The impact of high inflation rates on the demand for air passenger transportation

Originally written as the first author's M.S. thesis, Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1975May 1975Includes bibliographical references (p. 116-117)The impact of high inflation rates on the demand for domestic air passenger transportation is tested in a demand model using time-series data and linear and non-linear least squares regressions with Revenue Passenger Miles as the dependent variable, and measures of cost, income and inflation as the explanatory variables. The investigation begins with an extensive survey of the past and current air transportation demand models. The model selected uses linear and non-linear log specifications to account for the secular trend and detrended variables to account for the cyclical variations. These transformations allow determination of the coefficients comparable to delta log models and simultaneously retain the forecasting ability of linear log models. Forecasts are provided to 1990 for both the linear and non-linear secular trends. Results show that the price is the most stable and significant determinant of demand. Income and the rate of inflation are both significant but are more variable and highly dependent on the type of secular trend and the time period used in the regression. The non-linear secular trend model provided the best overall fit and explained 96% of the variation in demand

New directions for forecasting air travel passenger demand

July 1974Includes bibliographical references (p. 160-163)While few will disagree that sound forecasts are an essential prerequisite to rational transportation planning and analysis, the making of these forecasts has become a complex problem with the broadening of the scope and variety of transportation decisions. Until recently, the forecasting methods available addressed the issues which were important a couple of decades ago. These methods attempted to predict the amount and in some cases character of travel to be used in designing major highways, transit facilities, seaport facilities, and airports. However, today's issues to be addressed in transportation are much broader and more complex. For example, in the modern process of transportation planning, the decision-maker is concerned with the broad range of social, economic and environmental effects, equity issues, wider range of options including not building major facilities, resource constraints such as energy, and increased public participation in the planning process in general. The complexity of the problem has necessitated the planner's developing improved methods of forecasting the demand for transportation at all levels and by all modes. While significant contributions have been made recently to the development of improved methods in forecasting, we are still a long way from possessing tools which provide our decision-makers with more effective, that is, more useful, accurate and timely information. The purpose of this report is to present a very brief overview of the current and emerging air transportation forecasting methods with the aim of identifying areas which need further research. Throughout the report, the object is to indicate future directions for research into transportation forecasting methods which are more responsive to today's issues. For example, it is clear from reviewing the literature that tremendous improvements in travel forecasting methods can be achieved through deeper understanding of the traveler's behavior, under a range of conditions, development of models which are more policy-responsive and development of improved data bases. Peculiarities of the airline industry and aviation in general cause many standard techniques of economic and managerial analyses to break down. Air travel demand is unique in that even the sophisticated techniques developed by urban transportation analysts are often not directly applicable to modelling the demand for air transportation. Econometricians usually do not have specific training in air transportation. Airline managers, on the other hand, quite often do not have the technical background necessary to fully understand many highly detailed and complex models. In order to develop sophisticated yet user-oriented models, an analyst must have background in several areas. It is hoped that the material presented in this report will help bridge the gap between managerial and technical personnel and provide some new directions for air travel demand modelling. Generally speaking, there are two broad categories of forecasting methods. The quantitative group is composed of techniques which rely on the existence of historical data, and which assume that the historical trend will be expected to continue in the future. This group is further divided into two classes, time-series methods and causal methods. The quantitative techniques are by far the most widely used and contain such popular methodologies as moving averages, classical decomposition analysis, spectral analysis, adaptive filtering and Box-Jenkins methods under the category of time-series analysis. The causal methods contain such favorites as modelling classical consumer behavior through regression models and more recent applications in transportation demand analysis of Bayesian analysis, Markov chains, input-output analysis, simulation methods and control theory models. The second group of forecasting methods is qualitative in nature. The techniques in this group are used when none or very little historical data exists, or when the underlying trend of the historical data is expected to change. Qualitative techniques have in general been applied to project future technological developments and their impacts are described in literature as "technological forecasting methods." The group is further divided into two classes, exploratory and normative methods. The exploratory methods start with today's knowledge and its orientation and trends and seek to predict what will happen in the future and when. On the other hand, normative methods seek first to assess the organization's goals and objectives and then work backwards to identify new developments which will most likely lead to the achievement of these goals. Familiar examples of exploratory methods are the envelope, logistic or S-curve, the Delphi technique and morphological analysis. Examples of methods used to perform normative forecasting are relevance trees and cross impact analysis. Although this classification scheme is consistent with the way that many forecasters might differentiate models, it is by no means unique. Other and perhaps better classification schemes exist. For the purposes of this report we will not attempt to define a particular classification but present five broad areas which show the greatest potential for improving our capabilities of modelling the demand for air transportation. These areas are: technological forecasting, time-series models, control theory models, econometric models and simulation models. Each of the general techniques are reviewed, and specific examples are presented where relevant. Excessive mathematical detail was avoided in order to make this work easily understandable by managers and others who might not have a rigorous analytical background. Since a number of models discussed in the report require extensive computer modelling, we have included a few computer programs in the appendices to make the report more user-oriented.Prepared in part by the National Science Foundatio

