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4 Airports rely on air passenger demand studies and forecasts for a variety of purposes, such as airport planning, airport marketing, air service development, and passenger leakage. The models and forecasts developed for these purposes typically correlate an airportâs passenger activity to aggregate regional socioeconomic aggregate measures, such as regional population, average household income, and various measures of regional economic output. Such an approach implicitly assumes that the distribution of socioeconomic factors in the passenger population served by an airport remains relatively constant over time, and therefore the air travel behavior of the population can be adequately explained by these aggregate measures. However, there is concern that socioeconomic changes, such as changes in the age, racial and ethnic composition of society, the distribution of household incomes, and changing views on the use of dispos- able income, may not be captured well in these current approaches to air passenger demand modeling. The research project documented in this report explored whether the effects of these societal changes on air passenger demand may be better reflected in air passenger demand studies and models by using disaggregated socioeconomic data, such as regional distributions of age, gender, or household incomes. There has been little research to date that has investigated how disaggre- gated socioeconomic data can be incorporated into air passenger demand models or that evalu- ates potential benefits from using data that is more detailed in this way. By identifying examples and sources for disaggregated socioeconomic variables and exploring methods for incorporating them into traditional and more innovative models for air passenger demand, this research study can serve as an initial step toward a greater understanding of the opportunitiesâand potential difficultiesârelated to greater use of disaggregated socioeconomic variables. This research project was organized around five specific areas of inquiry, one of them involving a thorough review of existing studies and models of air passenger demand for airports, regions, and other geographical entities. The other four areas of inquiry were more forward-looking or open-ended, representing new research conducted during the project that built on the founda- tion of the assessment of the existing state of practice. The following questions summarize the motivations for each of the five areas of research inquiry pursued during the project: â¢ How have airports, airport consultants, academics, government agencies, and others conducted past studies of air passenger demand, based on published reports and planning documents? What types of socioeconomic data were used in the formal modeling, and how were those models structured? â¢ How does air passenger behavior differ across socioeconomic subsets of the population, especially with respect to individual or household propensities to travel by air? â¢ Can disaggregated socioeconomic factors be introduced into traditional approaches to modeling air passenger demand at individual airports or systems of airports, and do those C H A P T E R 1 Introduction
Introduction 5 variables provide new information compared to a baseline of traditional air passenger demand modeling using aggregate socioeconomic variables? â¢ Can new approaches to structuring econometric models or other approaches be developed and used to realize the value of incorporating disaggregated socioeconomic data in under- standing or modeling air passenger demand? â¢ Are there new forms of disaggregated socioeconomic data, or new ways of collecting such data, that can be used to model or study air passenger demand at airports and in regions? This report summarizes the projectâs research effort and results in these areas. Chapter 2 surveys past models and studies of air passenger demand, examining research conducted by academics, government agencies, and airport practitioners of many areas of expertise. The research discussed in Chapter 2 includes past models of air passenger demand and past efforts to identify passenger preferences and characteristics through their responses to passenger or consumer surveys. Chapter 3 assesses potential sources of disaggregated socioeconomic data that could be used in models of air passenger demand modified to include such inputs. This chapter covers sources for and examples of disaggregated regional socioeconomic data, in which regional populations and households are divided into distinct cohorts according to specific socioeconomic categories such as age or household income. It also provides examples of the trends in income and age distributions at the national level that motivate the growing interest in introducing into models new forms of data that capture how these divisions of populations into distinct cohorts has evolved in recent years. Chapter 3 then examines the results of a wide range of surveys of households and air travelers to explore how air travel propensity, expressed as the average number of air trips taken per year, varies with disaggregated socioeconomic characteristics and the implications of these findings for studies of air travel demand. This identification of the distribution of socioeconomic char- acteristics among air passengers is the core of the Chapter 3 analysis because if population sub- groups are more or less likely to use air transportation than others, changes in the distribution of socioeconomic traits in the general population (at the national or regional level) may have implications for the volume and patterns of air passenger demand over the same time period. Chapter 3 shows that across the airports analyzed there are common patterns of use of air travel by different socioeconomic or demographic cohorts, such as household income strata, age groups, or genders. The analysis of air passenger surveys indicates that air travel propensity, expressed as the average number of air trips per year by an individual or household, varies widely with a broad range of respondent socioeconomic characteristics, including household income, age, race/ethnicity, and educational attainment. It can be expected that changes in the distribu- tion of any of these characteristics across the population will have an effect on air travel demand. It also follows that the common practice in air passenger demand models of using aggregate or average measures of household income will fail to reflect the effect of changes in the distribution of household incomes as a percentage of the average income level. In addition, analysis of recent trends in age and income distributions for U.S. households indicate widening disparities in household incomes, with incomes growing more vigorously for higher income householdsâa finding that resonates with those of many recent analyses of the U.S. income distributionâas well as some correlation between such variables as household income levels and the age of the heads of U.S. households. Chapter 4 contains the results of the research projectâs case study of the effectiveness of adding disaggregated socioeconomic variables to traditional air passenger enplanement models that are based on the use of aggregate regional socioeconomic data, such as regional population, income, or economic output. The case study sample includes eight U.S. airports or airport systems of
