User-Driven Prioritisation Process (UDPP) from advanced experimental to pre-operational validation environment

When the European Air Traffic Management Network (EATMN) approaches its capacity limits at certain airports, generating increasingly costly delays to flights and for passengers, reducing the impacts on airlines and passengers will be essential. There are two ways to address such increases in delay and costs. One is to strive to augment the capacity to reduce both delays and costs. The other one, adopted in this paper, is to reduce the impact of the delay on airlines and passengers.The User Driven Prioritisation Process (UDPP) provides airlines with additional flexibility in planning within constrained situations where delays are allocated, beyond the current slot swapping process already implemented by the EUROCONTROL Network Manager (NM).The paper presents recent validation results. It summarizes earlier results in a simplified EATMN with one constraint and several AUs showing that UDPP could reduce the impact of that delays on AUs' direct operational costs by 58% on average, and improves passengers’ connections. The paper then describes a parallel validation activity including several trials with SWISS. It focuses on the most recent validation results in a pre-operational environment close to SWISS Operations Control Centre (OCC). The results revealed overall operational benefit up to 65% improvement for the airline; and an initial impact assessment on the EATMN -although limited to one UDPP in the EATMN and one airline using it- shows promising results for the on-going integration closer to operations.While limited due to the simplified network context, these results are encouraging to undertake more complete network impact studies as well as to move forward to environments that are more realistic, bridging towards deployment.     

Optimal route decision with a geometric ground-airborne hybrid model under weather uncertainty

Adverse weather is the dominant cause of delays in the National Airspace System (NAS). Since the future weather condition is only predictable with a certain degree of accuracy, managing traffic in the weather-affected airspace is a challenging task. In this paper, we propose a geometric model to generate an optimal combination of ground delay and route choice to hedge against weather risk. The geometric recourse model (GRM) is a strategic Probabilistic Air Traffic Management (PATM) model that generates optimal route choice, incorporating route hedging and en-route recourse to respond to weather change: hedged routes are routes other than the nominal or the detour one, and recourse occurs when the weather restricted airspace becomes flyable and aircraft are re-routed to fly direct to the destination. Among several variations of the GRM, we focus on the hybrid Dual Recourse Model (DRM), which allows ground delay as well as route hedging and recourses, when the weather clearance time follows a uniform distribution. The formulation of the hybrid DRM involves two decision variables - ground delay and route choice - and four parameters: storm location, storm size, maximum storm duration time, and ground-airborne cost ratio. The objective function has two components: expected total ground delay cost and expected total airborne cost. We propose a solution algorithm that guarantees to find the global optimum of the hybrid-DRM. Based on the numerical analysis, we find that ground-holding is effective only when combined with the nominal route. Otherwise, it is optimal to fly on the route determined by the DRM without ground delay. We also find the formula of the threshold ground-airborne cost ratio, which we call the Critical Cost Ratio (CCR), that determines the efficacy of ground delay: the higher the CCR, the more effective the strategies involving ground delay. We conclude that both ground delay and route hedging should be considered together to produce the best ATM decisions.     

Vulnerability assessment and re-routing of freight trains under disruptions: A coal supply chain network application

In this paper, we present a two-stage mixed integer programming (MIP) interdiction model in which an interdictor chooses a limited amount of elements to attack first on a given network, and then an operator dispatches trains through the residual network. Our MIP model explicitly incorporates discrete unit flows of trains on the rail network with time-variant capacities. A real coal rail transportation network is used in order to generate scenarios to provide tactical and operational level vulnerability assessment analysis including rerouting decisions, travel and delay costs analysis, and the frequency of interdictions of facilities for the dynamic rail system.     

Efficient aircraft spare parts inventory management under demand uncertainty

In airline industries, the aircraft maintenance cost takes up about 13% of the total operating cost. It can be reduced by a good planning. Spare parts inventories exist to serve the maintenance planning. Compared with commonly used reorder point system (ROP) and forecasting methods which only consider historical data, this paper presents two non-linear programming models which predict impending demands based on installed parts failure distribution. The optimal order time and order quantity can be found by minimizing total cost. The first basic mathematical model assumes shortage period starts from mean time to failure (MTTF). An iteration method and GAMS are used to solve this model. The second improved mathematical model takes into account accurate shortage time. Due to its complexity, only GAMS is applied in solution methodology. Both models can be proved effective in cost reduction through revised numerical examples and their results. Comparisons of the two models are also discussed.     