A multi-regression analysis of airline indirect operating costs

June 1968PB 183 012N69-33854Includes bibliographical references (p. 53-54)A multiple regression analysis of domestic and local airline indirect costs was carried out to formulate cost estimating equations for airline indirect costs. Data from CAB and FAA sources covering the years 1962-66 was used, and the costs were broken down into the classification of the uniform system of accounts Form 41, used by the airlines in reporting to the CAB. Thus regression equations were found for 1) annual system expenses in the categories such as Passenger Servicing, Traffic Servicing, Promotion and Sales, General and Administrative, etc. as well as an overall indirect operating cost; and 2) annual station expenses where the Aircraft and Traffic Servicing expenses for individual stations are examined. A stepwise regression technique is used to select the best combinations of independent variables for the equations. The independent variables were data such as revenue passenger miles, passengers enplaned, revenue aircraft miles, total revenue aircraft departures, etc. The results generally showed that a high degree of correlation could be found between the costs and some combination of these variables.Office of High Speed Ground Transport, Dept. of Commerc

Air freight : the problems of airport restrictions : final report on the Conference of Air Cargo Industry Considerations of Airport Curfews

April 1979Conference held in Jupiter, Fla. in January 1979Includes bibliographical references (p. 36)Noise due to aircraft was considered to be a potential problem as far back as 1952, when the Doolittle Commission established by President Truman urged that a major effort be made to reduce aircraft noise. With the 'advent of the jet age in the late 1950's and the concomitant spread of suburbs towards airports in major cities such as New York, Denver, and Minneapolis-St. Paul, many more people became exposed to noise, and concern and anger intensified. Although only a small percentage (estimated at about 2-3%) of the total population of the U.S. is affected by high noise levels, these people and their representatives have been quite vocal about their dissatisfaction with noise abatement progress, even though technological advances have reduced the noise emanating from aircraft engines. As a result, the airports, the communities, and the federal government are seeking additional measures that will further diminish the noise impact of aircraft and airport operations. The dilemma is to decrease noise with the minimum economic disruptions to commerce, the community, and the aviation industry. Since very few people like to travel during the night hours (approximately 10 p.m. - 7 a.m.), and indeed very few aircraft operations take place (less than 5% of total operations at most airports), an environmentally and politically appealing option to diminish the effect of aircraft noise is to ban airplane operations during nighttime hours. However, a disproportionate number of operations at night are dedicated to cargo (about 50% of scheduled domestic all-cargo flights), and it is upon the air cargo industry and those users dependent upon nighttime flights that the major burden of a curfew would fall. The benefits of curfews are apparent; the economic penalties associated with them are not. To address this issue, the Flight Transportation Laboratory of the Massachusetts Institute of Technology hosted a week-long conference at Jupiter, Florida, in January, 1979, on the impact of airport use restrictions on air freight. This conference was sponsored by the Federal Aviation Administration and the Port Authority of New York and New Jersey. More than 70 participants, including some 50 panelists and speakers, represented various viewpoints of the air cargo industry: the users, the airlines, the airports, the communities, and various governmental agencies

An AFM Approach Applied in a Study of α-Crystallin Membrane Association: New Insights into Lens Hardening and Presbyopia Development

