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Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2019. Using Disaggregated Socioeconomic Data in Air Passenger Demand Studies. Washington, DC: The National Academies Press. doi: 10.17226/25411.
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Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2019. Using Disaggregated Socioeconomic Data in Air Passenger Demand Studies. Washington, DC: The National Academies Press. doi: 10.17226/25411.
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Page 121
Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2019. Using Disaggregated Socioeconomic Data in Air Passenger Demand Studies. Washington, DC: The National Academies Press. doi: 10.17226/25411.
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119 References Abrahams, Michael (1983). A Service Quality Model of Air Travel Demand: An Empirical Study. Transportation Research Part A: General, 17(5): 385–393, September. Airport Authority Hong Kong (2011). Our Airport, Our Future: Hong Kong International Airport Master Plan 2030 – Technical Report. Hong Kong, China: May. Australia. Bureau of Transport and Communications Economics (1995). Demand Elasticities for Air Travel to and from Australia. Canberra: Department of Transport. (BTCE Working Paper 20) ——— (1986). Demand for Australian Domestic Aviation Services: Forecasts by Market Segment. Canberra: Australian Government Publishing Service. (BTE Occasional Paper 79) Baik, Hojong, et al. (2008). Forecasting Model for Air Taxi, Commercial Airline, and Automobile Demand in the United States. Transportation Research Record, Journal of the Transportation Research Board, No. 2052: 9-20. Baumol, W., and R. Quandt (1966). The Demand for Abstract Modes: Theory and Measurement. Journal of Regional Science, 6(2): 13–26. Behrens, Christiaan, and Eric Pels (2012). Intermodal Competition in the London-Paris Passenger Market: High-Speed Rail and Air Transport. Journal of Urban Economics, 71(3): 278–288, May. Belsley, David A., Edwin Kuh, and Roy E. Welsch (1980). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. New York: John Wiley & Sons. Bhadra, Dipasis (2003). Demand for Air Travel in the United States: Bottom-Up Econometric Estimation and Implications for Forecasts by Origin and Destination Pairs. Journal of Air Transportation, 8(2): 19–56. Bhadra, Dipasis (2013). Terminal Area Forecast Modernization (TAF-M). Federal Aviation Administration, Office of Aviation Policy and Plans, Presentation to Recurrent Planning Session, Washington, D.C., August 1. Bhadra, Dipasis (2014). City Pair Markets in the FAA’s TAF-M Forecast Models. Presentation at the 93rd Annual Meeting of the Transportation Research Board, Washington, DC, 12–16 January. Bhadra, Dipasis, and Jacqueline Kee (2008). Structure and Dynamics of the Core U.S. Air Travel Markets: A Basic Empirical Analysis of Domestic Passenger Demand. Journal of Air Transport Management, 14(1): 27–39, January. Binder, S., Macfarlane, G., Garrow, L.A. and Bierlaire, M. (2014). Associations Among Household Characteris- tics, Vehicle Characteristics and Emission Failures: An Application of Targeted Marketing Data. Transporta- tion Research Part A 59: 122–133. Caltrans (2016). California Household Travel Survey. California Department of Transportation, Division of Transportation Planning, Sacramento, California: Accessed January 27. http://www.dot.ca.gov/hq/tpp/ offices/omsp/statewide_travel_analytics/chts.html Cambridge Systematics (2008). National Travel Demand Forecasting Model Phase I Final Scope. Cambridge, MA, September. (Prepared for the American Association of State Highway and Transportation Officials-AASHTO, Standing Committee on Planning, as part of NCHRP Project 08-36, Task 70.) ——— (2014a). California High-Speed Rail 2014 Business Plan. Ridership and Revenue Forecasting. Technical Memorandum. Cambridge, MA, April. (Prepared for Parsons Brinckerhoff for the California High-Speed Rail Authority) ——— (2014b). California High-Speed Rail Ridership and Revenue Model. Version 2.0 Model Documentation. Final Report. Cambridge, MA, April. (Prepared for the California High-Speed Rail Authority) Carvalho, Tassio. (August 31, 2015). Interview with Tassio Carvalho, Senior Manager, Operations Research, American Airlines, Dallas, TX. Castelli, Lorenzo; Rafaelle Pesenti and Walter Ukovich (2003). An Airline-Based Multilevel Analysis of Airfare Elasticity for Passenger Demand. Paper presented at the 7th Air Transport Research Society (ATRS) World Conference, Toulouse, France, 10–12 July.