Aviation infrastructure performance and airline cost: a statistical cost estimation approach

The relationship between the performance of the US National Airspace System (NAS) and airline costs is examined by estimating airline cost functions that include NAS performance metrics as arguments, using quarterly data for 10 US domestic airlines. Performance metrics that vary by airline and quarter are developed by applying principal component analysis to seven underlying variables, including average delay, delay variance, and the proportion of flights that is cancelled. This analysis reveals that variation in the seven variables can be adequately captured by three or fewer factors, which we term NAS performance factors. If three factors are used, they can be interpretted as “delay”, “variability”, and “disruption”, the latter two of which are merged into a single “irregularity” factor in the two-factor model. Cost function estimation results confirm the anticipated link between NAS performance and airline cost. In the cost models with two and three performance factors, the irregularity and disruption factors are found to have the strongest cost impacts. These results challenge the prevailing assumption that delay reduction is the most important benefit from NAS enhancements. Using the estimated cost models, we predict airline cost savings from substantially improved NAS performance in the range $1–4 billion annually.     

A method for quantifying travel productivity for corporate travel managers

For large corporations with significant travel budgets, the efficiency in execution of employee travel is critical to the productivity of the enterprise. Air travel disruptions (i.e. delays, cancellations, missed connections) prevent employees from performing enterprise related tasks resulting in lost billable revenue and unbudgeted Indirect costs (e.g. unplanned overnight stays for stranded passengers, and idle time charges). Since travel disruption data is not readily available to Corporate Travel Managers, the Indirect charges cannot be included in budgets, and the magnitude of lost billable revenue is not known. Further, without measuring the travel delays and their impact, it is not possible to understand the underlying causes of the delays to improve the process.This paper describes a method for providing Corporate Travel Departments travel disruption statistics and their impact on revenue and profits. The method overcomes the problem of the absence of data by deriving travel delay statistics for corporate travel from publicly available historic airline flight data-bases. The method also uses a travel delay cost model to estimate the financial impact of travel disruptions. The implications of these results on Corporate Travel Management (CTM) productivity improvement strategies, corporate travel and indirect budgets, contracts with travel providers, and travel insurance are discussed.     

Do low cost carriers have different corporate governance models?

In this paper, we investigate whether different business models in the same industry (passenger air transportation) lead to different corporate governance models. We found that low-cost carriers organise their boards differently from full service carriers to achieve lower costs and a faster decision-making process that is required by their business model. We also found that low-cost carriers and full service carriers solve their potential agency cost problems differently and that full service carriers have more board monitoring committees, and low-cost carriers have a closer coincidence of interests between shareholders and executive directors.     

The hidden cost of uncertainty for airspace users

This article highlights the importance of uncertainty in day-to-day operations, and the need to take it into account to properly assess the cost of delay for airspace users. It defines a cost of uncertainty and estimates it using real data. It provides some easily computable models based on the average and standard deviation of delay to estimate the cost of delay in general. The article shows that uncertainty is also important in the formulation of buffers for airlines and provides a simple model to estimate the optimal assignment, further using real data to compute the optimal value at different airports.     

Impact of operational performance on air carrier cost structure: Evidence from US airlines

The impact of operational performance on airline cost structure is empirically investigated using an aggregate, statistical cost estimation approach. Two distinct sets of operational performance metrics are developed and incorporated into the airline cost models as arguments. Results from estimating a variety of airline cost models reveal that both delay and schedule buffer are important cost drivers. We also find that flight activity outside schedule windows increases cost, whereas flight inactivity within schedule windows does not. Using the estimated cost models, we predict the cost savings to airlines of “perfect” operational performance, obtaining an estimate in the range of $7.1–13.5 billion for 2007.     

Criteria for widening of two-lane rural highways

The purpose of this study was to evaluate flow characteristics on two-lane rural highways and to develop criteria for highway widening. The study is conducted on two tracks: theoretical development of delay models and use of a simulation model to estimate the effect of certain parameters on delay and percent-time-spent-following. Models of delay are presented, as are the regions in which the traffic is stable or unstable. It was also possible from the simulation to obtain the percent-time-spent-following, which is a key parameter in determining level-of-service on two-lane highways.The accrued delay over the usable life of a two-lane highway pavement, assumed to be 20 years, was discounted to present monetary value. This was then compared to four typical construction costs for different terrain types. The threshold average daily traffic volumes were determined at the points where the present value of the accumulated delay was equal to the cost of constructing two more lanes. These threshold values can be used as criteria for widening a two-lane highway and converting it into a four-lane facility. Additional criteria, based on percent-time-spent-following, are also presented.     

