Forecasting and Forecast Combination in Airline Revenue Management Applications

Predicting a variable for a future point in time helps planning for unknown future situations and is common practice in many areas such as economics, finance, manufacturing, weather and natural sciences. This paper investigates and compares approaches to forecasting and forecast combination that can be applied to service industry in general and to airline industry in particular. Furthermore, possibilities to include additionally available data like passenger-based information are discussed

Do Low-Quality Products Affect High-Quality Entry? Multiproduct Firms and Nonstop Entry in Airline Markets

This paper studies the effect of product ownership and quality on nonstop entry in the airline industry. Specifically, this paper empirically examines the decision of an airline to offer high quality nonstop service between cities given that the airline may or may not be offering lower quality one-stop service. I find that airlines that offer one-stop service through a hub are less likely to enter that same market with nonstop service than those that do not. In addition, the quality of the one-stop service is an important determinant of entry. Airlines are more likely to enter a market with nonstop service if their own or their rival'.s one-stop service in the market are of lower quality. Estimates suggest that the entry of a rival nonstop carrier diminishes the probability a carrier enters the market with nonstop service. However, airlines offering one-stop service respond differently to nonstop rivals. In particular, relative to other carriers, those offering one-stop service are more likely to enter markets if there are nonstop rivals, suggesting that cannibalization effects are diminished in the presence of nonstop competition.

Predicting the U.S. Airline Operating Profitability using Machine Learning Algorithms

With the increasing competition and cost pressures, the U.S. airline industry has explored methods to reduce operating costs and diversify revenue sources for improving financial performance. Understanding the influence of operating revenues and expenses on airline profitability is imperative for the long term growth of the airlines and continued generation of profits. This study examined the cost and revenue data of the U.S. major airlines from the Department of Transportation’s Bureau of Transportation Statistics Form 41 reports between 2009 and 2018. Using SAS Enterprise Miner software, researchers used variables representing revenue and expenses from these data to develop and test predictive models for airline profit generation. Decision trees and linear regression methods were used for two identical datasets one with monetary values and the other with percentage values to identify the best predictor of airline profitability. From this study, decision tree models appeared to be better predictors of profitability for major airlines. Using the decision model, transport-related revenue and expenses which are incidentals to the air transportation services performed by airlines were found to be the two most influential factors in predicting the U. S. airlines’ profitability

Complexity challenges in ATM

After more than 4 years of activity, the ComplexWorld Network, together with the projects and PhDs covered under the SESAR long-term research umbrella, have developed sound research material contributing to progress beyond the state of the art in fields such as resilience, uncertainty, multi-agent systems, metrics and data science. The achievements made by the ComplexWorld stakeholders have also led to the identification of new challenges that need to be addressed in the future. In order to pave the way for complexity science research in Air Traffic Management (ATM) in the coming years, ComplexWorld requested external assessments on how the challenges have been covered and where there are existing gaps. For that purpose, ComplexWorld, with the support of EUROCONTROL, established an expert panel to review selected documentation developed by the network and provide their assessment on their topic of expertise

A forecasting Tool for Predicting Australia\u27s Domestic Airline Passenger Demand Using a Genetic Algorithm

This study has proposed and empirically tested for the first time genetic algorithm optimization models for modelling Australia’s domestic airline passenger demand, as measured by enplaned passengers (GAPAXDE model) and revenue passenger kilometres performed (GARPKSDE model). Data was divided into training and testing datasets; 74 training datasets were used to estimate the weighting factors of the genetic algorithm models and 13 out-of-sample datasets were used for testing the robustness of the genetic algorithm models. The genetic algorithm parameters used in this study comprised population size (n): 200; the generation number: 1,000; and mutation rate: 0.01. The modelling results have shown that both the quadratic GAPAXDE and GARPKSDE models are more accurate, reliable, and have greater predictive capability as compared to the linear models. The mean absolute percentage error in the out of sample testing dataset for the GAPAXDE and GARPKSDE quadratic models are 2.55 and 2.23%, respectively

Determination of Factors That Influence Passengers’ Airline Selection: A Study of Low Cost Carriers in Thailand

