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Toolkit for Establishing Airport Catchment Areas (2023)

Chapter: Appendix A. Literature Review

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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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Suggested Citation:"Appendix A. Literature Review." National Academies of Sciences, Engineering, and Medicine. 2023. Toolkit for Establishing Airport Catchment Areas. Washington, DC: The National Academies Press. doi: 10.17226/27424.
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91 Appendix A. Literature Review

92 Summary of Literature Review In this literature review, we investigate academic studies published after the 1990s that either define airport catchment areas or use the concept of airport catchment areas. The results of these two types of literature are presented separately in this review (See Category I and Category II, respectively). A total number of 18 studies are reviewed for their contributions to defining or measuring airport catchment areas. These studies date from 1997 to 2021, covering airports of the United States, Europe, and Australia. These studies use two groups of methods to establish the boundary of airport catchment areas: analytical methods and passenger-oriented methods. For analytical studies, utilities built upon shortest distance, lowest cost, or mixed criteria are guiding the determination of airport catchment areas. And for studies adopting passenger-oriented methods, inputs are sourced from passengers, either in the form of stated preference data (surveys) or revealed preference data (market shares, social media geotags, or license plates of vehicles), to sketch areas where passengers originate from. US-focused studies choose counties, zip codes, or census tracts as the basic geographic unit in defining airport catchment areas. No studies use census block, a more delicate unit, mainly due to the computational cost. And for studies addressing European airports, the selection of geographic units is more diverse, depending on the data used. The reviewed studies do not have a consensus on the size or shape of the catchment area, which is dependent on the subject airports being analyzed. For studies utilizing the concept of airport catchment areas, they rely on the defined geographic boundary to delimit the scope of analysis. Among these studies, the airport catchment area has changed from the research subject in studies of Category I to one of the input conditions for further analysis. Interestingly, most of these studies disregard the research progress about airport catchment areas but choose a simplified approach, fixed-radius circles around airports, to define the limit of catchment areas. Several studies address the traffic leakage and competition between airports, where the concept of catchment areas and the extent of leaking traffic to competing airports are mutually dependent on each other. In summary, though existing literature has demonstrated relatively diverse methods to measure and use airport catchment areas, most of these studies use only a single approach. Usually, they do not provide cross-checks for the reliability of defined catchment areas. Few studies have considered digital revealed preference data, such as mobile data or User-Generated Content (UGC), to supplement traditional data sources, thus leaving such rich information idle.

93 Category I – Studies That Define Airport Catchment Areas

94 [Huber et al., 2021] Title: Modelling airport catchment areas to anticipate the spread of infectious diseases across land and air travel Author: Carmen Huber; Alexander Watts, Ardath Grills, Jean Hai Ein Yong, Stephanie Morrison, Sarah Bowden, Ashleigh Tuite, Bradley Nelson, Martin Cetron, Kamran Khan Journal: Spatial and Spatio-temporal Epidemiology Year: 2021 DOI/URL: https://doi.org/10.1016/j.sste.2020.100380 Research Topics: Prediction of the spread of infectious diseases through air travel Subject Airport: Miami International (MIA) & Harrisburg International (MDT) Competition: Two subject airports are viewed as located in Single-Airport Regions (SAR) Airport Category: MIA – Large Primary Hub / International Gateway MDT – Small Primary Hub Input Data: Total outbound international passenger volume at focused airports –International Air Transport Association (IATA) Distance between census tracts and focused airports – GIS applications Methodology: Huber et al. (2021) perform case studies on two US international airports and estimate their catchment areas based on the probability that passengers living in each census tract within the United States would use any airport within the United States for international flights. When applying the Huff model to estimate catchment areas of selected airports, Huber et al. (2021) take into account the Euclidean distance to airports, distance decay exponent, airport attractiveness, and airport attractiveness exponent. Two exponents are used to reveal how attractiveness and distance affect the airport choices of passengers, which indirectly shape the catchment areas of airports. Furthermore, in identifying the catchment area of MDT, Huber et al. (2021) transform the results to zip-code level and compare findings with an earlier study analyzing the same airport. Catchment areas size: Varying Key Findings: In this analysis, Huber et al. (2021) use the Huff model, a probabilistic choice behavior model, to examine the catchment areas of two selected airports. The results imply a nonlinearly negative relationship between the catchment area size and the access distance to the airport. Also, Huber et al. (2021) suggest that the Huff model is one of the most efficient tools for estimating catchment areas at any level of the geographic unit within a reasonable spatial scale if the human mobility information is lacking.

95 [Teixeira & Derudder, 2021] Title: Spatio-temporal dynamics in airport catchment areas: The case of the New York Multi Airport Region Author: Filipe Marques Teixeira, Ben Derudder Journal: Journal of Transport Geography Year: 2021 DOI/URL: https://doi.org/10.1016/j.jtrangeo.2020.102916 Research Topics: Catchment areas of airports in the Greater New York area Subject Airport: New York John F. Kennedy International Airport (JFK), LaGuardia Airport (LGA), Newark Liberty International Airport (EWR), Westchester County Airport (HPN), and Long Island MacArthur Airport (ISP) Competition: Multiple Airport Region (MAR) Hub Category: Primary: Large Hubs (JFK, EWR, LGA) & Small Hubs (ISP, HPN) Input Data: Airfare, connectivity characteristics, on-time performance – Bureau of Transport Statistics (BTS) Distances from census tracts to airports – HERE Maps dataset Methodology: Teixeira and Derudder (2021) take airfares, connectivity, and on-time performance into consideration and calculate the utility score for each airport in terms of these three variables. This study also applies the Huff model (probabilistic choice behavior model) to evaluate the attractiveness of each focused airport toward different census tracts. The study selects several temporal periods and estimates the probabilities that passengers within each census tract choose different airports as the origin airport during these periods. The estimated probabilities are then used to visualize variations of airport catchment areas in the New York region. Catchment areas size: Varying, based on airfare, connectivity, on-time performance, or the aggregated utilities. Key Findings: Teixeira and Derudder (2021) map the airport catchment areas of the Greater New York area by considering the variations of several elements in both temporal and spatial dimensions. The primary analytical model of this study is the Huff model, a probabilistic choice behavior model. Their model suggests that the airport catchment areas have a nonlinear and adverse relationship with the distance to airports. In this study, the catchment area of an airport would vary based on different elements of airport utility (fare, connectivity characteristics, on-time performance) and based on the temporal factors (different times of the day, days of the week, and quarters of the year).

