Yong Tian

The configuration of urban low‐altitude vertiports directly influences the efficiency and convenience of urban air mobility (UAM) transportation. This study aims to optimize the urban low‐altitude VTOL vector network (VVN) by integrating urban commuting and air–ground intermodal transportation. This study develops a data‐driven framework for city‐scale eVTOL aircraft vector network planning by integrating multi‐source geospatial data, mobile signaling‐based crowd OD flows, and an air–ground intermodal location model. Building on existing network design formulations, this study uses crowd OD flows rather than individual trips as the unit of analysis. More importantly, it proposes a pruning‐and‐reduction strategy that makes the resulting mixed‐integer optimization computationally tractable with massive amounts of observed OD data. The empirical analysis takes Nanjing, a first‐tier city in China, as the research area, integrating multi‐source large‐scale geospatial data, especially mobile signaling origin–destination (OD) flow data. The experimental results show that the pruning strategy reduces the variable scale by 99.79% while preserving the near‐equivalent solution quality. The optimized VVN layout significantly improves long‐distance trip service accessibility and overall benefits. With respect to air–ground intermodal trips, the flight segment accounts for approximately 16% of the total travel time, whereas the first and last‐mile connection times account for 84%. Therefore, it is necessary to optimize accessibility between residential communities and vertiport stations, improve interconnections for ground transportation, and enhance the construction of parking facilities at vertiports. This study implements data‐driven modeling by fusing multi‐source spatiotemporal data and coupling air–ground intermodal trip transfer/connection decisions, providing methodological references for optimizing VVN location selection in multi‐modal transportation coordination.

Precise trajectory prediction in the airspace of a high-density terminal area (TMA) is crucial for Trajectory Based Operations (TBO), but frequent aircraft interactions and maneuvering behaviors can introduce significant uncertainties. Most existing approaches use deterministic deep learning models that lack uncertainty quantification and explicit spatial awareness. To address this gap, we propose the BST-Transformer, a Bayesian spatio-temporal deep learning framework that produces probabilistic multi-step trajectory forecasts and supports probabilistic conflict alerting. The framework first extracts temporal and spatial interaction features via spatio-temporal attention encoders and then uses a Bayesian decoder with variational inference to yield trajectory distributions. Potential conflicts are evaluated by Monte Carlo sampling of the predictive distributions to produce conflict probabilities and alarm decisions. Experiments based on real SSR data from the Guangzhou TMA show that this model performs exceptionally well in improving prediction accuracy by reducing MADE 60.3% relative to a deterministic ST-Transformer with analogous reductions in horizontal and vertical errors (MADHE and MADVE), quantifying uncertainty and significantly enhancing the system’s ability to identify safety risks, and providing strong support for intelligent air traffic management with uncertainty perception capabilities.

The growing adoption of unmanned aerial vehicles (UAVs) for urban parcel delivery highlights the need for efficient distribution center placement. Particularly in dense urban environments, obstacle avoidance usually increases real path distances beyond straight-line measurements. Considering these distance differences, this study first employs the Informed-RRT* algorithm with spatial indexing to calculate real UAV flight paths between locations. We use the real path distances instead of straight-line distances to determine service coverage areas in the distribution center layout model. In this model, we aim to minimize the total economic cost and maximize customer satisfaction, considering the service range, the balance between delivery capability and demand, and the constraints of the distribution mode. An immune-algorithm-enhanced genetic algorithm, the immune genetic algorithm, is used to solve the model, acquiring the distribution center layout. We conduct a simulation experiment in Shenzhen’s low-altitude airspace and compare the layout results between the real path distance criterion and the straight-line path distance criterion. The results show that using the real path distances instead of the straight-line distances leads to changes in distribution center layout, demonstrating the necessity of this framework.

