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Accurate short-term demand forecasting is crucial to airline revenue management, yet most existing systems fail to meet this need because current models treat booking data as a single temporal dimension, either the accumulation of bookings for a specific flight or the historical booking profile of the same route. This unidimensional view discards information carried by the other temporal stream and forecasting absolute passenger counts introduces a further operational fragility when change in planned aircraft type alters total seat capacity. This study addresses both limitations. A dual-stream Long Short-Term Memory (LSTM) integrated with attention framework is proposed that simultaneously processes two complementary input sequences: a horizontal sequence capturing intra-flight booking accumulation over the days preceding departure, and a vertical sequence capturing inter-flight booking patterns at fixed days-before-departure offsets across historical flights. Multiple dual-stream architectural variants, combining self-attention, cross-attention, and hybrid attention with concatenation, residual, and gated fusion strategies, are developed and evaluated. Experiments on real-world re

Contrails account for a large portion of aviation's contribution to anthropogenic climate change. Navigational contrail avoidance is a promising solution to mitigate the warming caused by contrails. Prior trials testing navigational contrail avoidance have relied on bespoke integrations of contrail forecasts into airline operations. Here, we use a randomized control trial to test the feasibility of dispatcher-led contrail avoidance integrated into standard flight planning operations using a workflow that scales to an airline's entire network. We validated the efficacy of this intervention using satellite imagery and an automated flight-contrail attribution algorithm. Using this system, we observed an 11.6% reduction in contrail formation rate for the 1232 flights marked as eligible for contrail avoidance (intent-to-treat) relative to the flights in the control group (p = 0.011). In the 112 flights that flew contrail avoidance as planned (per-protocol flights), we observed a 62.0% lower contrail formation rate relative to the flights in the control group (p < 0.001). No statistically significant difference in fuel usage was observed between the two groups.

The European Union Emissions Trading System is set to substantially increase the effective carbon price faced by airlines. To quantify the impact of this carbon regulation on the European airline industry, we estimate a two-stage model of airline competition with endogenous route entry, flight frequencies, and pricing using European data on market shares and prices. Counterfactual simulations reveal that the impacts of carbon pricing are highly asymmetric across carrier types and market segments. Consumer surplus declines by up to 25% overall, with medium-haul markets bearing the brunt at up to 90%, while short-haul markets experience positive net welfare gains (including carbon revenue and the social value of avoided emissions) as airlines reallocate capacity toward shorter routes. We find that airline profits decline by 8-45% across scenarios, while carbon tax revenue of $0.9-3.1 billion and a social value of avoided CO2 emissions of $0.5-1.4 billion partially offset the welfare losses. We also show that a hypothetical Wizz Air-Ryanair merger primarily benefits firm profits through network expansion synergies.

This research highlights the potential of quantum annealing in tackling large-scale optimization problems within the airline industry,demonstrating its efficiency for certain problem sizes while also acknowledging its current limitations. The comparative analysis provides valuable insights into the performance of advanced computational techniques, paving the way for further advancements in optimizing fleet assignments in the aviation sector.

Codeshare agreements are contracts that allow two or more airlines to share seats on the same flight. These agreements, which are widespread in commercial aviation as a response to highly competitive environments, have enabled the expansion of airline networks without additional costs or risks for the companies involved. The literature presents ambiguous effects associated with the practice, with evidence of increased supply and reduced prices in situations of route complementarity, while also pointing to anti-competitive impacts in markets where companies act as competitors. A review of scientific production over time, including theoretical contributions and case studies, is essential to understand the evolution of these agreements and their implications, especially in the Brazilian context, marked by its own characteristics and particular regulatory history. Thus, this article reviews the literature on codesharing, with an emphasis on the Brazilian market, and uses the Litmaps computational tool, based on artificial intelligence techniques, to support the contextual analysis of publications through their citation relationships. The ultimate goal is to identify and evaluate the ma

I investigate how incumbents in the U.S. airline industry respond to threatened and actual route entry by Southwest Airlines. I use a two-way fixed effects and event study approach, and the latest available data from 1999-2022, to identify a firm's price and quantity response. I find evidence that incumbents cut fares preemptively (post-entry) by 6-8% (16-18%) although the significance, pattern, and timing of the preemptive cuts are quite different to Goolsbee and Syverson's (2008) earlier results. Incumbents increase capacity preemptively by 10-40%, up to six quarters before the entry threat is established, and by 27-46% post-entry. My results suggest a clear shift in firms' strategic response from price to quantity. I also investigate the impact of an incumbent's network structure on its preemptive and post-entry behaviour. While the results on price are unclear, a firm's post-entry capacity reaction depends strongly on its global network structure as well as the local importance (centrality) of the route.