96 [Yirgu et al., 2021] Title: Long-distance airport substitution and air market leakage: Empirical investigations in the US Midwest Author: Kaleab Woldeyohannes Yirgu, Amy M. Kim, and Megan S. Ryerson Journal: Transportation Research Record Year: 2021 DOI/URL: https://doi.org/10.1177%2F03611981211010797 Research Topics: Airport choice and traffic leakage Subject Airport: 27 primary airports in US Midwest Competition: MAR + Multiple SARs Hub Category: Primary – Large, Medium, and Small Hubs and Non-hubs Input Data: Ticket purchase date, Origin-Destination (O-D) information, route, zip codes of credit cards billing address used for booking – Airlines Reporting Corporation (ARC) Market Locator dataset Airfare, flight capacity, market mile, nonstop miles – Bureau of Transportation Statistics (BTS) Enplanement Data – Federal Aviation Administration (FAA) Distance from different ZIP codes to selected airports – ArcGIS Methodology: This study uses a two-step approach to investigate traffic leakage from local airports to substitute large hub airports in the US Midwest. In Step 1, the authors use a simple shortest distance approach to determine the airport catchment area at the zip code level. In Step 2, they use econometric models to estimate the traffic leakage to substitute airports with actual ticketing datasets. Catchment areas size: Varying, based on the location of each zip code area Key Findings: The primary intention of this study is not to establish airport catchment areas but to estimate traffic leakage from local airports to substitute large hub airports. Therefore, the approach used by this study to define airport catchment areas is rudimentary. In this study, the definition of “service area” is established in which each airport is assigned a local market consisting of a group of the ZIP codes closest to it.

97 [Gao, 2020] Title: Estimating the sensitivity of small airport catchments to competition from larger airports: A case study in Indiana Author: Yi Gao Journal: Journal of Transport Geography Year: 2020 DOI/URL: https://doi.org/10.1016/j.jtrangeo.2019.102628 Research Topics: Competition among three US primary hub airports Subject Airport: Indianapolis international airport (IND), Chicago O’Hare international airport (ORD), and Chicago Midway international airport (MDW) Competition: The subject airport, IND, is in a SAR, facing competition from nearby airports (ORD & MDW). Hub Category: Primary: Medium Hub Input Data: Standard mileage rate – US Internal Revenue Service (IRS) Value of travel time saving – Department of Transportation (DOT) Driving distance and driving time of the fastest path – ArcGIS Daily economy parking rate – Airport websites Lowest airfares, itinerary duration, enplanements of flights departing from IND, ORD, and MDW in 2018 — Department of Transportation DB1B On-time performance – Bureau of Transportation Statistics (BTS) Methodology: This study considers several variables and calculates the overall costs for flying from the origin airport to destinations. These costs play different roles concerning passengers with distinct travel purposes. Gao (2020b) considers several scenarios and conducts a sensitivity analysis to address how the catchment area of IND would vary under different scenarios. Catchment areas size: Varying, based on the traveling purposes and group size of passengers Key Findings: Gao (2020b) considers the impact of a few factors on the size of the airport catchment area and proposes different traveling scenarios accordingly. The findings reveal that the catchment area is not static but can vary with the travel plans of passengers and the size of the travel group. This study provides an easy- to-implement template for airports to establish their catchment areas.

98 [Baltazar & Silva, 2018] Title: Airport’s catchment area size definition: A Portuguese case study Author: Maria E. Baltazar, Jorge Silva Conference: Air Transport Research Society 23rd World Conference (2018) Year: 2018 DOI/URL: http://hdl.handle.net/10400.6/9639 Research Topics: Airport catchment area size of Portuguese airports Subject Airport: LIS – Lisbon International Airport, OPO – Oporto International Airport (OPO), and FAO – Faro International Airport Competition: Multiple SARs Hub Category: Airline hubs + International gateways Input Data: Multiple socioeconomic indicators, including company density, educational level, population density, employment level, economically active population, business volume, public health, tourist attractions, hotel establishments, accommodation capacity, and occupation rate, from the Instituto Nacional de Estatística (INE) Methodology: To identify the catchment areas of three selected airports, Baltazar and Silva (2018) survey import and export companies with the highest business volumes located within the hinterland of every airport, aiming to understand their approach to air transportation and their choices of airports. Catchment areas size: 30-, 60-, 90-, and 120-min driving distance for primary catchment areas Key Findings: Baltazar and Silva (2018) use the findings derived from surveys to show the airport choices of business stakeholder, and the survey results reveal the boundary of catchment areas. This approach uses the combination of stated and indicated preference data. In addition, Baltazar and Silva (2018) suggest that exclusively using travel time or distances to estimate an airport’s catchment area could present misleading results, thus calling for further studies.

99 [Zhou et al., 2018] Title: Investigating the impact of catchment areas of airports on estimating air travel demand: A case study of regional Western Australia Author: Heng Zhou, Jianhong (Cecilia) Xia, Qingzhou Luo, Gabi Nikolova, Jie Sun, Brett Hughes, Keone Kelobonye, Hui Wang, Torbjorn Falkmer Journal: Journal of Air Transport Management Year: 2018 Doi: https://doi.org/10.1016/j.jairtraman.2018.05.001 Research Topics: Air travel demand estimation Subject Airport: Regular public transport (RPT) airports in Western Australia (WA) Competition: Multiple SARs Hub Category: Regional hubs Input Data: Real-time flights information between regional RPT airports in WA – Flightradar24.com Population and per capita income – Australian Bureau of Statistics (ABS) The number of operating mine sites – Department of Mines and Petroleum Tourism traffic to regions of WA – Tourism Research Australia Airfares – Airlines’ websites Driving distance – ArcGIS Methodology: Two methods are used in mapping the catchment areas of multiple airports concurrently in Western Australia. The first method is based on a 2.5-hour fixed driving distance, which renders fixed-radius circles around airports. The second approach is to utilize the Thiessen polygon, which will find the nearest airport for any point on a map. Catchment area size: • 2.5-hour driving distance (Fixed distance-based) • Euclidean distance (Thiessen-polygon approach) Key Findings: Zhou et al. (2018) map catchment areas of regular public transport airports in Western Australia using fixed driving distance and the Thiessen-polygon approach. Their results show that using Euclidean distance and fixed driving distance would generate significantly different results in demand estimation. In terms of coverage, catchment areas formed by Thiessen-polygons can cover the entire Western Australia region. In contrast, the catchment areas based on fixed-radium circles only cover 32 percent of the Western Australia. The results of this study illustrate that distance between airports, airfare of the flight, population of the origin airport's catchment area and the number of operating mine sites of the destination airport's catchment area are significantly correlated with domestic air travel seat capacity provided.

