CCAR-135部运营人飞行员疲劳特征及影响因素研究

按照中国民用航空规章CCAR-135部执行的飞行任务，其不定时性和旋翼机的飞行环境极易诱发飞行员疲劳。为确定135部运营人飞行员疲劳特征，探讨其工作疲劳程度与影响因素间的作用路径，降低135部运营人飞行员疲劳风险，提高飞行安全阈值，运用方差分析、元分析、相关性分析以及路径分析的数据分析方法对160份问卷调查结果进行了分析。结果表明：135部运营人飞行员总体疲劳处于中度及以上水平，躯体疲劳、脑力疲劳以及工作动力减弱程度相对严重。睡眠质量和心理压力、焦虑、抑郁水平与疲劳程度呈显著正相关。疲劳水平、睡眠质量和心理负性情绪程度在人口学多个变量上存在显著差异。工作影响因素中，公司保障水平、排班与生物节律适应性、工作负荷接受程度显著影响疲劳水平。     

厦门高崎机场雾天气特征分析及安全飞行策略研究

依据2022年2月24日0时生效的第202202期导航数据库整理出厦门高崎机场运行标准，根据机场地形地势分析形成低能见度天气的原因，结合机场2010—2019年航空例行天气报告报文的详细内容，梳理机场低能见度天气月份分布及小时分布特点。最后通过对2018年3月4日一次雾天气发生过程的研究，梳理对机场运行造成的影响，提出厦门高崎安全飞行和运行控制的建议。研究可知：1）平流雾是厦门机场造成低能见度天气的主要因素之一，它的出现有明显的季节性；2）要确保低能见度天气下的安全飞行，需要多部门的协作，按照规章展开工作，并加大新技术的推广和应用；3）签派员应加强天气发展趋势的预报能力。     

Anomaly detection via a Gaussian Mixture Model for flight operation and safety monitoring

Safety is key to civil aviation. To further improve its already respectable safety records, the airline industry is transitioning towards a proactive approach which anticipates and mitigates risks before incidents occur. This approach requires continuous monitoring and analysis of flight operations; however, modern aircraft systems have become increasingly complex to a degree that traditional analytical methods have reached their limits – the current methods in use can only detect ‘hazardous’ behaviors on a pre-defined list; they will miss important risks that are unlisted or unknown. This paper presents a novel approach to apply data mining in flight data analysis allowing airline safety experts to identify latent risks from daily operations without specifying what to look for in advance. In this approach, we apply a Gaussian Mixture Model (GMM) based clustering to digital flight data in order to detect flights with unusual data patterns. These flights may indicate an increased level of risks under the assumption that normal flights share common patterns, while anomalies do not. Safety experts can then review these flights in detail to identify risks, if any. Compared with other data-driven methods to monitor flight operations, this approach, referred to as ClusterAD-DataSample, can (1) better establish the norm by automatically recognizing multiple typical patterns of flight operations, and (2) pinpoint which part of a detected flight is abnormal. Evaluation of ClusterAD-DataSample was performed on two sets of A320 flight data of real-world airline operations; results showed that ClusterAD-DataSample was able to detect abnormal flights with elevated risks, which make it a promising tool for airline operators to identify early signs of safety degradation even if the criteria are unknown a priori.     

航班快速恢复建模与仿真研究

随着民用航空的发展与竞争,航班延误不仅影响航空飞行的安全与正常,更与航空公司的运营效率、运营成本及乘客利益息息相关。针对某一恶劣天气影响,对某公司受影响航班进行重新调配,考虑到航班的备降、盘旋等待、延误、取消等多种状态,以总成本最小为目标函数,建立航班快速恢复模型,通过MATLAB运用遗传算法设计航班恢复算法进行求解,得出最经济的航班恢复方案。     

基于灰色二次指数平滑的QAR参数关联度预测

由于QAR参数波动的后果具有潜在性和滞后性,因此分析人员容易忽视其危险性而使其发展为不利事件。针对该问题,提出运用QAR参数关联度分析的方法,建立QAR参数波动与事件之间的对应关系;并构建基于二次指数平滑-灰色关联法的关联度预测模型,进一步明确QAR参数波动与事件之间关系的动态变化趋势。运用实际数据对该模型进行验证,表明结论符合实际,该模型科学合理,可以用于QAR参数分析过程。     

Fueling for contingencies: The hidden cost of unpredictability in the air transportation system

The aviation community is increasing its attention on the concept of predictability when conducting aviation service quality assessments. Reduced fuel consumption and the related cost is one of the various benefits that could be achieved through improved flight predictability. A lack of predictability may cause airline dispatchers to load more fuel onto aircraft before they depart; the flights would then in turn consume extra fuel just to carry excess fuel loaded. In this study, we employ a large dataset with flight-level fuel loading and consumption information from a major US airline. With these data, we estimate the relationship between the amount of loaded fuel and flight predictability performance using a statistical model. The impact of loaded fuel is translated into fuel consumption and, ultimately, fuel cost and environmental impact for US domestic operations. We find that a one-minute increase in the standard deviation of airborne time leads to a 0.88 min increase in loaded contingency fuel and 1.66 min in loaded contingency and alternate fuel. If there were no unpredictability in the aviation system, captured in our model by eliminating standard deviation in flight time, the reduction in the loaded fuel would between 6.12 and 11.28 min per flight. Given a range of fuel prices, this ultimately would translate into cost savings for US domestic airlines on the order of $120–$452 million per year.     

