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Airport slot allocation
(2023)
This dissertation addresses the concept of airport slot allocation as a major regulatory directive in air transport management. In three sequential parts, today’s slot allocation procedure, addressing the assignment of time windows for departure and landing operations at coordinated airports, is being assessed and critically evaluated. The three sections evolve from the evaluation and revision of a suitable criteria set to the development and implementation of a network solution. As a key feature, a carbon emissions price including the air carriers’ CO2 emissions is being provided, serving as the allocation principle. In all three parts, explicit reference to the IATA Worldwide Slot Guidelines, representing today’s regulatory framework, is being provided, highlighting explicit deficits and drawbacks of that solution. In the first part, the Analytic Hierarchy Process (AHP), as a concept of decision making based on a comparison of criteria and alternatives, is being applied guiding the allocation decision at a single airport. In this section, a set of multiple criteria is being proposed and weighted according a set of slot coordinators’ preferences. As a key feature, the concept of Analytic Hierarchy Process (AHP) is being extended by the conduction of a Pairwise Comparison-based Preference Measurement (PCPM) representing one stage of the Analytic Hierarchy Process (AHP). In part two, the perspective of the dissertation changes from a single-point to a multiple-point allocation environment. In this part, a model is being provided that includes the allocation of slots in an airport network. As a key feature, slots are being allocated such that the two complementary cost functions are being minimized. On one hand, the developed carbon cost function includes the minimization of the carbon footprint per traveling passenger. On the other hand, the developed handling cost function is being minimized incorporating a dedicated airport perspective to the solution. In part three, the proposed model is being further extended by the incorporation of a third directive, the minimization of connection cost, related to the application of solution in a hub-and-spoke network. As a result, the study demonstrates how slot allocation can be conducted efficiently in an airport network, and how the consideration of the carbon footprint per traveling passenger serves to calculate the allocation optimum.
Door-to-door (D2D) air travel is gaining momentum for airlines, airports, and feeder traffic providers. The mobility industry and researchers are broadening their scope to include the entire travel chain, from origin to final destination. Intermodal mobility products are already on the market. At the same time, widespread trends affect transport service providers (as the suppliers) and passengers (regarding demand). Acquiring a better understanding of future D2D air travel trends is crucial for the mobility sector for long-term planning, product adaptation, the services provided and the pricing of these, and improvements in the passenger experience. Focusing on the European market, the overall objective of this doctoral thesis is to identify and understand the future trends of D2D air travel. It is divided into three parts; these provide different perspectives on trends and employ a range of methods that lead to results that develop from each other.
In Part One, the Delphi technique is utilized to identify future travel trends. The study considers projections of European air passengers and their requirements for their entire air travel chain, including airport access, a long-haul flight, and airport egress. The research focuses on 2035 and is based on a two-round Delphi survey involving 38 experts from the transport industry, academia, and consultants. The Delphi survey is supplemented with findings from a preliminary study, consisting of a literature review, interviews with 18 experts in the field of air travel, and a workshop attended by experts. Results reveal that digitalization and personalization will be the main drivers in 2035 and that passengers might demand value-added use of their travel time. In addition, environmentally friendly travel products are considered desirable but only somewhat probable by 2035. Passenger type, age, origins, and travel budget will still be influential factors in 2035. Based on the results from a hierarchical cluster analysis, Part One presents three possible future scenarios: (1) personalized D2D travel, (2) integrated D2D travel, and (3) the game-changer. A technical chapter elaborates on the Delphi technique and individual research steps.
Part Two explores the supply aspect and to what extent transport service providers consider strategically relevant trends. The scope of D2D air travel is adapted by applying multi-labeled text classification models to 52 corporate reports from a sample of transport service providers that operate in the European market. Trends identified in the first Delphi study and from an additional literature review are used to develop seven classes. Two prototype models are developed: a dictionary-based classifier and a supervised learning model using the multinomial naive Bayes and linear support vector machine classifiers. The latter yields the best model output, revealing which trends have a higher, medium, or lower relevance on the supply side. The results show that providers consider environmentally friendly air transport and related products to be highly relevant while disruption management, leveraging passengers’ data, and improving airport feeder traffic through innovative mobility initiatives are considered to be of medium relevance.
Part Three explores air passengers’ preferences and willingness to pay for ancillary services in the current transition into the new normal, brought about by the ongoing COVID-19 pandemic, high uncertainty, and changing market dynamics. A choice-based conjoint analysis is used to test six attributes within a hypothetical travel scenario for a long-haul one-way air trip. Choice data from 269 German business and leisure passengers are analyzed using a hierarchical Bayes estimator. Results reveal that the total ancillary service upgrade price influences passengers’ choices the most, followed by a seat upgrade for greater comfort and the CO2-compensation of a flight. Hygiene-related ancillaries bring low utilities. Female and senior passengers care more for environmentally friendly ancillaries. Confirming previous research, business passengers and frequent flyers care more for onboard comfort.