As air transportation demand continues to rise, congestion in air route networks has seriously compromised the safe and efficient operation of air traffic. Few studies have examined the spatiotemporal characteristics of congestion propagation under different time lag conditions. To address this gap, this study proposes a cross-segment congestion-propagation causal time-lag analysis framework. First, to account for the interdependency across segments in air route networks, we construct a point–line congestion state assessment model and introduce the FCM-WBO algorithm for precise congestion state identification. Next, the Multi-Channel Attention DSNG-BiLSTM model is designed to estimate the causal weights of congestion propagation between segments. Finally, based on these causal weights, two indicators—CPP and CPF—are derived to analyze the spatiotemporal characteristics of congestion propagation under various time lag levels. The results indicate that our method achieves over 90% accuracy in estimating causal weights. Moreover, the propagation features differ significantly in their spatiotemporal distributions under different time lags. Spatially, congestion sources tend to spread as time lag increases. We also identify segments that are likely to become overloaded, which serve as the primary receivers of congestion. Temporally, analysis of time-lag features reveals that because of higher traffic flow during peak periods, congestion propagates 36.92% more slowly than during the early-morning hours. By analyzing congestion propagation at multiple time lags, controllers can identify potential congestion sources in advance. They can then implement targeted interventions during critical periods, thereby alleviating congestion in real time and improving route-network efficiency and safety.

Trajectory prediction serves as a prerequisite for future trajectory-based operation, significantly reducing the uncertainty of aircraft movement information within airspace by scientifically forecasting the three-dimensional positions of aircraft over a certain period. As convergence points in the aviation network, airport terminal airspace exhibits the most complex traffic conditions in the entire air route network. It has stronger mutual influences and interactions among aircraft compared to the en-route phase. Current research typically uses the trajectory time series information of a single aircraft as input for subsequent predictions. However, it often lacks consideration of the close-range spatial interactions between multiple aircraft in the terminal airspace. This results in a gap in the study of aircraft trajectory prediction that couples spatiotemporal features. This paper aims to predict the four-dimensional trajectories of aircraft in terminal airspace, constructing a Spatio-Temporal Transformer (ST-Transformer) prediction model based on temporal and spatial attention mechanisms. Using radar aircraft trajectory data from the Guangzhou Baiyun Airport terminal airspace, the results indicate that the proposed ST-Transformer model has a smaller prediction error compared to mainstream deep learning prediction models. This demonstrates that the model can better integrate the temporal sequence correlation of trajectory features and the potential spatial interaction information among trajectories for accurate prediction.

As explainable artificial intelligence (XAI) has grown in popularity, it has become possible to more clearly explain the functional correspondence between airspace and traffic flow, which can alleviate the contradiction between the continuously increasing traffic demand in the terminal area and the limited airspace capacity; this paper studies the three-dimensional sectorization in the terminal area based on the airspace function. First, trajectory clustering is adopted to classify the functions of air traffic flows. Then, a functional sectorization framework is proposed to enable different airspace sectors with varied functions, where specifically the functional consistency objective between the airspace sector and the traversed air traffic flows is proposed. Third, a corresponding efficient algorithm is designed to generate functional sectors with an accurate scope that can well separate different traffic flows. Finally, the proposed method is evaluated by the actual operation datasets in the Shanghai terminal area. The results show that the method proposed in this paper can not only generate three-dimensional sectors with specific functions according to the prevailing traffic flow in the complex terminal area, which is conducive to the construction of controllers’ situational awareness, but also can reduce potential conflicts and traffic density variance, increasing average sector flight time a lot.

Predicting flight delays plays a critical role in reducing financial losses and increasing passenger satisfaction. Due to their ability to combine multiple algorithms, ensemble methods have demonstrated strong predictive performance in many research fields. In this paper, ensemble methods are adopted to predict flight delays. First, based on the current studies, two novel explanatory variables, named arrival/departure pressure and cruise pressure, are proposed as factors affecting flight delays. Second, we introduce the ensemble methods and select representative algorithms for the prediction problem. In addition to the ensemble methods, classical algorithms are also used to predict flight delays. Finally, the actual operational data of Beijing Capital International Airport were utilized to conduct a case study. The results show that the stacking method has better prediction performance than other baseline methods. The mean absolute error (MAE) of the stacking method was about 12.58 min on the test dataset. Furthermore, we tested the effect of the two explanatory variables proposed in this paper, and the results show that the MAE was reduced by about 20% by using the stacking method.