Following this, according to the airspace characteristics of the ship terminal area and the operation process of the shipboard aircraft, an arrival-and-departure network flow model for shipboard aircraft is established by using capacity-limitation and flow-conservation conditions. Finally, simulated annealing (SA) is used to solve the maximum flow in the arrival-and-departure network flow model, and the capacity evaluation results of the ship terminal area are obtained. The results show that when the number of gates is n_g ≥ 7, the bottleneck in the ship terminal area’s operation capacity is the deck runway. When 3 ≤ n_g < 7, imbalanced take-off and landing tasks lead to a waste of runway resources, and when n_g < 3, the number of gates becomes the bottleneck which limits the capacity. With the number of gates being reduced from seven to three, the capacity is reduced from twenty sorties per hour to six sorties per hour. The model and core idea proposed in this paper can not only quickly evaluate the capacity of the terminal area of ships but also provide a solid foundation for the development of future fleet groups and the full use of maritime airspace.