100 [Heilman, 2017] Title: Spatial competition in airport markets: An application of the Huff model Author: Joel Heilman Publication type: Master’s thesis Year: 2017 DOI/URL: https://scholarworks.uni.edu/etd/465 Research Topics: Airport competition Subject Airport: Des Moines International Airport (DSM), The Eastern Iowa Airport (CID), Dubuque Regional Airport (DBQ), Waterloo Regional Airport (ALO), Mason City Municipal Airport (MCW), Fort Dodge Regional Airport (FOD), Southeast Iowa Regional Airport (BRL), Minneapolis−Saint Paul International Airport (MSP), and Quad City International Airport (MLI) Competition: Multiple SARs Hub Category: Primary – Large hubs, small hubs, non-hubs, and Nonprimary – Commercial Service airports Input Data: Driving time – ArcGIS Enplanements – Iowa Department of Transportation Methodology: Heilman (2017) uses the Huff model (probabilistic choice behavior model) to estimate the catchment areas of airports, where driving time and passenger volume of each airport are used to calibrate the model. Also, Heilman (2017) conducts a license plate survey on passengers to identify counties that travelers come from. Catchment areas size: 10-, 20-, 30-, 45-, 60-, 90-, 120-, 180-, 240-, and 300-min driving distance Key Findings: Huff model is primarily used in this study to estimate the catchment areas of airports in Iowa. Meanwhile, Heilman (2017) considers the license plates information collected from surveys to calibrate the model and approximate airport catchment areas in Iowa.

101 [Paliska et al., 2016] Title: Passengers' airport choice and airports' catchment area analysis in cross-border Upper Adriatic multi-airport region Author: Dejan Paliska, Samo Drobne, Giuseppe Borruso, Massimo Gardina, Dasa Fabjan Journal: Journal of Air Transport Management Year: 2016 DOI/URL: http://dx.doi.org/10.1016/j.jairtraman.2016.07.011 Research Topics: Airport catchment areas in cross-border regions Subject Airport: Ljubljana Joze Pucnik Airport (LJU), Venice Marco Polo Airport (VCE), and Trieste Pietro Savorgnan di Brazza Airport (TRS) Competition: MAR Hub Category: Regional hubs Input Data: Spatial data – GIS software Purposes of traveling, the closest airport, easy access, flight times, frequent service, ticket prices, and parking prices – Passenger surveys Methodology: Paliska et al. (2016) take a three-step approach. The first part is to graphically plot the airports' catchment areas in terms of access time using the GIS technique. Then, the study surveys residents living within up to 3-hour driving distance to the nearest airport in the cross-border regions to identify passengers' airport choices. Moreover, they record the zip codes of passengers in the survey to study the impact of geo-demographic characteristics of catchment areas. The final analytical method of this study includes the multinomial logit model and mixed logit model, which are used to explain the airport choice behavior of passengers living in the cross-border region. Catchment areas size: 30-, 60-, 90-, and 120-min driving distances Key Findings: Paliska et al. (2016) explore the catchment areas of airports in the cross-border Upper Adriatic MAR. Key findings can be summarized as: • The three subject airports have relatively small core catchment areas, and the market share of each airport rapidly decreases with the increasing access time to the airport. • The access time to the airport is the most critical determinant in airport choice for all segments (business/leisure and cross-border/domestic). The sensitivity to access time is more pronounced for business and domestic groups. • The results indicated pronounced loyalty to domestic airports and generally low importance of ticket prices. • In market conditions where all airports in the region have a low-cost carrier (LCC), the effect of LCC on airport choice is limited. • Borders might influence airport choice. In the cross-border regions, the passengers’ airport choice process is more complex than in the non-cross-border areas.

102 [Feng, 2015] Title: An exploration in airport market share and accessibility with Twitter Author: Muzi Feng Dissertation: Master’s thesis Year: 2015 DOI/URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1440366363 Subject Airport: 392 primary airports in the US Competition: Mixed Hub Category: Primary – large, medium, and small hub & nonhub airports Input Data: Real-time geotagged tweets – Collected from Twitter Developer Service US County map – US Census Bureau Methodology: Feng (2015) uses geotagged tweets as the data source to explore airport market share and airport catchment division at the county level. The map of catchment areas of focused airports is generated using ArcGIS. Moreover, a series of smaller buffer distances centered at airports are used to examine the difference of catchment areas defined by geotagged tweets. Catchment areas size: This study uses 400-km as the limit for airport catchment areas and uses various other radii to analyze different catchment scenarios. Key Findings: Feng (2015) demonstrates the possibility and procedure of utilizing geotagged social media data to infer airport catchment areas. Through using real-world tweets with geotags and certain assumptions to filter out irrelevant noise from the data, this study successfully establishes catchment areas where airport users reside at the county level. The study also demonstrates how to estimate market shares of different airports in a MAR. As put by the author, this study shows the potential of UGC data in social studies and provides an alternative way to conduct airport market analysis.

103 [Augustyniak & Olipra, 2014] Title: The potential catchment area of Polish regional airports Author: Wojciech Augustyniak, Lukasz Olipra Journal: Journal of International Studies Year: 2014 DOI/URL: http://dx.doi.org/10.14254/2071-8330.2014/7-3/13 Research Topics: Airport catchment areas Subject Airport: Katowice Wojciech Korfanty Airport (KTW) Competition: MAR Hub Category: Major airport/International (Poland) Input Data: Catchment areas map – WBData (Open Map data) Methodology: Augustyniak and Olipra (2014) utilize the Google Maps road distance tool with an interval of 10 km and a travel time of two hours from each airport to establish potential airport catchment areas for Polish regional airports. Based on the mapped catchment areas, they evaluate the relationship between the catchment areas and population density. Catchment areas size: 1-hour and 2-hour driving distance Key Findings: Augustyniak and Olipra (2014) use the threshold of isochrones of 1-hour and 2- hour driving time to determine the catchment areas. They identify the competitiveness distribution of the air transport market in Poland by evaluating the regional population density within the defined catchment areas of Polish airports.

104 [Suau-Sanchez et al., 2014] Title: An appraisal of the CORINE land cover database in airport catchment area analysis using a GIS approach Author: Pere Suau-Sanchez, Guillaume Burghouwt, Montserrat Pallares-Barbera Journal: Journal of Air Transport Management Year: 2014 DOI/URL: https://doi.org/10.1016/j.jairtraman.2013.07.004 Research Topic: Airport catchment areas Subject Airport: Top 20 airports in Europe Competition: Mixed (SAR & MAR) Hub Category: International gateways and major hubs Input Data: Population density disaggregated with the CORINE land-cover 2000 – European Environmental Agency (EEA) Flight seat capacities – Official Aviation Guide (OAG) Methodology In this study, Suau-Sanchez et al. (2014) use three fixed radii as boundaries to determine the size of the population within airport catchment areas. Catchment areas size: 25, 50, and 100-km fixed radii Key Findings: Suau-Sanchez et al. (2014) demonstrate the usage of both the CORINE dataset and GIS techniques to achieve consistent measurement of the population within defined airport catchment areas.

