As the value and importance of urban air mobility (UAM) are being recognized, there is growing attention towards UAM. To ensure that urban air traffic can serve passengers to the greatest extent while ensuring safety and generating revenue, there is an urgent need for a transportation scheduling plan based on safety considerations. The region of Beijing–Tianjin–Hebei was selected as the case study in this research. A real-time demand transportation scheduling model for a single day was constructed, with the total service population and total cost as objective functions, and safety intervals, eVTOL performance, and passenger maximum waiting time as constraints. A Joint Optimization of Cost and Scheduling Particle Swarm Optimization (JOCS-PSO) algorithm was utilized to obtain the optimal solution. The optimal solution obtained in this study can serve 138,610,575 passengers during eVTOLs’ entire lifecycle (15 years) with a total cost of CNY 368.57 hundred million, with the cost of CNY 265.9 per passenger. Although it is higher than the driving cost, it saves 1–1.5 h and thus has high cost effectiveness during rush hours.

Complex and diverse multi-attribute flight data can provide data-driven opportunities for airport flight delay prediction. However, it is a challenge to effectively and efficiently process multi-attribute flight data. This paper proposes a hybrid dynamic spatial-temporal long short-term memory network (LSTM) with 3D-directional multi-attribute features (3DF-DSCL) for departure flight delay prediction. The model is based on a 3D convolutional neural network (3D-CNN), graph convolutional network (GCN) and long short-term memory networks (LSTM) model. Firstly, the dataset divides the state and environment of departure flight delay into three situations, including the dynamic operation link, which integrates the trajectory system of aircraft movement in the terminal area, the network congestion link caused by aircraft multi-area movement in the air and ground, and other delay factors determined by the airport take-off and landing requirements. Multi-attribute data are divided into time series, spatial-temporal network and dynamic moving trajectory grid input variables. Among them, the spatial network and dynamic moving trajectory grid data are the inputs of GCN and 3D CNN models, which aim to extract spatial-temporal features. The time series input variables are fed into LSTM. These features are then integrated and fed into LSTM for flight delay prediction, where the flight delay of airport outbound flights is taken as the output variable. The case study shows that the proposed method can significantly improve the accuracy of flight prediction delay. The Mean Absolute Error (MAE) can reach 0.26, which is a 14.47% reduction compared with 2D CNN+GCN+LSTM.

The air transportation system is composed of multiple elements and belongs to a complex socio-technical system. It is difficult to assess the risk of an aircraft fault because it could constantly change during operation and is influenced by numerous factors. Although traditional methods such as Failure Mode, Effects, and Criticality Analysis (FMECA) and Fault Tree Analysis (FTA) can reflect the degree of fault risk to a certain extent, they cannot accurately quantify and evaluate the fault risk under the multiple influences of human factors, random faults, and external environment. In order to solve these problems, this article proposes a fault risk assessment method for aircraft control systems based on a fault risk composite assessment framework using the Improved Risk Priority Number (IRPN) as the basis for the fault risk assessment. Firstly, a Bayesian network (BN) and Gated Recurrent Unit (GRU) are introduced into the traditional evaluation framework, and a hybrid prediction model combining static and dynamic failure probability is constructed. Subsequently, this paper uses the functional resonance analysis method (FRAM) by introducing a risk damping coefficient to analyze the propagation and evolution of fault risks and accurately evaluate the coupling effects between different functional modules in the system. Finally, taking the fault of a jammed flap/slat drive mechanism as an example, the risk of the fault is evaluated by calculating the IRPN. The calculation results show that the comprehensive failure probability of the aircraft control system in this case is 3.503 × 10⁻⁴. Taking into account the severity, the detection, and the risk damping coefficient, the calculation result of IRPN is 158.00. According to the classification standard of the risk level, the failure risk level of the aircraft belongs to a controlled risk, and emergency measures need to be taken, which is consistent with the actual disposal decision in this case. Therefore, the evaluation framework proposed in this article not only supports a quantitative assessment of system safety and provides a new method for fault risk assessments in aviation safety management but also provides a theoretical basis and practical guidance for optimizing fault response strategies.

The novel coronavirus outbreak has significantly heightened environmental costs and operational challenges for civil aviation airlines, prompting emergency airport closures in affected regions and a substantial decline in ridership. The consequential need to reassess, delay, or cancel flight itineraries has led to disruptions at airports, amplifying the risk of disease transmission. In response, this paper proposes a spatial approach to efficiently address pandemic spread in the civil aviation network. The methodology prioritizes the use of a static gravity model for calculating route-specific infection pressures, enabling strategic flight rescheduling to control infection levels at airports (nodes) and among airlines (edges). Temporally, this study considers intervals between takeoffs and landings to minimize crowd gatherings, mitigating the novel coronavirus transmission rate. By constructing a discrete space–time network for irregular flights, this research generates a viable set of routes for aircraft operating in special circumstances, minimizing both route-specific infection pressures and operational costs for airlines. Remarkably, the introduced method demonstrates substantial savings, reaching almost 53.4%, compared to traditional plans. This showcases its efficacy in optimizing responses to pandemic-induced disruptions within the civil aviation network, offering a comprehensive solution that balances operational efficiency and public health considerations in the face of unprecedented challenges.

To explore the coupling among the safety, economic, and environmental objectives in flight operations control, the trade-off patterns among these objectives were analyzed. The core scope was defined, key factors and coupling feedback relationships were identified, and a system dynamics (SD) model with seven stocks was constructed, based on which a semi-quantitative trade-off analysis was conducted. A fully connected neural network was then introduced to fit the nonlinear mapping between six policy levers and four core objectives, and a multi-objective optimization model was built using the elitist Non-dominated Sorting Genetic Algorithm II (NSGA-II), generating the Pareto front. The constraint relationships were quantified via dynamic trend analysis, threshold attainability tests, and compromise and trade-off coefficients, and the stability of the conclusions was verified by robustness tests. The mean compromise coefficient was 0.957, and the mean profit–safety, carbon emissions–profit, and carbon emissions–safety trade-off coefficients were 12.335, 4.757, and −4.110, respectively, indicating trade-offs among the safety, economic, and environmental objectives. The study finds that under a given total resource budget, policy space, and the airline’s existing operating mode, the three objectives cannot simultaneously attain their individual optima within the feasible solution set. A strong trade-off exists between safety and economy, while the impacts of environmental performance on the other two objectives are constrained by policy settings and technological pathways, offering theoretical references for airlines’ operational decision-making.