The lens of the eye loses elasticity with age, while α-crystallin association with the lens membrane increases with age. It is unclear whether there is any correlation between α-crystallin association with the lens membrane and loss in lens elasticity. This research investigated α-crystallin membrane association using atomic force microscopy (AFM) for the first time to study topographical images and mechanical properties (breakthrough force and membrane area compressibility modulus (KA), as measures of elasticity) of the membrane. α-Crystallin extracted from the bovine lens cortex was incubated with a supported lipid membrane (SLM) prepared on a flat mica surface. The AFM images showed the time-dependent interaction of α-crystallin with the SLM. Force spectroscopy revealed the presence of breakthrough events in the force curves obtained in the membrane regions where no α-crystallin was associated, which suggests that the membrane’s elasticity was maintained. The force curves in the α-crystallin submerged region and the close vicinity of the α-crystallin associated region in the membrane showed no breakthrough event within the defined peak force threshold, indicating loss of membrane elasticity. Our results showed that the association of α-crystallin with the membrane deteriorates membrane elasticity, providing new insights into understanding the molecular basis of lens hardening and presbyopia

Statistical techniques to forecast the demand for air transportation

August 1977Includes bibliographical references (p. 76-78)Introduction and objectives: For some time now regression models, often calibrated using the ordinary least-squares (OLS) estimation procedure, have become common tools for forecasting the demand for air transportation. However, in recent years more and more decision makers have begun to use these models not only to forecast traffic, but also for analyzing alternative policies and strategies. Despite this increase in scope for the use of these models for policy analysis, few analysts have investigated in depth the validity and precision of these models with respect to their expanded use. In order to use these models properly and effectively it is essential not only to understand the underlying assumptions and their implications which lead to the estimation procedure, but also to subject these assumptions to rigorous scrutiny. For example, one of the assumptions that is built into the ordinary least-squares estimation technique is that the explanatory variables should not be correlated among themselves. If the variables are fairly collinear, then the sample variance of the coefficient estimators increases significantly, which results in inaccurate estimation of the coefficients and uncertain specification of the model with respect to inclusion of those explanatory variables. As a corrective procedure, it is a common practice among demand analysts to drop those explanatory variables out of the model for which the t-statistic is insignificant. This is not a valid procedure since if collinearity is present the increase in variance of the coefficients will result in lower values of the t-statistic and rejection from the demand model of those explanatory variables which in theory do explain the variation in the dependent variable. Thus, if one or more of the assumptions underlying the OLS estimation procedure are violated, the analyst must either use appropriate correction procedures or use alternative estimation techniques. The purpose of the study herein is three-fold: (1) develop a "good" simple regression model to forecast as well as analyze the demand for air transportation; (2) using this model, demonstrate the application of various statistical tests to evaluate the validity of each of the major assumptions underlying the OLS estimation procedure with respect to its expanded use of policy analysis; and, (3) demonstrate the application of some advanced and relatively new statistical estimation procedures which are not only appropriate but essential in eliminating the common problems encountered in regression models when some of the underlying assumptions in the OLS procedure are violated. The incentive for the first objective, to develop a relatively simple single equation regression model to forecast as well as analyze the demand for air transportation (as measured by revenue passenger miles in U.S. Domestic trunk operations), stemmed from a recently published study by the U.S. Civil Aeronautics Board [CAB, 1976]. In the CAB study a five explanatory variable regression equation was formulated which had two undesirable features. The first was the inclusion of time as an explanatory variable. The use of time is undesirable since, from a policy analysis point of view, the analyst has no "control" over this variable, and it is usually only included to act as a proxy for other, perhaps significant, variables inadvertently omitted from the equation. The second undesirable feature of the CAB model is the "delta log" form of the equation (the first difference in the logs of the variables),which allowed a forecasting interval of only one year into the future. This form was the result of the application of a standard correction procedure for collinearity among some of the explanatory variables. In view of these two undesirable features, it was decided to attempt to improve on the CAB model. In addition to the explanatory variables considered in the CAB study a number of other variables were analyzed to determine their appropriateness in the model. Sections II and III of this report describe the total set of variables investigated as well as a method for searching for the "best" subset. Then, Section IV outlines the decisions involved in selecting the appropriate form of the equation. The second objective of this study is to describe a battery of statistical tests, some common and some not so common, which evaluate the validity of each of the major assumptions underlying the OLS estimation procedure with respect to single equation regression models. The major assumptions assessed in Section V of this report are homoscedasticity, normality, autocorrelation, and multicollinearity. The intent here is not to present all of the statistical tests that are available, for to do so would be the purpose of regression textbooks, but to scrutinize these four major assumptions enough to remind the analyst that it is essential to investigate in depth the validity and precision of the model with respect to its expanded use of policy analysis. It is hopeful that the procedure outlined in this report sets an example to demand modeling analysts of the essential elements used in the development of reliable forecasting tools. The third and ultimate objective of this work is to demonstrate the use of some advanced corrective procedures in the event that any of the four above mentioned assumptions have been violated. For example, the problem of autocorrelation can be resolved by the use of generalized least-squares(GLS), which is demonstrated in Section VI of this report; and the problem of multicollinearity , usually corrected by employing the cumbersome and restrictive delta log form of equation, has been eliminated by using Ridge regression (detailed in Section VII). Finally, in Section VIII an attempt is made to determine the "robustness" of a model by first performing an examination of the residuals using such techniques as the "hat matrix", and second by the application of the recently developed estimation procedures of Robust regression. Although the techniques of Ridge and Robust regression are still in the experimental stages, sufficient research has been performed to warrant their application to significantly improve the currently operational regression models