105 [Peng, 2013] Title: Evaluating the Integrated Accessibility and Catchment Areas of US Airports Author: Keijing Peng Dissertation: Master’s thesis Year: 2013 DOI/URL: https://etd.ohiolink.edu/apexprod/rws_etd/send_file/send?accession=osu1366381 839&disposition=inline Research Topics: Assessment of land and air accessibility Subject Airport: Primary hub airports in the United States Competition: Hybrid (SARs + MARs) Hub Category: Primary – Large, Medium, and Small Hubs Input Data: Level of service of O-D pairs, travel time, airfare, flight schedule – Bureau of Transportation Statistics (BTS) Shortest path – Self-calculated based on travel time Population – American FactFinder Methodology: Peng (2013) divides passengers into three groups, which are captive, business, and leisure passengers, and explores the effects of land and air travel costs on their choices of origin airports. Peng (2013) identifies the core catchment area by comparing the total costs of originating from different airports to the same destination as well as the market shares of origin airports under the same condition. The results of airport catchment areas are presented using ArcGIS. Catchment areas size: Varying, depending on airport market shares and overall travel costs. Key Findings: Peng (2013) generates the catchment area and time accessibility maps at the national and regional scales. At the national scale, results suggest the inequality in accessibility among different locations in the air system. In addition, the study identifies factors that shape the accessibility and influence the airport competitions, including multiple airports in a metro area, highway infrastructure, ticket price at fortress hubs, and low level of service in remote airports.

106 [Lieshout, 2012] Title: Measuring the size of an airport’s catchment area Author: Rogier Lieshout Journal: Journal of Transport Geography Year: 2012 DOI/URL: https://doi.org/10.1016/j.jtrangeo.2012.07.004 Research Topics: Airport catchment area Subject Airport: Amsterdam Airport Schiphol (AMS) Competition: SAR with multiple competing airports Hub Category: Central airline hub + International gateway Input Data: Airport market shares (calculated using the Gravity model) • Passenger numbers – Marketing Information Data Tapes (MIDT) • Seat capacities – Official Airline Guide (OAG) • Distance – Calculation using the longitude and latitude Access costs – Online route planner using web scraping technology Airfares − MIDT Flight frequency – OAG Access time costs – Route planning websites Airside time costs (circuitous & transfer flights) – Airline schedules Methodology: Lieshout (2012) defines the catchment area by the market share of an airport in its hinterland regions. Using a Multinomial Logit (MNL) model, this study measures the market share of AMS in providing air services to different destinations. The resulting airport catchment areas achieved in this study are destination dependent and travel motives dependent. Catchment areas size: Distance varying, depending on competing airports, travel motives, and destinations. Key Findings: This is an influential study among the literature addressing airport catchment areas. Lieshout (2012) presents a novel methodology to assess the size of airport catchment areas and the airport’s market shares using the MNL passenger choice model. The study finds that the size of the airport catchment area is destination- based rather than fixed. And the change of air services from nearby competing airports will cause the airport catchment area to vary.

107 [Lian & Rønnevik, 2011] Title: Airport competition – Regional airports losing ground to main airports Author: Jon Inge Lian, Joachim Rønnevik Journal: Journal of Transport Geography Year: 2011 Doi: https://doi.org/10.1016/j.jtrangeo.2009.12.004 Research Topics: Air traffic leakage and airport competition Subject Airport: Alesund Airport (AES), Trondheim Airport (TRD), Harstad-Narvik Airport (EVE) and regional airports in Norway Competition: Multiple SARs Hub Category: Mixed: International gateway + regional hub Input Data: Data on the mode of access, flight number, disembarkation airport, final airport, place of residence and place of visit, trip duration, traveling party, the purpose of travel, airfare, age, sex, etc. from the 2003 and 2007 –Collected from Norwegian air travel surveys Methodology: In this study, Lian and Rønnevik (2011) use the logistic regression model (probabilistic choice behavior model) to estimate how passengers choose origin airports between main and regional airports. They analyze several factors that may affect passengers’ choice of origin airports, including travel time to airports, travel time differences to different airports (local regional and remote main airports), fare differences, and level of air services. Airport catchment areas in this study are established using survey responses. Catchment areas size: Varying with airfares, level of services, trip purposes, and geographic distance Key Findings: Lian and Rønnevik (2011) find that travelers are willing to spend extra hours driving to a larger airport in order to take advantage of lower fares and more convenient airline services. Traffic leakage from regional airports is high when the service from the regional airport is indirect and fare differences are significant. Traffic leakage is particularly evident in the leisure segment. Leakage levels tend to increase as competition is intensified at leading airports, but the evidence is somewhat mixed.

108 [Marcucci & Gatta, 2011] Title: Regional airport choice: Consumer behavior and policy implications Author: Edoardo Marcucci, Valerio Gatta Journal: Journal of Transport Geography Year: 2011 DOI/URL: https://doi.org/10.1016/j.jtrangeo.2009.10.001 Research Topics: Regional airport choices of passengers Subject Airport: Ancona Airport (AOI), Rimini Airport (RMI), Forlì Airport (FRL), and Bologna Guglielmo Marconi Airport (BLQ) Competition: MAR in central Italy Hub Category: Regional airports Input Data: Demographic information, bookings, trip purpose, type of carriers in selected airports, connectivity, waiting time, type of parking, flight frequency, main destinations, advanced booking, access time, and access cost – Collected from passenger surveys Methodology: Marcucci and Gatta (2011) use the stated preference data to study the origin airport choice problem and evaluate the effects of possible policy interventions. They perform a detailed segmentation of the sample studied. The study finds that socioeconomic variables are statistically relevant when interacted with the attributes used to characterize airport choice. Catchment areas size: Start with a 2-hour fixed driving time approach and then collect the zip codes of passengers to refine the definition. Key Findings: In this analysis aiming to address the origin airport choice problem, Marcucci and Gatta (2011) use a two-step approach to infer airport catchment areas for airports located in this central Italian MAR. They first use a 2-hour driving time to get the initial boundary of airport catchment areas. They then further refine the catchment area using the surveys on passengers in airports by asking the zip code of their departure locations.