Performance assessment of UK airports: Evidence from a Bayesian dynamic frontier model

This paper analyzes the cost efficiency of UK airports over the period 1998-2008, using a Bayesian dynamic frontier model. This model provides a more structural explanation for the variation in airports inefficiency than has been presented by previous models, and also allows for cost inefficiency effects. On average, the dynamic frontier results, estimated via the Markov Chain Monte-Carlo simulation, indicate that UK airports improved their efficiency over time. Factors found to be important determinants of cost efficiency include airport size, price regulation, price cap variations and airport competition. Policy implications of the results are derived.     

Flight delay impact on airfare and flight frequency: A comprehensive assessment

This paper presents a comprehensive empirical analysis of flight delay impact on airfare and flight frequency in the US air transportation system. We model airfare and flight frequency as functions of cost and demand characteristics, competition effects, and flight delays at origin, destination, and intermediate hub airports. Estimation results confirm that airlines tend to pass delay cost onto passengers through higher fare, whereas delay has an upward effect on flight frequency. We find that proportionate airport delay reduction across the system can result in annual fare reduction in the order of billion dollars.     

Dynamic cost indexing – Managing airline delay costs

This paper develops a decision-support tool for managing flight delay costs in the pre-departure and airborne phases of a flight. The dynamic cost indexing tool trades accelerated fuel burn against ‘cost of time’ and environmental impact. Many airlines have significant barriers to quantify the various components of ‘cost of time’. There are no industry standards for defining and interfacing the necessary tools. Dynamic estimation of passenger delay costs, delay recovery decision windows and air traffic control cooperation are key constraints. Short-term opportunities for saving fuel and/or reducing environmental impacts are identified.     

Managing severe airspace flow programs: The Airlines’ side of the problem

This paper presents a heuristic-based approach for minimizing airlines’ schedule disruptions and operation costs associated with severe airspace flow programs. It considers primary decisions made by flight dispatchers such as flight slot substitution and rerouting outside the boundaries of the flow-constrained area. A two-stage heuristic is developed. In the first, a linear approximation of the problem is used to screen inefficient routing and slot substitution alternatives. The second stage examines possible solution improvements through trading flight assignments for every pair of conflicting routes. A genetic algorithm is developed and used to benchmark the performance of the two-stage heuristic. In the algorithm, flight route and slot allocation schemes are modeled as chromosomes. The fitness of these chromosomes measures the magnitude of schedule disruption and overall operating cost. A set of experiments that compare the performance of the two heuristics considering airspace flow programs with different levels of severity is presented.     

A large neighborhood search heuristic to establish an optimal ad-hoc hubbing strategy in the wake of a large-scale airport outage

Abrupt airport outages can cause diversions and fuel-critical situations for flights, leading to costly passenger misconnections. We develop a large neighborhood search heuristic to optimize the rerouting of flights bound for a disrupted airport to a hub airport that is not disrupted, with the goal of accommodating passengers on existing flights departing the non-disrupted hub. The objective of the heuristic is to identify and reroute flights to the ad-hoc hub(s) – non-disrupted hub airport(s) – that minimize the sum of passenger travel time and wait time. We minimize the passenger cost as the sum of passenger travel time to the diversion airport and wait time for a connecting flight at the ad-hoc hub airport, subject to on-board fuel and diversion airport capacity constraints. We use the heuristic to determine how a coordinated traffic management strategy could have diverted flights immediately following a real-world airport outage.     

Reforming air traffic control: an assessment from the American perspective

This paper examines institutional and economic reform of the ways in which air traffic control (ATC) services are provided in the US. It also contrasts the European and US ATC systems in terms of size, scope, cost and organizational form. The paper suggests that many of the congestion and delay problems experienced in the US result from the inefficient provision and use of air traffic capacity in the airport area, and these conditions are likely to continue or worsen if economic principles are not used to organize and provide ATC services. The paper notes that, while Europe has advanced more rapidly in the organizational and economic reform of providing ATC services, other problems remain. Because most large European airports have slot controls to limit demand in the airport area, its ATC congestion is more pronounced in the en route environment.     