A methodology for determining the relationship between air transportation demand and the level of service

January 1976Includes bibliographical referencesIntroduction: Within the last ten years significant advances in the state-of-the art in air travel demand analysis stimulated researchers in the domestic air transportation field. Among these advances, researchers in academia, industry, and government have investigated the relationship between observed demand and general level of economic activity such as GNP on the one hand and general passenger-perceived characteristics such as fare on the other hand. Advanced econometric techniques have been used to develop these relationships. However, to date very little effort has been devoted to investigating the impact of a change in the supply of air transportation service on the demand for air transportation. Thus, for all practical purposes, there are no analytical economic models which show the complex interrelationship between the supply of and the demand for air transportation. This research report is an attempt to begin to understand these complex interrelationships. During the sixties the demand for air transportation services experienced substantial growth rates due to the fact that fares (in constant dollars) were continually declining (because of increasing productivity of transport aircraft) and partly due to the fact that the level of service offered was continuously increasing, again the result of improvements in technology. However, at the beginning of the current decade the growth in the demand for air transportation services began to exhibit radical and unforeseen changes. These changes were caused by a reversal of the impact of the two factors mentioned earlier, namely that the fares were now increasing (due to rapidly increasing costs, particularly with respect to the price of fuel) and the level of service was decreasing, particularly evidenced by fewer total flights and fewer direct flights. The demand models developed in the sixties were adequate to caution airline managers on the impact of changes in the general state of the economy and changes in fare level. However, since these models did not adequately incorporate the factors relating to the supply of air transportation services, very few analysts were able to predict the impact of a change in the level of service. As a result, the industry was quite surprised to observe suppressed traffic growth rates when the level of service offered was changed as a result of a general recession in the economy and shortage of fuel. Due to the deterioration in the financial position, the carriers began to cut costs by reducing further the level of service offered. However, instead of improving the profitability of the carriers, this strategy further suppressed traffic and hence revenue, resulting in even lower profits. On the basis of evidence from the above discussion, there is now a critical need for the development of economic models that simultaneously incorporate the factors effecting both the demand and the supply of air transportation services. In order to begin to fulfill this need, the Aeronautical Systems Office of Ames Research Center at NASA funded a research project to investigate how the supply related variables (particularly those related directly to technology) contribute to the determination of the demand for air transportation. The research was divided into two parts. The first part, mostly exploratory in nature, was designed to determine whether sophisticated economic models incorporating supply and demand factors can be developed given the state-of-the-art in econometric modeling and the limitations of the existing data. During this phase the thrust of the research effort was first to analyze the existing data, second to analyze the components of the levels of service and third to develop simple models which serve merely to generate avenues of pursuit for further research in the second phase. This report presents the results of the initial exploratory phase of the research project and contains directions for research in the second phase to be carried out in 1976. During the first phase, research efforts were directed at investigating single equation models incorporating a level of service index in addition to the usual fare and socioeconomic terms. The models were calibrated using data from fifty-eight region pairs over a sixteen year period. The level of service index developed in this report represents an improvement over the one incorporated in past models (namely flight frequency). The new level of service index is a nondimensional generalized trip time scaled from zero to one, which takes into account not only the number of flights, but also number of intermediate stops, direct or connecting service, speed of aircraft and most important, the matching of the departure schedules to time variability of demand. Based upon the preliminary results, it appears that the level of service is a more appropriate explanatory variable in the demand model than just frequency. The significant results of the demand models developed in this exploratory stage of the research will be discussed in the following sections of this report. Section 2 describes the reasons for calibrating the models based upon region pair data rather than city pair data. Section 3 differentiates between the supply and demand components of air travel and elaborates upon the development of the level of service index. Section 4 discusses the sampling procedures used in determining the region pairs. Section 5 contains the specification of the single equation models and presents the empirical results. The final section of this report outlines the plans for future research in Phase II of this project.Prepared under Contract by the Flight Transportation Laboratory, Dept. of Aeronautics and Astronautics, Massachusetts Institute of Technology for Ames Research Center, National Aeronautics and Space Administratio