‘Each flight is different’: Carbon emissions of selected flights in three geographical markets

Air travel is considered the biggest individual climate sin. Avoiding flying, however, seems impossible. In this paper we argue that the flight a passenger chooses can be significant. For this purpose we compared the carbon emissions of selected flights in three geographical markets. We found tremendous differences in the environmental performance of individual flights. Furthermore, we also found that flying with the most modern aircraft or flying non-stop represents, in many cases, the least polluting option. Nevertheless, we were able to show that there are exceptions to this rule. Based on our results, we provide recommendations to the industry and for further research.     

Enhanced delay propagation tree model with Bayesian Network for modelling flight delay propagation

An enhanced Delay Propagation Tree model with Bayesian Network (DPT-BN) is developed to model multi-flight delay propagation and delay interdependencies. Using a set of real airline data, results show that flights have non-homogeneous delay propagation effects. The DPT-BN model is used to infer posterior delay profiles with different delay and scheduling scenarios. The major contribution of the DPT-BN model is to demonstrate how the modelling of non-independent and identically distributed delay profiles is more realistic for the observed delay propagation mechanism, and how robust airline scheduling methodologies can benefit from this probability-based delay model.     

Adaptive prediction of flight time uncertainty for ground-based 4D trajectory management

An adaptive prediction model of level flight time uncertainty is derived as a function of flight and meteorological conditions, and its effectiveness for ground-based 4D trajectory management is discussed. Flight time uncertainty inevitably increases because of fluctuations in meteorological conditions, even though the Mach number, flight altitude and direction are controlled constant. Actual flight data collected using the secondary surveillance radar Mode S and numerical weather forecasts are processed to obtain a large collection of flight time error and flight and meteorological conditions. Through the law of uncertainty propagation, an adaptive prediction model of flight time uncertainty is derived as a function of the Mach number, flight distance, wind, and temperature. The coefficients of the adaptive prediction model is determined through cluster analysis and linear regression analysis. It is clearly demonstrated that the proposed adaptive prediction model can estimate the flight time uncertainty without underestimation or overestimation, even under moderate or severe weather conditions. The proposed adaptive prediction is able to improve both safety and efficiency of 4D trajectory management simultaneously.     

Improving airline fuel efficiency via fuel burn prediction and uncertainty estimation

Reducing fuel consumption is a unifying goal across the aviation industry. One fuel-saving opportunity for airlines is the possibility of reducing discretionary fuel loading by dispatchers. In this study, we propose a novel discretionary fuel estimation approach that can assist dispatchers with better discretionary fuel loading decisions. Based on the analysis on our study airline, our approach is found to substantially reduce unnecessary discretionary fuel loading while maintaining the same safety level compared to the current fuel loading practice. The idea is that by providing dispatchers with more accurate information and better recommendations derived from flight records, unnecessary fuel loading and corresponding cost-to-carry could both be reduced. We apply ensemble learning techniques to improve fuel burn prediction and construct prediction intervals (PIs) to capture the uncertainty of model predictions. The upper bound of a PI can then be used for discretionary fuel loading. The potential benefit of this approach is estimated to be $61.5 million in fuel savings and 428 million kg of CO₂ reduction per year for our study airline. This study also builds a link between discretionary fuel estimation and aviation system predictability in which the proposed models can also be used to predict benefits from reduced fuel loading enabled by improved Air Traffic Management (ATM) targeting on improved system predictability.     

Effects of noise exposure of other people on aircraft noise tolerability using a reference point approach

Reactions to aircraft noise resulting from an airport development vary considerably among individuals. This paper examines whether the noise level tolerated by individuals is affected when they compare their own situation to the situations of other people who are more severely affected by the noise. We modeled the individual noise tolerability by adopting the reference point concept and the value function of prospect theory. A questionnaire assessed different levels of noise-affected zones at a hypothetical airport. Individual noise tolerable level was measured by a prerecorded aircraft noise presented via headphones. Decision-making associated with the reported satisfaction level regarding the changes in operational flight frequency settings at the airport. An interview survey was conducted with residents near Manila airport in the Philippines. We applied genetic algorithm to jointly estimate the parameters for noise tolerability model and the value function. Results showed that individuals tolerated more overhead flights when they considered the situations of other people worse affected by the noise than they were. The tendency of noise tolerability increased with the severity of noise exposure situations of other people, but lower reactions to noise escalation. We also observed diminishing sensitivity to changes in noise relative to the noise tolerable level. The aircraft noise tolerability and the consideration of noise exposure situations of other people appeared to be a vital aspect to be considered in airport noise management and policy.