109 [Fuellhart, 2007] Title: Airport catchment and leakage in a multi-airport region: The case of Harrisburg International Author: Kurt Fuellhart Journal: Journal of Transport Geography Year: 2007 DOI/URL: https://doi.org/10.1016/j.jtrangeo.2006.08.001 Research Topics: Airport competition and traffic leakage Subject Airport: Harrisburg International Airport (MDT) Competition: MAR in south-central Pennsylvania Hub Category: Primary – Small hub Input Data: Home zip code of passengers – Collected from airport parking facilities Methodology: This study uses zip codes collected from passengers who drive to the Harrisburg International airport (MDT) and park their vehicles at the airport parking facilities to establish airport catchment areas. Catchment areas size: Start with a 75-mile radius circle as the “core” market area of the airport, then use passengers’ home zip codes to refine it further. Key Findings: Fuellhart (2007) follows a simple but effective approach to estimates the catchment area of the Harrisburg International airport (MDT). Fuellhart first starts from a fixed-radius circle catchment area, then uses passengers’ home zip codes collected from airport parking facilities to further refine the boundary of airport catchment areas.

110 [Pantazis & Liefner, 2006] Title: The impact of low-cost carriers on catchment areas of established international airports: The case of Hanover Airport, Germany Author: Nadine Pantazis, Ingo Liefner Journal: Journal of Transport Geography Year: 2006 DOI/URL: https://doi.org/10.1016/j.jtrangeo.2005.05.002 Research Topics: The impacts of low-cost carrier on catchment areas Subject Airport: Hanover International Airport (HAJ) Competition: SAR Hub Category: Major airport in Germany Input Data: Zip codes of passengers of Hapag Lloyd Express – Collected from customer surveys Methodology: Pantazis and Liefner (2006) use the Hanover Airport as the focused airport and examines the impacts of LCCs on the catchment area of the selected airport. By collecting the home zip codes of passengers, this study defines the main catchment area of the Hanover Airport as areas where most of the passengers reside. Catchment areas size: Zip code based, around HAJ airport Key Findings: Pantazis and Liefner (2006) perform zip code based surveys to identify the origins of passengers and then estimate the catchment areas based on the results collected from surveys. The study finds that LCCs can help airports to enlarge their catchment areas and strengthen their competitive position in national aviation markets.

111 [Hsu & Wu ,1997] Title: The market size of a city-pair route at an airport Author: Chaug-ing Hsu, Yai-hui Wu Journal: The Annals of Regional Science Year: 1997 DOI/URL: https://link.springer.com/content/pdf/10.1007/s001680050055.pdf Research Topics: Airport market size Subject Airport: N/A (Hypothetical data) Competition: N/A Hub Category: N/A Input Data: Passenger delay cost, airport access cost, and operating cost of airlines – Referred to previous studies (Caves et al. 1984; Teodorovic 1988; Kane 1990; Kling et al. 1991). Methodology: Hsu and Wu (1997) use a nonlinear programming model to determine the optimal number of passengers and the local service area of a city-pair market. In building the programming function, Hsu and Wu (1997) take into account three types of costs and use the coefficients estimated by previous studies. Then, they conduct a sensitivity analysis to identify the optimal market area, where results vary with different scenarios. Catchment areas size: The radius of the catchment area varies with the flight distance. Key Findings: In this study, Hsu and Wu (1997) estimate the size of the market for a city-pair route at an airport from both the demand and supply sides of air transportation. Though this study is different from the rest of the literature about airport catchment areas, it leads to an interesting finding that the service area of city-pair flights (equivalent to the catchment area concept) would depend on the route lengths.

112 Category II – Studies That Use Airport Catchment Areas

113 [Bergantino et al., 2020] Title: Modelling regional accessibility to airports using discrete choice models: An application to a system of regional airports Author: Angela Stefani Bergantino, Mauro Capurso, Stephane Hess Journal: Transportation Research Part A: Policy and Practice Year: 2020 DOI/URL: https://doi.org/10.1016/j.tra.2019.12.012 Subject Airport: Bari International Airport (BRI) Brindisi International Airport (BDS) Foggia “Gino Lisa” Airport (FOG) Grottaglie “Marcello Arlotta” Airport (TAR) (Note: Both FOG and TAR are not currently used for commercial services) Competition: MAR (Apulia, Italy) Hub Category: National Interest and Regional (Italian classification) Input Data: Stated preference data on passengers’ preferred access mode to airports – Collected from a paper-based survey Travel time, travel cost, and headway time (i.e., the time between two consecutive public transport services) – Collected from operators’ websites and www.viamichelin.com How does this study use the concept of catchment area? In this study, Bergantino et al. (2020) explore regional accessibility to Italian airports located in Apulia, Italy using discrete choice models. The concept of airport catchment is used to delimit the scope of data collection so that potential users of the airports can be located. Catchment areas size: N/A (The paper does not mention) Key Findings: Bergantino et al. (2020) use both stated and revealed preference data to analyze residents' decisions regarding airport access mode in Apulia, Italy. The study finds that the political pressures for opening and maintaining “local” airports are more substantial when accessibility toward main airports is poor.

114 [Suau-Sanchez & Voltes-Dort, 2019] Title: Drivers of airport scheduled traffic in European winter tourism areas: Infrastructure, accessibility, competition and catchment area Author: Pere Suau-Sanchez, Augusto Voltes-Dort Journal: Journal of Air Transport Management Year: 2019 DOI/URL: https://doi.org/10.1016/j.jairtraman.2019.101723 Research Topics: Airport traffic drivers in European winter tourism areas Subject Airport: 104 Airports serving ski resorts in Europe Competition: Airports are treated as from multiple SARs. Airport Category: Tourism destinations (airports located near ski resorts) Input Data: • Geographical location, traffic data for the period 2009–2019 (aircraft movements), and infrastructure (number of runways and runway length) – Collected from Official Airline Guide (OAG) and airports’ AIPs. • Location and characteristics of ski resorts in Europe, such as km of slopes per level of difficulty (“black” slopes are the most advanced), the resorts' elevation difference (i.e., “steepness”), and their user rating – Collected from www.skiresort.info • Population (2018) – ESRI's demographic databases provided with ArcGIS. How does this study use the concept of catchment area? In this study, Suau-Sanchez and Voltes-Dort (2019) define the catchment areas of airports serving ski resorts by fixed driving time (either one or two hours). The catchment area concept is used to measure the air travel demands from residents, as compared to seasonal travel demands for ski resorts. Catchment areas size: 1-hour and 2-hour driving distance Key Findings: Suau-Sanchez and Voltes-Dort (2019) focus on a specific type of airports that are located near ski resorts. The results of the analysis show that catchment area, competition, and infrastructure outweigh accessibility to ski resorts as the main drivers of scheduled traffic at small mountain airports.