Airline delay management problem with airport capacity constraints and priority decisions

This paper deals with the Airline Delay Management Problem (ADMP), which can be described as the task of dealing with daily airline operational delays and deciding whether to delay subsequent flights at a hub airport or to have them departing on time. An innovative integer linear programming approach is presented to the capacitated case of the ADMP and airport limitations in terms of bay availability, taxiway capacity and runway separation are incorporated to represent capacity constraints. Fuel cost, passenger compensation, and passenger inconvenience costs are included in the objective function. The decision variables include the re-timing of flight departures and arrivals, the use of the airport capacity over time and the rebooking of passengers in case of missed connections. To guarantee the linearity of the optimization model and fast computational times, a receding horizon modeling framework is adopted. The approach is applied to a case study using real operational and passenger data from an international hub-and-spoke carrier. The case study shows the capability of the linear model to deal with a complete day of operations within a few minutes. The results suggest that the proposed approach can lead to cost reductions of almost 30% during recovery, when compared with the solution from the airline. In addition, a sensitivity analysis is provided to investigate the impact of not including passenger inconvenience costs and of reducing runway capacity.     

Innovation: The winning formula to regain profitability in aviation?

Most airlines have overcome the immediate effects of the recent global aviation crisis partly because of economic upturn and partly because of massive restructuring. Legacy network carriers had to take on the challenge of low-cost carriers, and regain competitiveness in short- and mid-haul business through considerable cost cutting and more flexible pricing models and are now profitable. On the other hand, many airlines do not make a reasonable profit, and the majority of carriers do not earn their capital cost. Airlines claim that they are still squeezed between their neighbors in the aviation value chain that leverage local monopolies (such as airports) or oligopolies (such as aircraft equipment manufacturers). Many legacy carriers, however, have not properly prepared for an era of deregulated and changing markets. They have not yet taken appropriate steps to escape from there positions between the few successful premium carriers and low-cost carriers. This middle position has little differentiation potential, an unsatisfactory growth perspective, and poor profitability prospects. Further, despite ongoing liberalization, the regulatory framework still does not enforce far-reaching consolidation, leaving the industry in a fragmented status with massive overcapacities. Consequently, the industry needs to further leverage external deregulation as well as internal restructuring to establish more efficient and competitive business models. Aside from basic cost cutting, innovation may become the decisive driver of progress, comprising advanced business models, customer segmentation, and technologies (Franke, M., 2006. Innovation: the winning formula to regain profitability in aviation? Speech at the Hamburg Aviation Conference, Hamburg).     

Low cost carriers in the Middle East and North Africa (MENA) region: Emergence and barriers to development

This paper investigates the development of low cost carriers (LCCs) in the Middle East and North Africa (MENA) region from a broader perspective. We use passenger volume data at the airline and flight segment levels to illustrate the development of LCCs in the region; we conduct a structured comparison of the actual business model characteristics of the MENA-based LCCs to assess their adherence to the archetypical LCC business model; and we compile the key barriers to LCC growth in MENA from the literature.We find that the overall market share of LCCs in MENA is still below the world average – despite high growth in recent years. The presence of LCCs varies considerably between MENA countries and route groups. The more upscale, “Jetblue”-style business model, where passengers benefit from additional, complementary services or product characteristics, prevails. Political tensions, adverse regulations and lower levels of liberalization compared to Europe or North America negatively affect LCC development in many MENA countries. While low middle-class proportions appear to be a challenge for low cost business models, Asian diaspora and tourism – from beach holidays to pilgrimage – seem to induce additional demand.     

Analyzing passenger behavior in airport terminals based on activity preferences

Airport terminals are facilities that provide a variety of activities related to both the preparation of the passengers for their air trip (aeronautical) and their free time inside the terminal (non-aeronautical). In the last years, the number of non-aeronautical activities has substantially increased and significantly diversified both before and after the security checkpoint. The established role of non-aeronautical activities forces planners and managers to better understand passenger behavior. The potential of discrete choice models for the exploration of passenger behavior is analyzed in this paper. For the demonstration of the methodology, Lisbon Humberto Delgado International airport is used as a case study. Data is collected through a revealed and stated preference survey inside the terminal at the area before the security checkpoint. Activity-choice models are developed to identify the factors that affect the choices of the passengers over the area where they conduct non-aeronautical activities. Forecasts show that when increasing the percentage of passengers who conduct the check-in online and have planned their activities before arriving at the airport, the passengers’ preferences to conduct non-aeronautical activities only after the security checkpoint increase. This paper shows the contribution of developing discrete choice models in the better comprehension of passenger decisions over the activities they perform in an airport terminal.