Maintenance cost studies of rotary wing commercial transport aircraft

December 1974Includes bibliographical references (p. 121-123)Introduction: The vertical take-off and landing (VTOL) aircraft market has had substantial growth in the period of the last ten years when one considers the overall number of aircraft in use. The military fleet has continued to increase, as have such operators as natural resource (petroleum and lumber) companies, and law enforcement agencies. (See Table 1.) In scheduled passenger service, however, the VTOL- market has not enjoyed sustained growth. Consider Table 2, the type and number of helicopters in passenger service during 1962-1972. Following the cessation of federal subsidies to helicopter operators in 1966 the number of aircraft (and total available seats) has been steadily declining. Table 3 shows the composition of the fleets of the certificated carriers since 1966. Los Angeles Airways has been in bankruptcy since 1969; Chicago Helicopter is now largely a charter operator, although retaining its certificate; New York Airways, after a period of experimentation with the fixed wing Twin Otter (DHC-6) in 1968-1969, finally made it into the black in 1973, flying Sikorsky S-61's; and SFO Helicopter has retrenched its passenger services severely, but is not yet profitable. Why is the state of scheduled passenger operations so bleak? Many answers to this question have been given. For example, it has been said that the aircraft used by the operators have been inadequate: that they have been designed for military use and are ill suited for civilians who have been used to a higher comfort level (especially since most flights taken on a helicopter are in conjunction with a ride on a large, comfortable jet transport). Alternatively, it has been said that the high cost of operating the current helicopter fleet has caused the ticket price to be too high to be attractive to the traveler. Sometimes the operators have been fingered as the culprits -- that they have not priced their product adequately and have structured their networks poorly, i.e., that the failure has been one of management and marketing. And from the purely technology minded, the answer has been that once the properly designed rotary wing aircraft arrives on the scene -- one designed for civilian use and having the proper payload-range configuration -- the market will boom as VTOL aircraft enter city-center to city-center service. Doubtless there is a kernel -of truth in all these explanations, and examples to sustain most of them can be found in the history of helicopter operations in the United States. The intent of the work described in this report was to explore one frequently cited cause of the problem of high operating costs of helicopters in scheduled service - to wit, high maintenance costs of rotary wing aircraft. This attempt was made to allow a look ahead and to predict trends in maintenance costs of future rotary wing aircraft.This work was performed under a NASA Contract for Ames Research Cente

A model for forecasting future air travel demand on the North Atlantic

April 1970Includes bibliographical references (p. 125-128)Introduction: One of the key problems in the analysis and planning of any transport properties and facilities is estimating the future volume of traffic that may be expected to use these properties and facilities. Estimates of this kind are now being made regularly as the transport system continues to expand. The future planning, implementation and operation of a successful transportation system requires accurate and realistic forecasts of traffic volumes. To achieve optimal policies, the planner needs to be able to predict the effect of alternate decisions. Although the planning process involves much more than a forecast of the future traffic statistics, these statistics provide the essential quantitative dimensions for the planning process. Forecasts of expected traffic are an essential prerequisite to long-range planning. The link between planning and forecasting lies in recognizing that in order to bring an expected situation under control, the planner must be provided with the entire spectrum of situations that could be anticipated and, hence, could be planned for. The reasonableness and reliability of these traffic statistics is, therefore, of vital importance to the planner. This study investigates the North Atlantic passenger travel demand. The final goal is to make a forecast of the passenger traffic on this route. It is believed that such a forecast would prove to be a critical tool for long-range planning of transport properties and facilities on both sides of the Atlantic. For this reason, it is important to be well informed about the technical and economic factors which will determine and limit the travel volume, especially for manufacturers of aircraft, domestic and international airlines, and the government. Governments, for example, must be provided with traffic forecasts if they are to provide adequate ground facilities and air traffic control systems