120 Using Disaggregated Socioeconomic Data in Air Passenger Demand Studies Chi, Junwook; Won W. Koo, and Siew H. Lim (2010). Demand Analysis for Air Passenger Service in U.S. City-Pair Markets. Journal of the Transportation Research Forum, 49(1): 81–93, Spring. Cosmas, Alexander and Wollersheim, Christoph (September 23, 2015). Alexander Cosmas, Chief Scientist, and Christoph Wollersheim, Lead Associate, Booz Allen Hamilton, Boston, MA. Dargay, Joyce (2010). The Prospects for Longer Distance Domestic Coach, Rail, Air and Car Travel in Britain. Leeds, England: University of Leeds, Institute for Transport Studies. (Report to the Independent Transport Commission). Dietz, Bryan (2014). Data 1: Demographics. Presentation at the Air Service Data Seminar, Airports Council International - North America, Albuquerque, NM, 26–28 January. Dublin Airport Authority (2006). Dublin Airport Passenger & Aircraft Movement Demand Forecast Report. Final version, Ref DAPF06/01. Dublin, Ireland: Group Strategy Department, April (text updated August). Fridström, Lasse, and Harald Thune-Larsen (1989). An Econometric Air Travel Demand Model for the Entire Conventional Domestic Network: The Case of Norway. Transportation Research Part B: Methodological, 23(3): 213–223, June. FAA (2016). FAA Aerospace Forecast: Fiscal Years 2016–2036. Washington, DC: U.S. Federal Aviation Admin- istration, March. Gosling, Geoffrey (2014). Use of Air Passenger Survey Data in Forecasting Air Travel Demand. Transportation Research Record: Journal of the Transportation Research Board, No. 2449: 79–87. Gosling, Geoffrey D. (2008) ACRP Synthesis 5: Airport Ground Access Mode Choice Models. Transportation Research Board of the National Academies, Washington, D.C. Granados, Nelson et al. (2012). À La Carte Pricing and Price Elasticity of Demand in Air Travel. Decision Support Systems, 53(2): 381–394, May. Greater Toronto Airports Authority (2007). Taking Flight: The Airport Master Plan 2008–2030. Toronto – Lester B. Pearson International Airport. Toronto, Canada: December. Hamal, Krishna (1998). Australian Outbound Holiday Travel Demand: Long-haul Versus Short-haul. Paper presented at the 8th Australian Tourism and Hospitality Research Conference, Gold Coast, Queensland, 11–14 February. (BTR Conference Paper 98.2) Heimlich, John P. (2016). Status of Air Travel in the USA. Airlines for America, Presentation to TRB Airline Subcommittee, Washington, D.C., June 29. Heimlich, John P., and Chris Jackson (2017). Air Travelers in America: Survey Highlights. Airlines for America, Presentation, Washington, D.C, February 28. Horowitz, Alan (2006). NCHRP Synthesis 358: Statewide Travel Forecasting Models. Transportation Research Board, Washington, D.C. Hotle, Susan and Laurie Garrow (2014). The Role of Competitor Pricing on Multi-Airport Search. Transporta- tion Research Record: Journal of the Transportation Research Board, No. 2400: 21–27. Howard, Chris. (September 3, 2015). Interview with Chris Howard, Manager, Business Intelligence Services, Airlines Reporting Corporation, Arlington, VA. InterVISTAS Consulting, Inc. (2007). Estimating Air Travel Demand Elasticities. Final Report. (Report prepared for IATA) ——— (2014). Estimating Air Travel Demand Elasticities. Final Report. (Report prepared for Airports Council International-North America) Intraplan Consult, GmbH (2014). Air Traffic Forecast – Aéroport International de Genève. Final Report, Pre- pared for the Swiss Federal Office of Civil Aviation (Office fédéral de l’aviation civile). Munich, Germany: December Ippolito, Richard (1981). Estimating Airline Demand with Quality of Service Variables. Journal of Transport Economics and Policy, 15(1): 457–64, January. Kennedy, Peter (1985). A Guide to Econometrics. Cambridge, MA: MIT Press. King, Bill (2014). E-mail communication to Geoffrey Gosling from Bill King, Senior Business Development Executive, AirSage, Atlanta, Georgia: March 3. King, Bill (2015). Population Movements: Data Solutions for Your Transportation Studies. Powerpoint presentation provided to Laurie Garrow on January 7, 2016. AirSage, Atlanta, Georgia: November. King, Bill (2016). Telephone discussion between Laurie Garrow and Bill King, Senior Business Development Executive, AirSage, Atlanta, Georgia: January 7. Kressner, Josephine, and Laurie Garrow (2012). Using Lifestyle Segmentation Variables as Predictors of Home-Based Trips for Atlanta, Georgia, Airport. Transportation Research Record: Journal of the Transporta- tion Research Board, No. 2266: 20-30. Landau, Steve, et al. (2015). ACRP Web-Only Document 22: Passenger Value of Time, Benefit-Cost Analysis, and Airport Capital Investment Decisions. Transportation Research Board, Washington, D.C. LeBoff, Peter (2016). Forecast Process for 2015 TAF. Federal Aviation Administration, January 19. Accessed January 22, 2016, http://www.faa.gov/data_research/aviation/taf/

