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Author: Jack B. Jarvis
Publisher:
ISBN:
Category : Traffic estimation
Languages : en
Pages : 144
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Book Description
Author: Jack B. Jarvis
Publisher:
ISBN:
Category : Traffic estimation
Languages : en
Pages : 144
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Book Description
Author: Jack Brogden Jarvis
Publisher:
ISBN:
Category : Aeronautics, Commercial
Languages : en
Pages : 206
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Author: Sidney J. Armore
Publisher:
ISBN:
Category : Aeronautics, Commercial
Languages : en
Pages : 98
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Author: Christopher Andrew Boyer
Publisher:
ISBN:
Category :
Languages : en
Pages : 170
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Book Description
The emergence of less restricted fare structures in the airline industry reduced the capability of airlines to segment demand through restrictions such as Saturday night minimum stay, advance purchase, non-refundability, and cancellation fees. As a result, new forecasting techniques such as Hybrid Forecasting and optimization methods such as Fare Adjustment were developed to account for passenger willingness-to- pay. This thesis explores statistical methods for estimating sell-up, or the likelihood of a passenger to purchase a higher fare class than they originally intended, based solely on historical booking data available in revenue management databases. Due to the inherent sparseness of sell-up data over the booking period, sell-up estimation is often difficult to perform on a per-market basis. On the other hand, estimating sell-up over an entire airline network creates estimates that are too broad and over-generalized. We apply the K-Means clustering algorithm to cluster markets with similar sell-up estimates in an attempt to address this problem, creating a middle ground between system-wide and per-market sell-up estimation. This thesis also formally introduces a new regression-based forecasting method known as Rational Choice. Rational Choice Forecasting creates passenger type categories based on potential willingness-to-pay levels and the lowest open fare class. Using this information, sell-up is accounted for within the passenger type categories, making Rational Choice Forecasting less complex than Hybrid Forecasting. This thesis uses the Passenger Origin-Destination Simulator to analyze the impact of these forecasting and sell-up methods in a controlled, competitive airline environment. The simulation results indicate that determining an appropriate level of market sell-up aggregation through clustering both increases revenue and generates sell-up estimates with a sufficient number of observations. In addition, the findings show that Hybrid Forecasting creates aggressive forecasts that result in more low fare class closures, leaving room for not only sell-up, but for recapture and spill-in passengers in higher fare classes. On the contrary, Rational Choice Forecasting, while simpler than Hybrid Forecasting with sell-up estimation, consistently generates lower revenues than Hybrid Forecasting (but still better than standard pick-up forecasting). To gain a better understanding of why different markets are grouped into different clusters, this thesis uses regression analysis to determine the relationship between a market's characteristics and its estimated sell-up rate. These results indicate that several market factors, in addition to the actual historical bookings, may predict to some degree passenger willingness-to-pay within a market. Consequently, this research illustrates the importance of passenger willingness-to-pay estimation and its relationship to forecasting in airline revenue management.
Author: Richard H. Zeni
Publisher: Universal-Publishers
ISBN: 1581121415
Category : Business & Economics
Languages : en
Pages : 274
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Book Description
Accurate forecasts are crucial to a revenue management system. Poor estimates of demand lead to inadequate inventory controls and sub-optimal revenue performance. Forecasting for airline revenue management systems is inherently difficult. Competitive actions, seasonal factors, the economic environment, and constant fare changes are a few of the hurdles that must be overcome. In addition, the fact that most of the historical demand data is censored further complicates the problem. This dissertation examines the challenge of forecasting for an airline revenue management system in the presence of censored demand data. This dissertation analyzed the improvement in forecast accuracy that results from estimating demand by unconstraining the censored data. Little research has been done on unconstraining censored data for revenue management systems. Airlines tend to either ignore the problem or use very simple ad hoc methods to deal with it. A literature review explores the current methods for unconstraining censored data. Also, practices borrowed from areas outside of revenue management are adapted to this application. For example, the Expectation-Maximization (EM) and other imputation methods were investigated. These methods are evaluated and tested using simulation and actual airline data. An extension to the EM algorithm that results in a 41% improvement in forecast accuracy is presented.
Author: Jeffrey Stuart Zickus
Publisher:
ISBN:
Category : Airlines
Languages : en
Pages : 138
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Book Description
Airline revenue management entails protecting enough seats for late-booking, high-fare passengers while still selling seats which would have otherwise gone empty at discounted fares to earlier-booking customers. In the evolution of revenue management to network origin-destination control, previous research has shown that revenue gains of some seat optimization algorithms can be much lower than others. One possible reason is the process by which demand estimates are generated; namely, forecasting and detruncation. Forecasting is used to estimate passenger demand based on historical flight data, while detruncation makes projections of what demand would have been in cases where the historical data has been constrained by a capacity limitation. This thesis explores the question of the interaction between forecasting methods, detruncation methods, and seat optimization algorithms on a simulated airline network, using the Passenger Origin-Destination Simulator (PODS) revenue management simulation tool, which models a network environment with two competing airlines. Changes in the forecasting and detruncation methods in combination with the seat optimization algorithms were tested in order to see what revenue impacts resulted. Additionally, passenger loads, forecasts, and fare class availability were examined to understand the reasons behind the observed revenue results. The simulations showed that seat optimizers which had relatively poor performance using a standard forecasting and detruncation method had substantial revenue increases when different forecasting and detruncation combinations were implemented. The results also indicate that the better combination of forecasting and detruncation causes higher revenues for all seat optimization methods tested, as a better passenger mix is realized due to higher levels of detruncation and more accurate forecasts. However, the sensitivity of the seat optimizers to the forecasting and detruncation methods remains mixed. Inferior detruncation (or forecasting) methods on a network can offset the revenue gains resulting from improvement to origin-destination control from leg-based control for some seat optimization algorithms.
Author: Nawal K. Taneja
Publisher: Free Press
ISBN:
Category : Business & Economics
Languages : en
Pages : 264
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Author: United States. Congress. Senate. Committee on the Judiciary. Subcommittee on Administrative Practice and Procedure
Publisher:
ISBN:
Category :
Languages : en
Pages : 466
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Author: United States. Congress. Senate. Committee on the Judiciary. Subcommittee on Administrative Practice and Procedure
Publisher:
ISBN:
Category :
Languages : en
Pages : 464
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Author:
Publisher:
ISBN:
Category : Forecasting
Languages : en
Pages : 110
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Book Description
