The popularity of unmanned aerial vehicle (UAV) technology has made UAV logistics transportation possible. However, based on the current development status of logistics UAVs, there are difficulties in using UAVs directly for door-to-door logistics transportation. Therefore, it is necessary to establish UAV parcel-receiving stations that can gather logistics needs in a small area. The construction of stations allows the UAVs to transport back and forth between the distribution warehouse and the established stations, enabling customers to send and receive packages at the more convenient stations. Based on the current situation, it is a more appropriate air–ground cooperative transport mode to solve the “last-mile” cargo transportation problem. In this paper, a bi-layer UAV parcel-receiving station and public air-route planning method is proposed to explore the interaction between station location and public route planning, and is solved with a genetic algorithm and max–min ant system (GA-MMAS). The model proposed in this paper can determine the location of the stations and plan the public air routes between the warehouse and stations simultaneously. Simulation results show that the planning results of the bi-layer optimization model proposed in this paper meet the requirements of station location and public air-route planning. Compared with the layered planning results, the cost of the upper-layer model is reduced by 5.12% on average, and the cost of the lower-layer model is reduced by 4.48%.

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.

The terminal area is a convergence point for inbound and outbound traffic, and it is characterized by a complex airspace structure and high traffic density. It is an area that frequently experiences flight congestion and ground delays. A system capable of the intelligent, reliable, timely, and accurate identification of air traffic congestion for air–ground coupled flight flow constitutes a key technology with respect to unlocking the potential capacity of the terminal area, mitigating traffic congestion, and assisting air-traffic-control-related decision making. Therefore, this article aims to extract and analyze the multi-scale and multi-dimensional evaluation indicators of air–ground coupled flight flow congestion, use the TSNE-FCM algorithm to classify congestion levels, and, based on this work, construct a real-time and fast congestion identification model using the LightGBM algorithm. The case study analyzed China Baiyun Airport (CAN), and the experimental results indicate the following: (1) The congestion level classification achieved using the TSNE-FCM algorithm is superior to that achieved using the FCM algorithm. Furthermore, flight delays predominantly occur in slightly congested and congested states. (2) The congestion identification model based on LightGBM outperforms the XGBoost, RandomForest, and ExtraTree models. The macro-average and micro-average AUC curve areas for the LightGBM model were 0.96 and 0.96, respectively. The LightGBM model demonstrates excellent performance and is suitable for identifying congestion levels in practical engineering applications.