The International Air Transport Association (IATA) states that the revenue from interline tickets must be shared among the different airlines according to a weighted system. We analyze this problem following an axiomatic approach, and our theoretical results support IATA's procedure. Our first result justifies the use of a weighted system, but it does not specify which weights should be applied. Assuming that the weights are fixed, we provide several results that further support the use of IATA's mechanism. Finally, we provide results for the case in which all flights can be considered equivalent and no weighting is required.

We introduce a set of open-source packages that form a highly extensible framework for quantum optimization. One design goal of the system is the inclusion of a command line based configuration system for setting up experiments. The possible options are derived using well-known Python packages and presented to the user intuitively, allowing the configuration of repeatable variational quantum optimization experiments. We give an example of using the system through the Airline Crew Pairing problem, a highly relevant industrial problem, and the MaxCut problem, for which instances of manageable size are readily available.

Airline operations are prone to delays and disruptions, since the schedules are generally tight and depend on a lot of resources. When disruptions occur, the flight schedule needs to be adjusted such that the operation can continue. Since this happens during the day of operations, this needs to be done as close to real time as possible, posing a challenge with respect to computation time. Moreover, to limit the impact of disruptions, we want a solution with minimal cost and passenger impact. Since airline operations include many interlinked decisions, an integrated approach leads to better overall solutions. We specifically look at resolving these disruptions in both the aircraft and crew schedules. Resolving these disruptions is complex, especially when it is done in an integrated way, i.e. including multiple different resources. To solve this problem in an integrated manner, we developed a fast simulated annealing approach. To the best of our knowledge, we are the first to develop a local search approach to resolve airline disruptions in an integrated way. This approach is compared with traditional approaches, and an experimental study is done to evaluate different neighbour gene

Disruptions are inevitable during airline operations, and disruptions cost airlines and the traveling public. Disruption recovery decisions are often made in a sequential process that includes flight rescheduling, aircraft rerouting, crew reassignment, passenger re-accommodation, and gate reassignment for airline operations. Such a sequential recovery approach alleviates the complexity of the whole recovery process but often causes further disruptions to airport gate assignments and high recovery costs for airlines and passengers. This paper presents a novel airline disruption recovery approach by integrating the schedule and aircraft recovery with gate reassignment. We propose a Benders and column generation (BCG) method to solve the integrated problem. By exploiting the favorable structure arising from the Benders decomposition framework, we provide two acceleration techniques for Benders subproblems, a separation technique and an effective infeasibility certificate, to enhance the efficiency of the BCG method. The proposed model is tested on real-world data. In our experiments, all test instances are solved by the BCG method under a five-minute threshold with optimality gaps wit

The air transportation market is highly competitive and dynamic. Airlines often form alliances to expand their network reach, improve operational efficiency, and enhance customer experience. However, the impact of these alliances on market competition and operational efficiency is not fully understood. In this paper, we propose a novel approach to analyze airline alliances using multi\mfabian{-}attribute graph partitioning. We develop metrics to quantify the competitiveness of flight segments and the market penetration capability of airlines based on their alliance memberships. We formulate a bi\mfabian{-}objective optimization problem to maximize both competition and market penetration simultaneously. We also propose algorithms to solve this optimization problem and demonstrate their effectiveness using real-world flight schedule data. Our results provide insights into the structure of airline alliances and their implications for market competition and operational efficiency.

This study explores the enhancement of customer satisfaction in the airline industry, a critical factor for retaining customers and building brand reputation, which are vital for revenue growth. Utilizing a combination of machine learning and causal inference methods, we examine the specific impact of service improvements on customer satisfaction, with a focus on the online boarding pass experience. Through detailed data analysis involving several predictive and causal models, we demonstrate that improvements in the digital aspects of customer service significantly elevate overall customer satisfaction. This paper highlights how airlines can strategically leverage these insights to make data-driven decisions that enhance customer experiences and, consequently, their market competitiveness.