115 [Wei & Grubesic, 2018] Title: The dehubbing Cincinnati/Northern Kentucky International Airport (CVG): A spatiotemporal panorama Author: Fangwu Wei, Tony H. Grubesic Journal: Journal of Transport Geography Year: 2018 Doi: https://doi.org/10.1016/j.jtrangeo.2015.10.015 Research Topics: Airport dehubbing process Subject Airport: Cincinnati/Northern Kentucky International Airport (CVG) and nearby airports, including DAY, IND, CMH, SDF, and LEX Competition: SAR Hub Category: Primary – Medium hub Input Data: Street network data – ESRI Market and Ticket databases (DB1B) – Bureau of Transportation Statistics (BTS) Commercial airport data – National Transportation Atlas (NTA) How does this study use the concept of catchment area? In this study, Wei and Grubesic (2015) use the fixed radius approach to define airport catchment areas. They generate 90-minute drive time catchment areas using the street database. The concept of the airport catchment area is used in this study to extract airport market data, including information pertaining to basic population patterns. Catchment areas size: 90-minute driving distance Key Findings: Wei and Grubesic (2015) aim to explore the spatiotemporal dynamics of CVG's dehubbing process. Results suggest that a combination of commercial carrier strategies, operational efficiencies, hub structures, network topologies, and regional competition contributed to the service deterioration of CVG.

116 [Ryerson & Kim, 2018] Title: A drive for better air service: How air service imbalances across neighboring regions integrate air and highway demands Author: Megan S. Ryerson; Amy M. Kim Journal: Transportation Research Part A: Policy and Practice Year: 2018 Doi: https://doi.org/10.1016/j.tra.2017.10.005 Research Topics: Air traffic leakage from regional airports to large substitution hubs Subject Airport: Twelve airport pairs and four substitute airports, which are: • Atlanta international airport (ATL) o Chattanooga Metropolitan (CHA) o Huntsville (HSV) o Birmingham (BHM) o Savannah (SAV) o Knoxville (TYS) • Charlotte international airport (CLT) o Columbia Metropolitan (CAE) o Charleston (CHS) o Greensboro (GSO) • Dallas/Fort Worth international airport (DFW) o Oklahoma City (OKC) o Shreveport Regional (SHV) • Phoenix international airport (PHX) o Tucson Airport (TUS) Competition: Airports are analyzed in pairs, thus can be viewed as multiple MARs Hub Category: 4 large Hub airports (substitution airports) + 11 regional airports (local) Input Data: Flight operations data from DB1B and T100 – Bureau of Transportation Statistics (BTS) Ground access distance – Not mentioned (using the driving distance) Ground access time – Self-calculated using assumed speeds How does this study use the concept of catchment area? In this study by Ryerson and Kim (2018), airport catchment areas and traffic leakage from local airports to remote substitution airports are mutually dependent on each other. The catchment area of an airport must be defined first before it can be determined how much local traffic is leaked into substitution airports. Catchment areas size: N/A (The paper does not explicitly mention this.) Key Findings: Ryerson and Kim (2018) aim to estimate the magnitude of air traveler leakage at small and medium airports across the US. The study estimates that travelers living in small and mid-sized metropolitan regions have the incentive to “leak” from their airport to a distant, better-served airport. In addition, they find that the potential leaked passengers contribute 1–2.75% of average daily highway traffic at heavily congested portions of the interstate highways connecting airports and up to 10–12% of traffic on low-density parts of the highway.

117 [Kim & Ryerson, 2018] Title: A long drive: Interregional airport passenger “leakage” in the US Author: Amy M. Kim; Megan S. Ryerson Journal: Tourism Management Year: 2018 Doi: https://doi.org/10.1016/j.tourman.2017.10.012 Research Topics: Interregional airport passenger leakage Subject Airport: N/A Competition: N/A Hub Category: N/A Input Data: US airport passenger leakage literature How does this study use the concept of catchment area? This review completed by Kim and Ryerson (2018) examines the US airport passenger leakage literature, focusing specifically on leakage from small and medium airports to large airports using personal vehicles. In this review, the concept of the catchment area is the research topic addressed by the reviewed literature. Catchment areas size: N/A Key Findings: This review emphasizes the need for ongoing data collection to support advanced methodological applications and development. Kim and Ryerson (2018) also observe a need for more attention to integrated multimodal, interregional planning – specifically, understanding the air and ground connectivity of the interregional transportation system.

118 [Leung et al., 2017] Title: Why passengers’ geo-demographic characteristics matter to airport marketing Author: Abraham Leung, Barbara T.H. Yen, Gui Lohmann Dissertation: Journal of Travel & Tourism Marketing Year: 2017 DOI/URL: https://doi.org/10.1080/10548408.2016.1250698 Research Topic: Geo-demographic classification analysis Subject Airport: Gold Coast Airport (OOL) Competition: SAR Airport Category: Major airport (Australia) Input Data: Trip-related data – Collected from passenger surveys Geo-demographic data – Australian Bureau of Statistics (ABS) How does this study use the concept of catchment area? This study uses the threshold of 50 km land access distance (approximately 1-hour access time) to define the boundary of the airport catchment area. Leung et al. (2017) then use this catchment area to delimit the scope of data collection. Catchment areas size: 50 km land access distance (equivalent to 1-hour access time) Key Findings: Leung et al. (2017) find distinctive contrasts in passenger origin location for short- haul domestic trips and long-haul international trips, in which passengers from afar are willing to travel long distances to reach a second-tier airport to make use of cheaper airfares. The study suggests that low-cost carriers should better target their customers by offering geographically targeted marketing.

119 [Yang et al., 2016] Title: Airport location in multiple airport regions (MARs): The role of land and airside accessibility Author: Zhongzhen Yang; Shunan Yu; Theo Notteboom Journal: Journal of Transport Geography Year: 2016 DOI/URL: https://doi.org/10.1016/j.jtrangeo.2016.03.007 Research Topics: Land and airside accessibility Subject Airport: Weihai Dashuibo Airport (WEH), Yantai Penglai international airport (YNT), Beijing Capital international airport (PEK), and Tianjin Binhai international airport (TSN) Competition: Two MARs (Weihai-Yantai area & Beijing-Tianjin area) Input Data: Sum of productions of II and III industries, population, retailing amount of social consumption goods, flight density, number of airports with a nonstop connection, the sum of lengths of all flights from the airport (sources of data are not mentioned) How does this study use the concept of catchment area? Yang et al.(2016) utilize a structural equation model (SEM) to analyze the relationship between airport catchment area and the scale of flight network. Their original airport catchment areas are built by the proximity rule. And catchment areas are used to define the scope of analysis in their study. Catchment areas size: N/A Key Findings: In this study, Yang et al.(2016) build the regional air transportation accessibility profile using landside and airside accessibilities. Then they use the regional air transportation accessibility profile as a criterion to evaluate airport locations at a macro-planning level. Their study also suggests the optimal airport locations in both areas of interest.