This dissertation assesses the characteristics and viability of the emerging longhaul Low Cost Carriers (LCCs). In particular, the aim is to understand their business model, evaluate the cost and revenue performance, and investigate its impact on other carriers. Existing academic literature is inconclusive about characteristics and viability of the business model. To validate its defining characteristics, 37 airlines flying on North Atlantic routes are clustered using Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) along a newly constructed long-haul airline business model framework. To contribute to the evaluation of business model viability, cost differences between clusters are uncovered followed by a discussion of their sustainability. Key findings include the characterization of the emerging long-haul LCC business model and its significant differences from Full-Service Network Carrier (FSNC) and leisure carrier models. On a cluster average, 33% lower unit costs compared to FSNCs are identified, of which 24 percentage points are evaluated as sustainable. As these cost advantages over FSNCs are smaller compared to the original savings of short-/medium-haul LCCs, revenue competitiveness on the longhaul becomes more critical. To assess long-haul revenue performance, a new metric for benchmarking the revenue per equivalent flight capacity is defined. Subsequently, a revenue model combining traffic, fare, load factor, and seat data from the North Atlantic is developed to determine the revenue per flight capacity across a sample of city-pairs. The results show that LCCs earn revenue per flight capacity comparable to FSNCs on shorter long-haul routes. Key factors to compensate lower direct yields are fewer low-yield connecting passengers, sales of ancillary services, higher load factors, and significantly more passengers per aircraft. Long-haul LCC market impact, and in particular their impact on incumbents’ fare levels, has not yet been assessed and short-/medium-haul LCC-related findings cannot be readily applied. To evaluate the impact of long-haul LCC presence on the incumbents’ pricing, a stylized analytical model is proposed for hypotheses development. Subsequently, Two-Steps Least Squares (2SLS) regressions with Instrumental Variables (IVs) are performed, distinguishing between economy, premium economy, and business classes, based on a sample of North Atlantic routes. Confirming the first hypothesis, LCC presence reduces incumbents’ long-haul economy and premium economy fares by -10% and -13%, respectively, ceteris paribus. LCC presence, however, does not significantly impact long-haul business class fare levels, confirming the second hypothesis. These findings are particularly relevant as the North Atlantic market represents to date one of the remaining profit pools for North American and European legacy carriers. The findings of this research implicate that the long-haul LCC model is economically viable, at least on trunk routes with high demand. FSNC management should be aware of the rising competition and fare impact on North Atlantic routes. Potential reactions could include the de-bundling of entry fares with the option of ancillary sales even on long-haul routes, a re-evaluation of the revenue impact of low-yield connecting passengers, a focus on premium passengers, and a continuous reduction of operating costs.
This dissertation investigates the application of machine learning to improve decision making in airline operations. In an introductory overview, we1 discuss the airline industry and the challenges of decision making in airline operations: e. g., complex IT infrastructure, interconnected resources, delay management, fuel price volatility and future environmental regulation. Machine learning can efficiently integrate a large volume of data from a variety of data sources and formats to generate accurate predictions. To assess the viability of using machine learning models for decision making in airline operations, we develop a model based on linear regression and gradient boosting to predict aircraft arrival time. Furthermore, we integrate cost index optimization to model the impact of aircraft speed on arrival time. While we find that machine learning can improve prediction accuracy by more than 30 %, the optimal cost index varies according to fuel cost, flight distance and delay costs. We propose an overall reduction in cost index for short-haul flights to reduce fuel cost while maintaining punctual operations. Arrival time predictions serve as input for daily operations planning and control. For network carriers accurate arrival time predictions are key for efficient hub operations. In a next step, we focus on aircraft arrival time prediction for intercontinental flights. We analyze the accuracy of en-route weather data provided by the flight plan, generate features based on en-route weather data and integrate them in our prediction model. We evaluate three machine learning models: linear regression, random forest and gradient boosting. Through our approach, we can assess the impact of en-route weather data. Overall, an increase in prediction accuracy of 25 % is achieved. By including en-route weather data, prediction accuracy is improved by 5 %. Our model outlines the essential features for intercontinental arrival time predictions and assess the value of en-route weather data. Furthermore, we outline organizational challenges in implementing predictive analytics. Future environmental regulations are a challenge for the airline industry. From 2020 onwards net growth in CO2 emissions is prohibited. Thus, airlines need to focus on initiatives to limit fuel consumption. We develop a prediction model for fuel consumption considering ten different aircraft types. Our results show that fuel consumption can be improved by more than 30 % for short and long-haul flights resulting in an annual reduction of 10.5 million e in fuel costs and a reduction on green house gas emissions of 66.000 tons. Furthermore, we assess the pilots’ willingness to integrate our prediction model in their fuel decision. Our analysis shows that high prediction accuracy, model understanding and a long testing phase are essential for acceptance of the prediction model. The main implication is that decision making in airline operations can be substantially improved through machine learning. Therefore, more prediction.
1The term “we” refers to the authors of the respective chapters as denoted at the beginning of each chapter. For the abstract, this refers to the authors of Achenbach and Spinler (2018a,b), Achenbach et al. (2017).
CO2 hedging for airlines
(2012)