This project aims to develop a toolkit enabling primary airports in the US to efficiently and precisely map scenario-based catchment area incorporating economic, spatial and operational factors.
An accurate estimation of airport catchment area enables airlines and airport operators to make informed decisions and to target potential passenger markets precisely. However, as all airports are unique, a one-size-fits-all approach does not apply to different airports to produce up-to-date and adaptive catchment area estimation. There lacks a consensus among industry practitioners and academic researchers on how airport catchment area should be defined. Airport operators are faced with limited options but have to rely on costly and time-consuming survey-based approach to estimate the boundary of their catchment areas, which is likely to lead to inconsistent, static, and often biased results.
This proposed project aims to develop an analytical approach that is generalizable to all primary airports in the US. Key deliverables of the project would be a toolkit to assist airport operators and authorities generate catchment area estimation effectively and efficiently. The toolkit will include data collection instrument, analytical tools, operational instructions, and report preparation template that can be readily adopted by airports of different sizes and resources. With this toolkit, primary airports across the nation will be able to make informed decisions to deploy marketing resources, estimate passenger/cargo demand, and plan for future demand growth.
At this stage, we anticipate the completion of the project involves the following steps:
- Survey/interview primary airport operators in the nation for a thorough understanding of how airports make use of catchment area estimation results in resource and operations planning.
- Synthesize the current practice of catchment area mapping process used by airports and consulting firms.
- Collect operational, spatial, and economic data that are to be used in the estimation of catchment area airports of different categories.
- Conduct case studies for sample airports representing different regions, enplanements, and hub categories.
- Build a standard toolkit with user friendly instructions to enable airport operators to self-analyze and self-map adaptive catchment area that considers different travel scenarios.
- Draft the final report and present the formal deliverables to the ACRP.
We anticipate the completion of the project requires at least 12 months with an approximate budget of $300,000 to support the following personnel and expenses:
PI: Total full-time commitment: 3 months; Salary: $35,000
Co-PI: Total full-time commitment: 3 months; Salary: $35,000
Two graduate research assistants, 9 months each: Salary: $40,000
Fringe Benefits: (29.64% for PI and Co-PI): Expense: $20,748
IT equipment, office supplies and consumables: Expense: $5,000
Trips to airports and to ACRP Expense: $10,000
Indirect cost: (Purdue Rate: 54%): Expense: $153,000
Estimated Total: $300,000
There isn't a standard estimation method to help operators of different airports in estimating catchment area. Several previous studies have used distinctive fixed radius to determine the boundary of catchment area, such as 75 miles (Martin Dresner, 1996; Morrison et al., 2001), 150 miles (Suzuki, Crum & Audino, 2004), 200 miles (Fu & Kim, 2016) 250 miles (Suzuki & Audino, 2003), or as far as 300 miles (Ryerson & Kim, 2018). Researchers have used different approaches to estimate airport catchment area, including interviewing drivers at airport parking facilities (Fuellhart, 2007), survey-based data collection and regression-based analysis (Loo, 2008, Lieshout, 2012), Geographical Information System (GIS) analysis (Suau-Sanchez et al., 2013) and analytical methods (Gao, 2020). To the best of our knowledge, there is no previously completed ACRP project addressing the estimation of airport catchment area, thus leaving a big gap to be fulfilled. Airport operators, therefore, cannot refer to any of the aforementioned studies for guidance but are left to rely on commercial consulting firms for expensive albeit non-adaptive solutions.
Dresner, M., Lin, J.-S. C., & Windle, R. (1996). The Impact of Low-Cost Carriers on Airport and Route Competition. Journal of Transport Economics and Policy, 30(3), 309–328. https://doi.org/10.2307/20053709
Fuellhart, K. (2007). Airport catchment and leakage in a multi-airport region: The case of Harrisburg International. Journal of Transport Geography, 15(4), 231–244. https://doi.org/10.1016/j.jtrangeo.2006.08.001
Gao, Y. (2020). Estimating the sensitivity of small airport catchments to competition from larger airports: A case study in Indiana. Journal of Transport Geography, 82, 102628. https://doi.org/10.1016/j.jtrangeo.2019.102628
Lieshout, R. (2012). Measuring the size of an airport's catchment area. Journal of Transport Geography, 25, 27–34. https://doi.org/10.1016/j.jtrangeo.2012.07.004
Loo, B. P. Y. (2008). Passengers' airport choice within multi-airport regions (MARs): some insights from a stated preference survey at Hong Kong International Airport. Journal of Transport Geography, 16(2), 117–125. https://doi.org/10.1016/j.jtrangeo.2007.05.003
Morrison, S. A. (2001). Actual , Adjacent , and Potential Competition : Estimating the Full Effect of Southwest Airlines. Transport Economics, 35(2), 239–256. Retrieved from https://www.jstor.org/stable/20053869
Ryerson, M. S., & Kim, A. M. (2018). A drive for better air service: How air service imbalances across neighboring regions integrate air and highway demands. Transportation Research Part A: Policy and Practice, 114(November 2017), 237–255. https://doi.org/10.1016/j.tra.2017.10.005
Suau-Sanchez, P., Burghouwt, G., & Pallares-Barbera, M. (2013). An appraisal of the CORINE land cover database in airport catchment area analysis using a GIS approach. Journal of Air Transport Management, 34, 12–16. https://doi.org/10.1016/j.jairtraman.2013.07.004
Suzuki, Y., Crum, M. R., & Audino, M. J. (2004). Airport leakage and airline pricing strategy in single-airport regions. Transportation Research Part E: Logistics and Transportation Review, 40(1), 19–37. https://doi.org/10.1016/S1366-5545(03)00055-3