The fatigue of air traffic controllers (ATCOs) on duty seriously threatens air traffic safety and needs to be managed. ATCOs perform several different types of work, with each type of work having different characteristics. Nonetheless, the influence of work type on an ATCO’s fatigue has yet to be demonstrated. Here, we present a field study in which the fatigue of ATCOs working in two types of work was compared based on an optimized data-driven method that was employed to detect the percentage of eyelid closure over the pupil over time (PERCLOS). Sixty-seven ATCOs working within two typical jobs (i.e., from the terminal control unit (TCU) and area control unit (ACU)) were recruited, and their fatigue was detected immediately before and after shift work using PERCLOS. Using a Spearman correlation test analysis, the results showed that the influence of work type on an ATCO’s fatigue had interesting trends. Specifically, the ATCOs at the TCU who handle departures and arrivals, which include converging with and maneuvering around conflicts, retain normal circadian rhythms. Their fatigue was significantly influenced by the various demands from tasks focusing on sequencing and conflict resolution and by the time phase of a normal circadian rhythm. At the ACU, ATCOs manage flights that are mainly on route, causing monotonous monitoring and routine reporting tasks, and the ATCOs generally have frequent night shifts to handle overflights. Their fatigue was significantly influenced by the demand characteristics from tasks, but changes in fatigue rule were not consistent with a normal circadian rhythm, revealing that the ATCOs’ circadian rhythms may have already been slightly disturbed. Furthermore, the interactions between task demand and circadian rhythm with an ATCO’s fatigue were significantly observed in ATCOs working in the TCU but not in those in the ACU. This study provides first evidence that an ATCO’s work type influences his or her fatigue. This discovery may incite stakeholders to consider work type in the management of employee fatigue, not only in the civil aviation industry but also in other transport industries.

This paper investigates the risk quantification for Unmanned Aircraft (UA) in urban environments, focusing on the safety of ground people. An assessment methodology is proposed to quantify the flying risk, which indicates the ground fatalities resulted from different potential risk sources. With the knowledge of UA’s specifications and ground environments, the flying risk of the target UA flying in the target area could be evaluated from the combination of results from independent assessment procedures focusing on multiple potential risk sources with specific safety metrics. A study case to assess the flying risk of the Talon and the DJI Inspire 2 flying in one piece of the region in Chengdu is presented. From the assessment result, the airspace management strategies for both Talon and DJI Inspires 2 could be easily developed to guarantee the safety of ground people, therefore, this risk quantification method could be a general tool to support decision-making in safety work.

The innovative concept of digital tower provides a new solution for reducing the construction and operation costs of airports with adverse natural environments, poor intervisibility conditions, or sparse traffic. However, it leads to changes in the situational awareness of air traffic controllers and to challenges in safety performance. To research the safety performance of apron controllers at a large-scale airport applying a digital tower, a field study was conducted at Baiyun International Airport in Guangzhou, China. In this study, we established a comprehensive index system from the perspective of situational awareness, which provided measurements on the areas of interests, gaze and physiological features, and vigilance of controllers. Three modules were compared: a physical tower module, a digital tower module with a large panoramic screen, and a digital tower module with a small panoramic screen. The differences in the safety performances of apron controllers are discussed in two aspects: adaptability and reliability. The results indicated that the apron controllers at the three modules performed different cognition patterns, but similar cognition effort was paid toward maintaining performance. Furthermore, the significant vigilance decrement of controllers exists between after-duty and before-duty, but with no significant difference among the three modules. In conclusion, apron controllers at a large-scale airport could obtain effective safety performances based on a digital tower that were no less than those from a physical tower.

