统计标准对航班正常性计算的影响——以首都机场为例

近年来,民航运输发展迅猛,但同时航班延误状况也日益严峻,受到广泛关注。航班延误不仅影响航空公司运营效率,而且引发旅客投诉,降低旅客满意度,因此航班延误也成为研究热点之一。在以往文献中,鲜有探究不同标准与航班正常性计算结果的关系。公正、准确地衡量航班正常与否,关系着后续研究的正确性。首先,本文总结历年来我国航班正常性统计办法,进行简要评述。其次,以2014年3至5月北京首都国际机场5万余离港航班运行数据为例进行统计,比较不同统计标准下航班正常性的差异。最后,结合现状给出航班正常性统计的建议。     

基于可控性分类的航班延误问题应对策略建议

本文从航班延误的主要原因入手,对航班延误问题进行可控性分类,提出了应对延误问题的两种思路及其适用阶段,针对不同可控类型的航班延误问题,从风险管理的角度分别提出不同的应对策略与措施建议。     

航班延误考问民航品质

航班延误，与城市拥堵一样，一方面是运行主体急剧增加与交通资源相对有限的矛盾，另一方面则是社会需求旺盛与管理运营滞后的矛盾，但更为重要的，还在于航空公司自身的问题。无论是民肮局的调研结果，还是公众的普遍感受，一些航空公司运营水平不高、服务质量不够，人为放大了航空延误频率，也恶化了乘客对航班延误的心理感受。     

美国提升航空系统容量以减少航班延误

航班延误是美国国家运输系统中最令人头痛的问题之一。2000年，美国1／4的航班延误，延误航班数达到1400万个，这在美国历史上是空前的。2001年“9．11”恐怖袭击事件及2003年亚太地区的SARS，大大降低了对航空旅行的需求，航班延误现象相应有所减少。之后，航空旅行的需求开始恢复，航班延误又呈现出了上升趋势。     

我国航空公司航班延误补偿问题与对策

本文通过分析国内航空公司航班延误现状,以及对现有航班延误补救方式及标准的介绍,揭示了目前国内航班延误补偿机制所存在的问题,并从航班延误后责任主体的确定、经济补偿措施的改进、建立航班延误保险三个方面提出了改进航空公司航班延误的补偿对策。     

考虑风险偏好的航班延误旅客行为选择模型

目前,航班延误在我国还很普遍。研究航班延误后旅客行为选择倾向,对提升航班延误后服务补救具有一定的实际意义。本文利用前景理论,构建了航班延误后旅客行为选择模型,在基于航班延误规模、旅客属性确定的动态参照点的基础上,进行风险偏好系数的修正,并以北京-上海航线大规模延误为研究背景,分别计算风险偏好系数相同及不同两种情况下旅客选择行为的前景值,确定不同延误情形下旅客的最优行为选择。通过调查问卷及实地调查两种方式得到航班延误后旅客的实际选择行为,并与理论计算结果进行对比分析。结果表明,前景理论能够较好的解释旅客的选择行为。同时,在充分考虑旅客风险偏好的基础上,理论模型与实际行为的契合度更高。     

航班定制化的旅客登机策略选择

登机是飞机周转的关键环节。有效缩短登机时间,对减少航班延误,提高服务质量有现实意义。本文根据实地机场调研,确定影响登机进程的主要因素;利用元胞自动机进行登机仿真建模,探究不同因素对登机时间的影响;基于仿真结果 ,考虑不同航班的旅客组成及行李数量分布差别显著的特征,进行旅客登机策略选择系统设计,为每个航班提供个性化服务,选择最优登机策略,为机场及航空公司降低运营成本、提高保障效率提供方法参考。     

美国治理航班延误的方式及启示

本文介绍了美国航班延误的现状,分析了美国治理航班延误的措施——容量提升和需求管理,并得出对我国治理航班延误的启示。     

解决我国航班延误问题需要协同治理

航班延误是民航运输服务中的现象,它给民航运输各相关主体带来了诸多的负面影响,甚至升级为公共事件。因此,航班延误的治理问题,已经成为民航实务界和学术界长期以来关注的行业热点之一。本文从公共管理的视角,利用协同治理理论,构建航班延误协同治理的概念模型,分析航班延误问题解决的难点,提出解决我国航班延误的路径选择。分析结果表明:有效解决我国航班延误问题,深化认知和明确治理目标是前提,明晰治理角色和职责是基础,构建科学的长效治理机制是关键,建立和完善治理制度是保障,有效的激励性治理是重要动力,学习借鉴是必然要求,实现协同治理是路径选择。     

旅行中不能承受之屈

航班延误已是个老话题了。据统计,近年来我国的航班延误率大约在25%左右,也就是说每4人次航空旅客中大约有1人次曾被航班延误困扰过。航班延误发生得如此频繁,航空公司也在叫委屈,但其实最委屈的还是乘客,因为乘客是“出门一日难”的弱者。笔者最近就亲身经历了一次航班延误之苦。8月26日,我们一行10人在参加了青藏铁路铺轨通过唐古拉山的仪式后,乘坐国航CA4111航班从拉萨返回北京。航班的正点起飞时间是下午1点整,可是在乘客登上这架空客A340客机后,飞机却迟迟没有起飞。大家感觉机舱内非常闷热,纷纷质问乘务员为什么不起飞,这时机长才通…     

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.     