120 [Perboli et al., 2015] Title: Flights and their economic impact on the airport catchment area: an application to the Italian tourist market Author: Guido Perboli; Marco Ghirardi; Luca Gobbato; Francesca Perfetti Journal: Journal of Optimization Theory and Application Year: 2015 DOI/URL: https://doi.org/10.1007/s10957-014-0613-8 Research Topics: The economic impact of flights and new airport routes Subject Airport: Airports located in two Italian Tourism markets: • The summer tourism market in Sardinia o Alghero (AHO) o Olbia Costa Smeralda (OLB) o Cagliari (CAG) • The winter tourism market in Piedmont o Turin (TRN) o Milan Linate (LIN) o Milan Malpensa (MXP) o Bergamo Orio al Serio (BGY) o Verona (VRN) o Venice (VCE) o Treviso (TSF) Competition: Two Italian MARSs Hub Category Mixed, a combination of different categories in two Italian MARs Input Data: Estimated costs related to the accommodation – Collected from telephone and online survey to several hotels How does this study use the concept of catchment area? Airport catchment areas in this study are different from most of the literature in that they are based on arrival passengers rather than origin passengers. Catchment areas are established using data collected by passenger surveys at the airport. Catchment areas size: Varying and could overlap with each other Key Findings: This study (Perboli et al., 2015) provides a standard methodology for analyzing the economic impact of flights and new airport routes. The method is applied to the tourist case studies to verify the adaptability of the proposed approach and of the AirCAST framework to different characteristics of the tourist market.

121 [Semenza et al., 2014] Title: International dispersal of dengue through air travel: Importation risk for Europe Author: Jan C. Semenza; Bertrand Sudre; Jennifer Miniota; Massimiliano Rossi; Wei Hu; David Kossowsky; Jonathan E. Suk; Wim Van Bortel; Kamran Khan Journal: PLOS Neglected Tropical Diseases Year: 2014 DOI/URL: https://doi.org/10.1371/journal.pntd.0003278 Research Topics: Dispersal of infectious diseases through international air travel Subject Airport: International airports in Europe Competition: N/A Hub Category: Hybrid (442 airports) Input Data: Passenger volumes into Europe – International Air Transport Association (IATA) Worldwide dengue outbreak notifications – DengueMap and European Centre for Disease Prevention and Control (ECDC) The number of reported dengue importations – National surveillance institutes, scientific publications, and reference laboratories How does this study use the concept of catchment area? This study aims at identifying how dengue spreads in Europe through air travel. Semenza et al. (2014) take into account the origin airports outside Europe but inside a 200km perimeter of dengue outbreak. Only catchment areas of the origin non-European international airports affected by dengue disease are considered by this study. Catchment areas size: 200 km perimeter (equivalent to 2-hour access time) Key Findings: The highest risk of dengue importation in 2010 was restricted to three months and can be ranked according to arriving traveler volume from dengue-affected areas into cities where the vector is present.

122 [Stergiou et al., 2013] Title: Expanding cross border airport catchment areas using intermodality: The case of Izmir Adnan Menderes Airport Author: Dimitrios P. Stergiou, Andreas Papatheodorou, Ioulia Poulaki Journal: Tourism Today Year: 2013 DOI/URL: https://www.researchgate.net/publication/265850210_Expanding_Cross_Border_ Airport_Catchment_Areas_Using_Intermodality_The_Case_of_Izmir_Adnan_M enderes_Airport Research Topics: Passengers’ origin airport choice Subject Airport: Izmir Adnan Menderes Airport (ADB) Competition: Between Izmir Adnan Menderes Airport (ADB) and Athens International Airport (ATH) Hub Category: Major airport (Turkey) Input Data: Demographic and traveling information of participants – Collected from resident surveys Access time and cost – Airline and independent booking websites How does this study use the concept of catchment area? In this study, Poulaki et al. (2013) define the catchment area using three different driving times to identify the amount of population being served. Poulaki et al. (2013) assume that intermodal transport can bring more passengers from remote regions. Catchment areas size: 30-, 60-, and 120-min driving distance Key Findings: Stergiou et al. (2013) explore airport choice between Athens International Airport (ATH) and Izmir Adnan Menderes Airport (ADB), by inhabitants of the Greek Eastern Aegean Island of Chios. The research findings suggest that ADB was the clear preference of respondents compared to ATH in the majority of cases, i.e., crossing the Greek-Turkish border did not prove a deterrent.

123 [Grubesic & Wei, 2012] Title: Evaluating the efficiency of the Essential Air Service program in the United States Author: Tony H. Grubesic; Fangwu Wei Journal: Transportation Research Part A: Policy and Practice Year: 2012 DOI/URL: https://doi.org/10.1016/j.tra.2012.08.004 Research topics: US Essential Air Service program Subject Airport: Essential Air Service airports in the United States (2010) Competition: Multiple SARs Hub Category: Nonprimary – Commercial Service + Primary – Nonhub Input Data: • Locations of all public-use airports – National Transportation Atlas (NTA) • Information on EAS communities and their associated subsidy levels – Office of Aviation Analysis (2010) • Originating flights (total), passenger traffic(total), and load factors for 2010 – Bureau of Transportation Statistics (BTS) • 2009 Population estimates – ESRI • Airport catchment areas and the shortest network distance from EAS airports to its nearest large or medium hub – ArcGIS How does this study use the concept of catchment area? In this study, Grubesic and Wei (2012) aim to examine the efficiency of the EAS program, a policy introduced after the 1978 US airline deregulation to subsidize remote and rural communities which would otherwise lose their access to commercial aviation due to the unintended consequence of the liberalization. This study uses the fixed radius (70 miles) to define airport catchment areas, which are used as the boundary to estimate the size of the population that might use these EAS airports. Catchment areas size: 70 miles (fixed radius) Key Findings: This study finds that though a handful of EAS airports exhibit relatively high levels of technical efficiency higher than 80, many more EAS airports do not. Approximately 85% of the EAS locations have efficiency scores below 80 in the geographically informed DEA model. Thus, one crucial policy question to consider is if these low-efficiency communities are worthy of federal subsidies.

124 [Maertens, 2012] Title: Estimating the market power of airports in their catchment areas – a Europe-wide approach Author: Sven Maertens Journal: Journal of Transport Geography Year: 2012 DOI/URL: https://doi.org/10.1016/j.jtrangeo.2011.11.007 Subject Airport: 50 largest European airports Competition: The study does not mention. Subject airports can be treated as located in multiple SARs. Hub Category: International gateway/Major airports (Europe) Input Data: GDP data at NUTS 3 level – Eurostat Flight supply (Departing seats in July 2010) – Official Airline Guide (OAG) Distances from airports to regions – ArcGIS & Google Maps How does this study use the concept of catchment area? Maertens (2012) utilizes one geographically statistic unit (Nomenclature of Units for Territorial Statistics, also known as NUTS 3) and defines all NUTS 3 regions as catchment areas whose geographical centers are located within an accessible distance of 100 km from each studied airport. This study then uses this catchment area as the boundary to estimate market shares. Catchment areas size: 100 km distance from geographical centers to airports (maximum of twice the average access time) Key Findings: The primary contribution of this study is that it develops a transparent modeling approach for the estimation of airport market power in the local passenger market segment.