References 121 Lubulwa, A.S.G. (1986). Brandow Demand Functions for Australian Long Distance Travel. In: 11th Australian Transport Research Forum, Darwin 14–15 May 1986: Forum Papers, Volume 2, 200–218. [Darwin]: Northern Territory Department of Transport and Works. May, T.E.; E.W.A. Butcher and G. Mills (1986). Consumer Responsiveness to Changes in Air Fares. In: Inde- pendent Review of Economic Regulation of Domestic Aviation, Volume 2, Appendix L. Canberra: Australian Government Publishing Service. Macfarlane, G, Garrow, L.A., and Mokhtarian, P (2015). The Influences of Past and Present Residential Locations on Vehicle Ownership Decisions. Transportation Research Part A, 74: 186–200. McCartney, Scott. (2015). Travel to the Airport of the Future. The Wall Street Journal, July 16. Morrison, Steven, and Clifford Winston (1985). An Econometric Analysis of the Demand for Intercity Passenger Transportation. Research in Transportation Economics, 2: 213–237. Mumbower, Stacey; Laurie Garrow, and Matthew Higgins (2014). Estimating Flight-level Price Elasticities Using Online Airline Data: A First Step toward Integrating Pricing, Demand, and Revenue Optimization. Transportation Research Part A: Policy and Practice, 66: 196–212, August. NuStats, LLC (2013). 2010–2012 California Household Travel Survey, Final Report. Prepared for the Califor- nia Department of Transportation, in association with GeoStats, Franklin Hill Group, and Mark Bradley Research & Consulting. Austin, Texas: June. Oum, Tae, and David Gillen (1983). The Structure of Intercity Travel Demands in Canada: Theory Tests and Empirical Results. Transportation Research Part B: Methodological, 17(3): 175–191, June. Oum, Tae; David Gillen and S.E. Noble (1986). Demand for Fareclasses and Pricing in Airline Markets. The Logistics and Transportation Review, 22(3): 195–222. Outwater, Maren, et al. (2015). A Tour-Based National Model System to Forecast Long-Distance Passenger Travel in the United States. Paper 15-4322 presented at the 94th Annual Meeting of the Transportation Research Board, Washington, DC, 11-15 January. Parrella, Barney Co., et al. (2013) ACRP Report 98: Understanding Airline and Passenger Choice in Multi-Airport Regions. Transportation Research Board of the National Academies, Washington, D.C. Proctor, Bernadette D., Jessica L. Semega, and Melissa A. Kollar (2016). Income and Poverty in the United States: 2015. Washington, DC: U.S. Census Bureau, September (Current Population Reports P60-256(RV)). Resource Systems Group, Inc., et al. (2015). ACRP Report 118: Integrating Aviation and Passenger Rail Planning. Transportation Research Board of the National Academies, Washington, D.C. Sydney Airport (2014). Sydney Airport Master Plan 2033. Sydney, Australia: March. Suzuki, Yoshinori, and Michael Audino (2003). “The Effect of Airfares on Airport Leakage in Single-Airport Regions,” Transportation Journal (American Society of Transportation & Logistics), 42(5): 31–41, Fall. United Kingdom Airports Commission (2015). Strategic Fit: Forecasts, London, England, July. UK Department for Transport (2013). UK Aviation Forecasts. London, DfT. U.S. Bureau of Transportation Statistics (2006). Long Distance Transportation Patterns: Mode Choice. Amer- ica on the Go, Findings from the National Household Travel Survey. U.S. Department of Transportation, Washington, DC, May. U.S. Census Bureau. Historical Income Tables: Households (2017), Last revised: August 10 https://www.census. gov/data/tables/time-series/demo/income-poverty/historical-income-households.html U.S. Federal Aviation Administration (FAA) (2017). Terminal Area Forecast (TAF). January 20. Accessed January 24, 2017, http://www.faa.gov/data_research/aviation/taf/. Verleger, Philip (1972). “Models of the Demand for Air Transportation,” The Bell Journal of Economics and Management Science, 3(2): 437–457. Watterson, Andrew (2014). Demographic Data and Airline Route Planning [Southwest Airlines]. Presentation at the 93rd Annual Meeting of the Transportation Research Board, Washington, DC, 12–16 January.

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TRB’s Airport Cooperative Research Program (ACRP) Research Report: 194: Using Disaggregated Socioeconomic Data in Air Passenger Demand Studies explores the potential benefits of using disaggregated socioeconomic data, such as regional household income distributions and air passenger and travel survey data, for air passenger demand studies.

Aviation demand is strongly correlated to socioeconomic activity, and analysts typically use aggregate socioeconomic data, such as gross regional product or average regional household income, to better understand current and potential future aviation demand.

Since the United States is experiencing significant and ongoing demographic trends there is a question as to whether traditional methods and data sources will adequately capture these trends or would more detailed, disaggregated socioeconomic data, or even nontraditional data provide more accurate results.

This report summarizes long-term socioeconomic trends, attempts to understand their potential impact, and provides guidance for incorporating disaggregated socioeconomic data into air passenger demand studies.

The following appendices to ACRP Research Report 194 are available online:

Appendix A: Detailed Survey of Past Analyses of Air Passenger Demand

Appendix B: Airport Industry Use of Socioeconomic Data for Air Passenger Demand Studies

Appendix C: Additional Material on Sources of Disaggregated Socioeconomic Data

Appendix D: Detailed Case Study Analysis Results

Appendix E: Background on Other Analytic Approaches

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