The close proximity of crosswinds to airport runways presents great hazards to landing operations. As a result, an aircraft is susceptible to encountering a loss of control. Elevated levels of turbulence are commonly linked with strong crosswind speeds over the runway glide path. Therefore, it is imperative to evaluate the factors that impact crosswind speeds. The susceptibility of the runways at Hong Kong International Airport (HKIA) to severe crosswinds is well established. This study aimed to build a scaled model of HKIA, along with its surrounding terrain/buildings, within a TJ-3 ABL wind tunnel to compute the crosswind speeds under different wind directions over the runway glide path. Subsequently, utilizing the outcomes of the experiment, a cutting-edge local cascade ensemble (LCE) model was employed in conjunction with a tree-structured Parzen estimator (TPE) to evaluate the crosswind speed over the north runway glide path. The comparative analysis of the TPE-LCE model was also conducted with other machine learning models. The TPE-LCE model demonstrated superior predictive capabilities in comparison to alternative models, as assessed by MAE (0.490), MSE (0.381), RMSE (0.617), and R² (0.855). The SHAP analysis, which utilized TPE-LCE predictions, revealed that two factors, specifically “Effect of Terrain/Buildings” and “Distance from Runway,” exhibiting noteworthy influence over the probability of encountering elevated crosswind speeds over the runway glide path. The optimal conditions for high-crosswind speeds were found to be characterized by the absence of nearby terrain features or structures, a smaller distance from HKIA’s north runway threshold, and with a wind direction ranging from 125 to 180 degrees.

In civil aviation, severe weather conditions such as strong wind shear, crosswinds, and thunderstorms near airport runways often compel pilots to abort landings to ensure flight safety. While aborted landings due to wind shear are not common, they occur under specific environmental and situational circumstances. This research aims to accurately predict aircraft aborted landings using three advanced deep learning techniques: the conventional deep neural network (DNN), the deep and cross network (DCN), and the wide and deep network (WDN). These models are supplemented by various data augmentation methods, including the Synthetic Minority Over-Sampling Technique (SMOTE), KMeans-SMOTE, and Borderline-SMOTE, to correct the imbalance in pilot report data. Bayesian optimization was utilized to fine-tune the models for optimal predictive accuracy. The effectiveness of these models was assessed through metrics including sensitivity, precision, F1-score, and the Matthew Correlation Coefficient. The Shapley Additive Explanations (SHAP) algorithm was then applied to the most effective models to interpret their results and identify key factors, revealing that the intensity of wind shear, specific runways like 07R, and the vertical distance of wind shear from the runway (within 700 feet above runway level) were significant factors. The results of this research provide valuable insights to civil aviation experts, potentially revolutionizing safety protocols for managing aborted landings under adverse weather conditions, thereby improving overall airport efficiency and safety.

Pilots commonly undergo training to effectively manage instances of wind shear (WS) during both the landing and takeoff stages. Nevertheless, in exceptional circumstances, there may be instances of severe wind shear (SWS) surpassing a magnitude of 30 knots, leading to adverse effects on the operation of taking off and landing aircraft. This phenomenon can lead to the execution of aborted landing maneuvers and deviations from the intended glide path. This study utilized the explainable boosting machine (EBM), an advanced machine learning (ML) model known for its transparency, to predict the severity of WS occurrences and analyze the underlying factors. The dataset consisted of 21,392 data points from 2018 to 2022 acquired from two Doppler light detection and ranging (LiDAR) systems installed at Hong Kong International Airport (HKIA). Initially, the Doppler LiDAR data received data treatment in order to address the issue of data imbalance. Subsequently, utilizing the processed data, the hyperparameters of EBM were optimized using the Bayesian optimization technique. The EBM model underwent subsequent training and evaluation, wherein its performance metrics were computed and compared with those of an alternative glass-box model including decision tree (DT) and counterpart black-box models, namely, random forest (RF) and extreme gradient boosting (XGBoost). The EBM model trained on synthetic minority oversampling technique (SMOTE)-treated data demonstrated superior performance in comparison with the alternative models, as indicated by its higher geometric mean (0.77), balanced accuracy (0.78), and Matthews’ correlation coefficient (0.169). Furthermore, the EBM exhibited enhanced predictive performance and facilitated a comprehensive analysis of individual and pairwise factor interactions in the prediction of WS severity. This enabled the assessment of the factors that contributed to the instances of SWS in the proximity of airport runways.