6 Using Disaggregated Socioeconomic Data in Air Passenger Demand Studies differing size and circumstance. For each of these, the case study exercise, using annual data from 1990 to 2010, compares model and forecast performance of a âtraditionalâ regression model using aggregated socioeconomic variables with those of an âalternativeâ model that also includes a regional disaggregated household income variable. In the second half of the chapter, air passenger demand in the three-airport Baltimore-Washington region, which is included among the eight case study airports or regions, is analyzed in greater detail using more complex models. The analysis in Chapter 4 indicates that including disaggregated socioeconomic variables in regressions that also use aggregate regional socioeconomic variables can modestly improve model fit to the sample data, as measured by the adjusted R-squared, although the strong posi- tive correlations among the socioeconomic variables often result in statistically insignificant regression parameter estimates. This is especially true in the case study regression results pre- sented in the first half of Chapter 4. The case study regressions without and with a disaggregated socioeconomic independent variable are also analyzed for each case study airport or airport sys- tem by comparing out of sample forecast performance. The results for this performance measure are mixed, because for some case study airports the forecast accuracy is poor at best, and adding the disaggregated socioeconomic variable to the model does not always improve the forecast accuracy compared to the baseline model using only the aggregated socioeconomic variable. In the first half of Chapter 4, the case study regression models that are compared use a fairly simple functional form with a limited number of variables in each model. In the second half of Chapter 4, more complex models with additional variables, including dummy variables to account for year-specific effects, are used in a more detailed analysis of air passenger demand in the Baltimore-Washington metropolitan region. Because that region is served by three major commercial service airports, it is possible to analyze regional demand while avoiding distor- tions from changes in the regional share of specific airports. Along with the exploration of more complex models, the analysis explored the use of an alternative disaggregated measure of house- hold income that was not strongly correlated with aggregate measures of household income, an important difference from the strong correlations between socioeconomic variables that were present in the initial case study analysis. This more detailed analysis of passenger demand in the Baltimore-Washington region confirms the finding from the case study analysis that relies on much simpler model specifications and a limited number of variables. Including a disaggregated household income variable improves the model fit and can improve forecast accuracy over a 5-year out of sample period, although for both measures the improvements over the same model with only an aggregate household income variable are not large. Over longer forecast periods, a disaggregated household income variable in an air passenger demand model allows forecasts to be prepared, assuming different scenarios of future changes in household income distribution and an analysis to be performed of the sensitivity of forecast levels of air passenger demand to possible changes in household income distribution. As found in the initial case study results, the inclusion of the disaggregated household income variable in the more detailed models of Baltimore-Washington airport system O&D enplane- ments resulted in a significant change in the parameter estimate for the aggregate household income variable that was included in the baseline specification and the specification that also included the disaggregated variable. Chapter 5 covers the research projectâs work on the final two motivating questions: are there effective new approaches or specifications for modeling air passenger demand using disaggre- gated socioeconomic variables and are there new forms of disaggregated socioeconomic vari- ables that can support new types of air travel demand models? Answers to the first of these rely in part on the prior research work on identifying passenger propensities to travel by air for
Introduction 7 passengers of differing socioeconomic characteristics. Four distinct approaches to modeling air passenger demand in ways that may allow the incorporation of disaggregated socioeconomic data into the set of independent model variables are explored. Each of these represents a direc- tion for future research in this area. Answers to the second question are developed through a detailed examination of records of air travel expenditure activity found in (personally dis-identified) financial transaction records, a data source of increasing interest (and use) by many marketing organizations. Based on this analysis of the financial transaction database, the research team concludes that although finan- cial transaction data show promise for use in the future, these data currently are deficient in criti- cal ways, most notably in the inability to consistently identify the airports used by the travelers identified in the data. Further research is needed to determine how complete and representative these financial transaction databases are. Chapter 6 summarizes the projectâs research findings in greater detail, identifying issues that remain unresolved and proposing potential opportunities for future air passenger demand research.