This research examined the factors that influenced the airline selection of Low Cost Carriers (LCCs) in Thailand. The research was justified based on the rapid growth of LCC travel in Thailand, particularly in domestic and regional travel. There is a relative lack of successful explanation of the choice of LCCs in Thailand, with only a few studies addressing topics like passenger satisfaction and perceptions of service quality. Following an extensive literature review, the author used a theoretical framework based on the Theory of Planned Behavior (TPB) (Ajzen, 1991) in order to explain passenger behavioral intentions. This framework was supplemented by airline operational and marketing factors identified from the literature, including Price, Service Quality, Airline Reputation, Airline Safety, Route Availability and Convenience, and Frequent Flier Programs. A large scaled survey was sent to Thai LCC passengers at major airports in Thailand. The final sample (n = 781) was predominantly working-age, female, highly educated, and with average incomes. In general, they flew frequently (two to three times a year or more). In order to test the relationship among the external factors, TPB factors, behavioral intentions, and actual behavior, Structural Equation Modeling (SEM) was conducted. Results showed that Subjective Norms, Perceived Behavioral Control, Airline Reputation, Price, and Service Quality had a positive impact on Behavioral Intentions, while Behavioral Intentions positively influenced Buying Behavior. This research has important implications both in academia and industry. It indicates that LCC passengers are not merely driven by price as concluded by economic studies in LCC selection. Instead, factors like service quality, airline reputation, and social acceptability implied by subjective norms play a significant role in the choice of LCCs over Full Service Carriers (FSCs). Additionally, the results of this research provide LCCs with useful guidance to form appropriate strategies to attract more passengers: protecting price leadership, improving service quality, enhancing public image, and maintaining route diversity

Which univariate time series model predicts quicker a crisis? The Iberia case

In this paper four univariate models are fitted to monthly observations of the number of passengers in the Spanish airline IBERIA from January 1985 to October 1994. During the first part of the sample, the series shows an upward trend which has a rupture during 1990 with the slope changing to be negative. The series is also characterized by having seasonal variations. We fit a deterministic components model, the Holt-Winters algorithm, an ARIMA model and a structural time series model to the observations up to December 1992. Then we predict with each ofthe models and compare predicted with observed values. As expected, the results show that the detenninistic model is too rigid in this situation even if the within-sample fit is even better than for any of the other models considered. With respect to Holt-Winters predictions, they faH because they are not able to accornmodate outliers. Finally, ARIMA and structural models are shown to have very similar prediction performance, being flexible enough to predict reasonably well when there are changes in trend

Stochasticity in pandemic spread over the World Airline Network explained by local flight connections

Massive growth in human mobility has dramatically increased the risk and rate of pandemic spread. Macro-level descriptors of the topology of the World Airline Network (WAN) explains middle and late stage dynamics of pandemic spread mediated by this network, but necessarily regard early stage variation as stochastic. We propose that much of early stage variation can be explained by appropriately characterizing the local topology surrounding the debut location of an outbreak. We measure for each airport the expected force of infection (AEF) which a pandemic originating at that airport would generate. We observe, for a subset of world airports, the minimum transmission rate at which a disease becomes pandemically competent at each airport. We also observe, for a larger subset, the time until a pandemically competent outbreak achieves pandemic status given its debut location. Observations are generated using a highly sophisticated metapopulation reaction-diffusion simulator under a disease model known to well replicate the 2009 influenza pandemic. The robustness of the AEF measure to model misspecification is examined by degrading the network model. AEF powerfully explains pandemic risk, showing correlation of 0.90 to the transmission level needed to give a disease pandemic competence, and correlation of 0.85 to the delay until an outbreak becomes a pandemic. The AEF is robust to model misspecification. For 97% of airports, removing 15% of airports from the model changes their AEF metric by less than 1%. Appropriately summarizing the size, shape, and diversity of an airport's local neighborhood in the WAN accurately explains much of the macro-level stochasticity in pandemic outcomes.Comment: article text: 6 pages, 5 figures, 28 reference