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.     

Future electricity demand from battery‐powered vehicles

An important decision faced by airline schedulers is how to adapt the flight schedule and aircraft assignment to unforeseen perturbations in an established schedule. In the face of unforeseen aircraft delays, schedulers have to decide which flights to delay, and when delays become excessive, which to cancel. Current scheduling models deal with simple decision problems of delay or cancellation, but not with both simultaneously. But in practice the optimal decision may involve results from the integration of both flight cancellations and delays. In Part I of this paper, a quadratic programming model for the integration decision problem is given. The model can formulate the integration of flight cancellations and delays as well as some special cases, such as the ferrying of surplus aircraft and the possibility of swapping different types of aircraft. In this paper, based on the special structure of the model, an effective algorithm is presented, sufficient computational experiments are conducted and some results are reported. These show that we can expect to obtain a sufficiently good solution in terms of reasonable CPU time.     

Flight and passenger delay assignment optimization strategies

This paper compares different optimization strategies for the minimization of flight and passenger delays at two levels: pre-tactical, with on-ground delay at origin, and tactical, with airborne delay close to the destination airport. The optimization model is based on the ground holding problem and uses various cost functions. The scenario considered takes place in a busy European airport and includes realistic values of traffic. A passenger assignment with connections at the hub is modeled. Statistical models are used for passenger and connecting passenger allocation, minimum time required for turnaround and tactical noise; whereas uncertainty is also introduced in the model for tactical noise. Performance of the various optimization processes is presented and compared to ration by schedule results.     

A Heuristic Algorithm for the Allocation of Airport Runway System Capacity

Abstract

This paper develops a heuristic algorithm for the allocation of airport runway capacity to minimise the cost of arrival and departure aircraft/flight delays. The algorithm is developed as a potential alternative to optimisation models based on linear and integer programming. The algorithm is based on heuristic (‘greedy’) criteria that closely reflect the ‘rules of thumb’ used by air traffic controllers. Using inputs such as arrival and departure demand, airport runway system capacity envelopes and cost of aircraft/flight delays, the main output minimises the cost of arrival and departure delays as well as the corresponding interdependent airport runway system arrival and departure capacity allocation. The algorithm is applied to traffic scenarios at three busy US airports. The results are used to validate the performance of the proposed heuristic algorithm against results from selected benchmarking optimisation models.     

Ground delay program planning under uncertainty in airport capacity

Abstract

This paper presents an algorithm for assigning flight departure delays under probabilistic airport capacity. The algorithm dynamically adapts to weather forecasts by revising, if necessary, departure delays. The proposed algorithm leverages state-of-the-art optimization techniques that have appeared in recent literature. As a case study, the algorithm is applied to assigning departure delays to flights scheduled to arrive at San Francisco International Airport in the presence of uncertainty in the fog clearance time. The cumulative distribution function of fog clearance time was estimated from historical data. Using daily weather forecasts to update the probabilities of fog clearance times resulted in improvement of the algorithm's performance. Experimental results also indicate that if the proposed algorithm is applied to assign ground delays to flights inbound at San Francisco International airport, overall delays could be reduced up to 25% compared to current level.     

Heuristic approach to the airline schedule disturbances problem

Abstract

When disturbances make it impossible to realise the planned flight schedule, the dispatcher at the airline operational centre defines a new flight schedule based on airline policy, in order to reduce the negative effects of these perturbations. Depending on airline policy, when designing the new flight schedule, the dispatcher delays or cancels some flights and reassigns some flights to available aircraft. In this paper, a decision support system (DSS) for solving the airline schedule disturbances problem is developed aiming to assist decision makers in handling disturbances in real-time. The system is based on a heuristic algorithm, which generates a list of different feasible schedules ordered according to the value of an objective function. The dispatcher can thus select and implement one of them. In this paper, the possibilities of DSS are illustrated by real numerical examples that concern JAT Airways' flight schedule disturbances.     

Flight delays,capacity investment and social welfare under air transport supply-demand equilibrium

This paper analyzes benefits from aviation infrastructure investment under competitive supply-demand equilibrium. The analysis recognizes that, in the air transportation system where economies of density is an inherent characteristic, capacity change would trigger a complicated set of adjustment of and interplay among passenger demand, air fare, flight frequency, aircraft size, and flight delays, leading to an equilibrium shift. An analytical model that incorporates these elements is developed. The results from comparative static analysis show that capacity constraint suppresses demand, reduces flight frequency, and increases passenger generalized cost. Our numerical analysis further reveals that, by switching to larger aircraft size, airlines manage to offset part of the delay effect on unit operating cost, and charge passengers lower fare. With higher capacity, airlines tend to raise both fare and frequency while decreasing aircraft size. More demand emerges in the market, with reduced generalized cost for each traveler. The marginal benefit brought by capacity expansion diminishes as the capacity-demand imbalance becomes less severe. Existing passengers in the market receive most of the benefit, followed by airlines. The welfare gains from induced demand are much smaller. The equilibrium approach yields more plausible investment benefit estimates than does the conventional method. In particular, when forecasting future demand the equilibrium approach is capable of preventing the occurrence of excessive high delays.     

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.     

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.