125 [Dobruszkes et al., 2011] Title: An analysis of the determinants of air traffic volume for European metropolitan areas Author: Frédéric Dobruszkes; Moritz Lennert; Gilles Van Hamme Journal: Journal of Transport Geography Year: 2011 Doi: https://doi.org/10.1016/j.jtrangeo.2010.09.003 Research Topics: Air travel demand estimation Subject Airport: Airports located in the 113 European functional urban areas (FUAs) Competition: Mixed (SAR + MAR) Hub Category: Mixed categories Input Data:  Population and GDP – Municipal data from Espon database  National and international administrative function –Espon 1.4.3  Economic decision-power – Forbes, GAWC Data Set 12, Bureau Van Dijk  Knowledge and scientific research – Webometrics, Eurostat  Tourism – ULB-IGEAT, Michelin  Distance to the nearest airports – Self-measured How does this study use the concept of catchment area? Due to the number of airports dealt with concurrently by this study, it is impractical to use more refined approaches to measure multiple airport catchment areas. As the proxy for airport catchment areas, functional urban areas (FUAs) are then used to delimit the scope of independent variables. Catchment areas size: Use functional urban areas (FUAs) as proxies of the actual catchment areas. Key Findings: Dobruszkes et al. (2011) find that GDP, the level of economic decision-power, tourism functions, and the distance from a major air market account for more than two-thirds of the variation in air service. These results indicate that air service remains profoundly rooted in the metropolitan features of urban regions (notably size and decision-power), even if low-cost airlines are probably less linked to the latter because they partly focus on niche markets and regional airports.

126 [Fröhlich & Niemeier, 2011] Title: The importance of spatial economics for assessing airport competition Author: Karsten Fröhlich, Hans-Martin Niemeier Journal: Journal of Air Transport Management Year: 2011 DOI/URL: 10.1016/j.jairtraman.2010.10.010 Research Topics: Airport competition Subject Airport: N/A Competition: N/A Hub Category: N/A Input Data: Previously developed economic models of spatial competition How does this study use the concept of catchment area? In this study, Fröhlich and Niemeier (2011) compare the characteristics of the catchment area and available substitutes and assess the market power of airports. Catchment areas size: N/A (The study does not mention.) Key Findings: This study reviews two economic models of spatial competition, namely the Hotelling Model and the Ferreira & Thisse Model, in the analysis of airport competition. Fröhlich and Niemeier (2011) point to some of the reasons why there may be coordination and consolidation among airport companies.

127 [Grubesic & Matisziw, 2011] Title: A spatial analysis of air transport access and the essential air service program in the United States Author: Tony H. Grubesic, Timothy C. Matisziw Journal: Journal of Transport Geography Year: 2011 DOI/URL: https://doi.org/10.1016/j.jtrangeo.2009.12.006 Research Topics: Essential Air Service of the US Subject Airport: US Essential Air Service (EAS) airports Competition: N/A Hub Category: Nonprimary – Commercial service + Primary – nonhub Input Data—Sources: Categories of US airports – Innovata’s SRS database (2006) Driving distance between census tracts and airports – GIS How does this study use the concept of catchment area? In this study, Grubesic and Matisziw (2011) initially use driving distances of 70 and 100 miles to map the catchment areas of EAS and primary hub airports in the United States. They later estimate the percentage of the US population covered by airport catchment areas of different radiuses. Catchment areas size: 70- and 100-miles driving distance Key Findings: Grubesic and Matisziw (2011) consider two fixed distances in defining the catchment areas of focused airports. The results suggest the redundant coverage of EAS market areas by alternative Federal Aviation Administration designated hub airports can contribute to EAS airport market leakage and that alternative definitions of EAS community eligibility have the potential to dramatically increase programmatic efficiency and reduce federal monies spent on EAS subsidies.

128 [Suzuki et al., 2004] Title: Airport leakage and airline pricing strategy in single-airport regions Author: Yoshinori Suzuki; Michael R. Crum; Michael J. Audino Journal: Transportation Research Part E: Logistics and Transportation Review Year: 2004 DOI/URL: https://doi.org/10.1016/S1366-5545(03)00055-3 Research Topics: Airport traffic leakage Subject Airport: Des Moines International Airport (DSM) Competition: SAR Hub Category: Primary – Medium hub Input Data: • Traveler demographics, travel frequency, traveler age, participating frequent flyer programs, travelers’ type (business vs. leisure), access time to airports, and airport usage experience – Collected from passenger surveys • Airfares, number of flight legs, enplanements – Department of Transportation DB1A • Flight frequency per week – Official Airline Guide (OAG) • Income per capita – Bureau of Economic Analysis (BEA) • Population – US Census Bureau • Airlines market shares in airports – Self-estimated How does this study use the concept of catchment area? In addressing the effects of airfare on airport leakage, Suzuki et al. (2004) use 1- hour driving time as the radius of the catchment area of DSM. Then, Suzuki et al. (2004) conduct mail and intercept surveys on passengers living in the catchment area to understand their travel characteristics. Catchment areas size: 1-hour driving distance (75 miles) Key Findings: Suzuki et al. (2004) suggest that airlines may have under-estimated the airport- leakage tendencies of travelers in single-airport regions, and consequently, their current airfares in these regions may be higher than the optimal, or revenue- maximizing, levels. The results imply that, for most airlines, current airfares in DSM may be higher than the optimal and that they may increase revenues (or profits) by reducing airfares in DSM.

Next: Appendix B. Case Studies »
Toolkit for Establishing Airport Catchment Areas Get This Book
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 Toolkit for Establishing Airport Catchment Areas
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The catchment area of an airport encompasses areas where passengers are more likely to use the subject airport, even when there are other airport options in the vicinity.

ACRP Web-Only Document 56: Toolkit for Establishing Airport Catchment Areas, from TRB's Airport Cooperative Research Program, comprises various analytical tools, such as the Travel Utility Analysis tool, that enable airport industry practitioners to calculate the likely responses of travelers to different market and operational inputs, thus forecasting potential catchment areas for airports.

Supplemental to the report are three case studies: Case 1: Akron-Canton Airport (CAK), OH; Case 2: Ontario International Airport (ONT), CA; and Case 3: Albert J. Ellis Airport (OAJ), NC.

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