In practice, both passenger and cargo flights are vulnerable to unexpected factors, such as adverse weather, airport flow control, crew absence, unexpected aircraft maintenance, and pandemic, which can cause disruptions in flight schedules. Thus, managers need to reallocate relevant resources to ensure that the airport can return to normal operations on the basis of minimum cost, which is the airline recovery problem. Airline recovery is an active research area, with a lot of publications in recent years. To better summarize the progress of airline recovery, first of all, keywords are chosen to search the relevant studies, then software is used to analyze the existing studies in terms of the number of papers, keywords, and sources. Secondly, the airline recovery problem is divided into two categories, namely Passenger-Oriented Airline Recovery Problem (POARP) and Cargo-Oriented Airline Recovery Problem (COARP). In POARP, the existing studies are classified according to recovery strategies, including common recovery strategies, cruise speed control strategy, flexible aircraft maintenance strategy, multi-modal transportation strategy, passenger-centric recovery strategy, and clubbing

The aviation industry has experienced constant growth in air traffic since the deregulation of the U.S. airline industry in 1978. As a result, flight delays have become a major concern for airlines and passengers, leading to significant research on factors affecting flight delays such as departure, arrival, and total delays. Flight delays result in increased consumption of limited resources such as fuel, labor, and capital, and are expected to increase in the coming decades. To address the flight delay problem, this research proposes a hybrid approach that combines the feature of deep learning and classic machine learning techniques. In addition, several machine learning algorithms are applied on flight data to validate the results of proposed model. To measure the performance of the model, accuracy, precision, recall, and F1-score are calculated, and ROC and AUC curves are generated. The study also includes an extensive analysis of the flight data and each model to obtain insightful results for U.S. airlines.

This research gives a detailed analysis of the application of blockchain technology to the airline reservation systems in order to bolster trust, transparency, and operational efficiency by overcoming several challenges including customer control and data integrity issues. The study investigates the major components of blockchain technology such as decentralised databases, permanent records of transactions and transactional clauses executed via codes of programs and their impacts on automated systems and real-time tracking of audits. The results show a 30% decrease in booking variations together with greater data synchronization as a result of consensus processes and resistant data formations. The approach to the implementation of a blockchain technology for the purpose of this paper includes many APIs for the automatic multi-faceted record-keeping system including the smart contract execution and controllable end-users approach. Smart contracts organized the processes improving the cycle times by 40% on the average while guaranteeing no breach of agreements. In addition to this, the architecture of the system has no single point failure with over 98% reliability while measures tak

This thesis investigates the dynamics of multimarket contact and airline mergers on collusive pricing of airlines. In align with Bernheim and Whinston (1990) and Athey et.al.(2004), it detects collusive pricing via pairwise price difference and price rigidity. The piece of work extends previous work by incorporating additional controls such as distinction between non-stop and stopover itineraries and detailed market concentration measures. The findings confirm a significant relationship between multimarket contact and reduced price differences, indicating collusive equilibria facilitated by frequent interactions across markets. Moreover, the results highlight that airlines exhibit more collusive behavior when pricing non-stop flights, and are more likely to attain tacit collusion when they approaches duopoly in a particular market. The study also explores the effects of airline mergers on collusion, employing an event study methodology with a difference-in-difference (DID) design. It finds no direct evidence that mergers lead to increased collusion among unmerged carriers. However, it reveals that during and after the merger process, carrier pairs between merged and unmerged carrie

This paper describes an econometric model of the Brazilian domestic carrier Azul Airlines' network construction. We employed a discrete-choice framework of airline route entry to examine the effects of the merger of another regional carrier, Trip Airlines, with Azul in 2012, especially on its entry decisions. We contrasted the estimated entry determinants before and after the merger with the benchmarks of the US carriers JetBlue Airways and Southwest Airlines obtained from the literature, and proposed a methodology for comparing different airline entry patterns by utilizing the kappa statistic for interrater agreement. Our empirical results indicate a statistically significant agreement between raters of Azul and JetBlue, but not Southwest, and only for entries on previously existing routes during the pre-merger period. The results suggest that post-merger, Azul has adopted a more idiosyncratic entry pattern, focusing on the regional flights segment to conquer many monopoly positions across the country, and strengthening its profitability without compromising its distinguished expansion pace in the industry.

In the US airline industry, independent regional airlines fly passengers on behalf of several national airlines across different markets, giving rise to $\textit{common subcontracting}$. On the one hand, we find that subcontracting is associated with lower prices, consistent with the notion that regional airlines tend to fly passengers at lower costs than major airlines. On the other hand, we find that $\textit{common}$ subcontracting is associated with higher prices. These two countervailing effects suggest that the growth of regional airlines can have anticompetitive implications for the industry.