Airport classification criteria based on passenger characteristics and terminal size

Adikariwattage, Varuna; Barros, Alexandre G. de; Wirasinghe, S. C.; Ruwanpura, Janaka Y.

doi:10.1016/j.jairtraman.2012.06.004

Cited by 46 publications

(33 citation statements)

References 6 publications

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“…Thus the understanding of what passengers expect is the most vital step in delivering and defining the high-quality service. Service quality evaluation by customers is one of the best approaches to determine their perception and expectations (Adikariwattage, De Barros, Wirasinghe, & Ruwanpura, 2012). Fulfilled or even surpassed expectations of passengers lead to their satisfaction with airport's provided services.…”

Section: Passengers' Satisfaction As An Advantage Formentioning

confidence: 99%

Factor Analysis of Passengers’ Satisfaction at “RIGA International Airport”

Paramonovs

Ijevleva

2015

Economics and Business

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The aim of the paper is to determine complex factors influencing passengers' satisfaction at RIGA International Airport (Latvia). Theoretical evidence was examined and factor analysis was carried out to identify the main groups of factors affecting passengers' satisfaction at the Airport. The findings of the study show that the most important five factors affecting it are: (1) availability of telecommunications, (2) effective way finding signs, (3) cleanliness of restrooms, (4) courtesy of staff and (5) availability of staff. The results based on this study will help airport managers in the Baltic Sea region to better serve their passengers through introduction of modern technologies and improved attitudes.

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Section: Passengers' Satisfaction As An Advantage Formentioning

confidence: 99%

Factor Analysis of Passengers’ Satisfaction at “RIGA International Airport”

Paramonovs

Ijevleva

2015

Economics and Business

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“…Hence, there is a potential to optimise the social benefits from AIP investments by changing the NPIAS airport classification to explicitly acknowledge the importance of hub connectivity along with the airport's potential for traffic generation. 1 Previous papers have already addressed the limitations of the FAA's uni-dimensional method along the same lines (Rodríguez-Déniz et al, 2013), and proposed alternative approaches that take into account airport size, traffic generation and connectivity (Adikariwattage et al, 2012). However, these studies are biased by the lack of detailed data on international markets, which is not provided by the widely-used DOT traffic databases.…”

Section: Take Down Policymentioning

confidence: 99%

“…Airport connectivity/centrality studies as detailed and only origin and destination statistics are provided. Measuring international connectivity for individual airports is therefore not possible.Using the DOT datasets,Adikariwattage et al (2012)classified US airports using four variables: boarding gates, domestic origin-destination passengers, domestic transfers and…”

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confidence: 99%

Regulatory airport classification in the US: The role of international markets

Suau-Sánchez¹,

Voltes-Dorta²,

Rodríguez-Déniz³

2015

Transport Policy

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The US Federal Aviation Administration (FAA) provides grants to airports for capital developments under the Airport Improvement Program (AIP). The AIP is one of five major sources of airport capital development funding. The other sources are tax-exempt bonds, passenger facility charges (PFC), state and local grants, and airport operating revenue. Different airports use different combinations of these sources depending on the individual airport's financial situation and the type of project being considered. Small airports are more dependent on AIP grants than large or medium sized airports. The larger airports, whose projects tend to be much more costly, are more likely to participate in the tax-exempt bond market or finance capital development projects with a PFC (Kirk, 2007). Hence, although the AIP may not be the main source of finance for major airports, it is still an important source of capital for improvements related to airport safety, capacity, security and the environment. In 2014, $586.2 million in AIP funds were allocated to the 30 largest hubs in the US (approximately 18.2% of the nationwide grants). The latest estimates for the AIP budget indicate a 19% decrease ($52.2 billion to $42.5 billion) for the period 2013-2017, with respect to the estimates provided two years earlier (FAA, 2012). While this drop can be linked to the waning effect of the economic stimulus legislation (DOT, 2013), the FAA has also been pressured to reduce its budget; the Department of Transportation (DOT) pointing at cost inefficiencies as the root of the problem (DOT, 2013:113). From the Agency's perspective, a debate on the future of the FAA's funding model has been proposed (Flightglobal, 2013). Within a context of financial constraints, public spending should look for a higher efficiency and impact of the resources invested. In this regard, this paper develops a new US airport typology that can help optimise the AIP. The current statutory classification is defined in the National Plan of Integrated Airport Systems (NPIAS) report, which groups airports according to their size and role within the US network and it is mainly based on each airport's share of the total US passenger enplanements (Table 1). Table 1.FAA's system of airport classification. Commercial Airport Type At least 2,500 boardings Hub type Percentage of US-wide annual passenger enplanements (739.3 million enplanements in 2013) Common name Primary Large 1% or more Large Hub Medium At least 0.25%, but less than 1% Medium Hub Small At least 0.05%, but less than 0.25% Small Hub Non-hub More than 10,000 enplanements, but less than 0.05% Non-hub Primary Non-primary Non-hub At least 2,500 and no more than 10,000

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“…On the other hand, there are numerous applications of clustering techniques in the air transportation sector: Adikariwattage et al (2012) have examined major airports in the USA and identified, using traditional clustering techniques, homogeneous groups of airports using as classification variables the number of gates and annual international and domestic and transit passenger traffic. Malighetti et al (2009) classified the 467 European airports examined into 8 strategic groups on the basis of the specific characteristics of each airport and the role covered within the network.…”

Section: State Of the Artmentioning

confidence: 99%

Port Cooperation Policies in the Mediterranean Basin: An Experimental Approach Using Cluster Analysis

Fancello

Pani

Serra

et al. 2014

Transportation Research Procedia

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The great potential of the Mediterranean area, as yet not fully tapped due to the lack of the integrated management of its ports, calls for innovative management policies for achieving competitiveness within the Mediterranean port system. To this end, the current regime of intra-port competition has proven highly unproductive and needs to be rethought, implementing new cooperation policies. The aim of this study is to identify, by means of traditional clustering techniques, homogeneous groups of ports within the Mediterranean region. In so doing, it would be possible to propose new cooperation policies between ports of the same cluster, but also between different clusters, on the basis of their specific features. A data set has been created for 34 major Mediterranean container ports. Relations between ports have been evaluated from a quantitative perspective through traditional statistical techniques: hierarchical cluster analysis based on the Ward method. Different sets of homogeneous ports have been obtained alternating different combinations of input variables and varying these over suitable ranges, in line with the assumed cooperation policies. The findings provide the basis for exploring the strategic functional relationships among ports, in order to promote collective integrated actions that could prove essential for the competitiveness of the Mediterranean port system.

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Airport classification criteria based on passenger characteristics and terminal size

Cited by 46 publications

References 6 publications

Factor Analysis of Passengers’ Satisfaction at “RIGA International Airport”

Factor Analysis of Passengers’ Satisfaction at “RIGA International Airport”

Regulatory airport classification in the US: The role of international markets

Port Cooperation Policies in the Mediterranean Basin: An Experimental Approach Using Cluster Analysis

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