Vertiports, as dedicated facilities for electric vertical takeoff and landing (eVTOL) aircraft, are essential to ensure the efficiency and sustainability of Urban Air Mobility (UAM). However, UAM infrastructure site selection has become increasingly complex due to limited land availability, complex spatial conditions, and the need to balance multiple objectives. Focusing on passenger-carrying UAM operations, this study proposes a systematic framework for vertiport site selection. First, key factors are classified into high, medium, and low levels across the safety, economic, and social dimensions, forming a modular evaluation system. A GIS-based spatial screening process is developed to identify potential vertiport locations. Subsequently, a variable representing the level of demand satisfaction is incorporated into a progressive coverage model specifically designed for vertiport site optimization. A hybrid algorithm is designed to solve the model. Using Shenzhen as a case study, the proposed approach is validated through real-world data. The results show that vertiport size and spatial requirements significantly influence the selection of suitable land types. High economic constraints may cause facility over-concentration, while setting standards aligned with regional functions better supports equitable access. Locating vertiports in high-demand areas enhances demand satisfaction levels, and both service capacity and range strongly influence overall system performance. These findings provide practical insights for future vertiport planning, promoting the efficient use of urban resources and supporting the successful implementation and sustainability of UAM.

Fatigue detection for air traffic controllers is an important yet challenging problem in aviation safety research. Most of the existing methods for this problem are based on facial features. In this paper, we propose an ensemble learning model that combines both facial features and voice features and design a fatigue detection method through multifeature fusion, referred to as Facial and Voice Stacking (FV-Stacking). Specifically, for facial features, we first use OpenCV and Dlib libraries to extract mouth and eye areas and then employ a combination of M-Convolutional Neural Network (M-CNN) and E-Convolutional Neural Network (E-CNN) to determine the state of mouth and eye closure based on five features, i.e., blinking times, average blinking time, average blinking interval, Percentage of Eyelid Closure over the Pupil over Time (PERCLOS), and Frequency of Open Mouth (FOM). For voice features, we extract the Mel-Frequency Cepstral Coefficients (MFCC) features of speech. Such facial features and voice features are fused through a carefully designed stacking model for fatigue detection. Real-life experiments are conducted on 14 air traffic controllers in Southwest Air Traffic Management Bureau of Civil Aviation of China. The results show that the proposed FV-Stacking method achieves a detection accuracy of 97%, while the best accuracy achieved by a single model is 92% and the best accuracy achieved by the state-of-the-art detection methods is 88%.

In recent years, flight delay costs the air transportation industry millions of dollars and has become a systematic problem. Understanding the behavior of flight delay is thus critical. This paper focuses on how flight delay is affected by operation-, time-, and weather-related factors. Different econometric models are developed to analyze departure and arrival delay. The results show that compared to departure delay, arrival delay is more likely to be affected by previous delays and the buffer effect. Block buffer presents a reduction effect seven times greater than turnaround buffer in terms of flight delays. Departure flights suffer more delays from convective weather than arrival flights. Convective weather at the destination airport for flight delay has a greater impact than at the original airport. In addition, sensitivity analysis of flight delays from an aircraft utilization perspective is conducted. We find that the effect of delay propagation on flight delay differs by aircraft utilization. This impact on departure delay is greater than the impact on arrival delay. In general, specific to the order of flights, the previous delay increases the impact on flight on-time performance as a flight flies a later leg. Buffer time has opposite effects on departure and arrival delay, with the order increasing. A decrease in buffer time with the order increasing, however, still has a greater reduction effect on departure delay than arrival delay. Specific to the number of flights operated by an aircraft, the more flights an aircraft flies in a day, the more the on-time performance of those flights will suffer from the previous delay and buffer time generally.

Eye movement patterns have become an essential element in modern approaches for identifying air traffic controller fatigue. By observing eye movements among various individuals and environments, researchers have discovered correlations with multiple physiological metrics and cognitive processing abilities. This study involved human-in-the-loop simulations to collect eye movement and fatigue data from air traffic controllers and students. The eye movements were classified into three main types: fixation, saccade, and blink. Statistical analyses were performed to determine the most important indicators. Using support vector machine and random forest models for training and prediction, it was found that the fixation characteristic is significantly important for monitoring air traffic controller fatigue. The implementation of this model has the potential to identify forthcoming instances of controller fatigue during their shifts, thereby helping to avert the possibility of unsafe situations.