Analysis of the Air Transport Network Characteristics of Major Airports

Song, Mi Sook; Yeo, Gi Tae

doi:10.1016/j.ajsl.2017.09.002

Cited by 48 publications

(20 citation statements)

References 20 publications

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“…Although SNA was developed to investigate social interactions among people, it has also been used to perform analysis in non-social structures [30,31]. SNA concepts and metrics have been employed in various fields, such as the World Wide Web [32,33]; product development [34,35]; spread of epidemic disease [36]; physics [37]; diffusion of new ideas, knowledge, and inventions [38][39][40]; air traffic routes among airports [41][42][43]; logistics and transportation [44]; and electric power grids [45][46][47]. Particularly, complex network theory has been utilized to assess the vulnerability of the European power transmission grid with respect to extreme space weather [48].…”

Section: Methodsmentioning

confidence: 99%

An SNA-DEA Prioritization Framework to Identify Critical Nodes of Gas Networks: The Case of the US Interstate Gas Infrastructure

Storto

2019

Energies

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This paper presents a framework to identify critical nodes of a gas pipeline network. This framework calculates a set of metrics typical of the social network analysis considering the topological characteristics of the network. Such metrics are utilized as inputs and outputs of a (Data Envelopment Analysis) DEA model to generate a cross-efficiency index that identifies the most important nodes in the network. The framework was implemented to assess the US interstate gas network between 2013 and 2017 from both the demand and supply-side perspectives. Results emerging from the US gas network case suggest that different analysis perspectives should necessarily be considered to have a more in-depth and comprehensive view of the network capacity and performance.

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Section: Methodsmentioning

confidence: 99%

An SNA-DEA Prioritization Framework to Identify Critical Nodes of Gas Networks: The Case of the US Interstate Gas Infrastructure

Storto

2019

Energies

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“…To decompose SE from AOE, we need to obtain the pure technical airport efficiency (PTAOE) under the variable returns to scale (VRS) condition. For this purpose, we applied Σλ = 1 in Equation (1). For this decomposition to enhance efficiency, we shall decompose AOE under the constant returns to scale (CRS) condition as follows.…”

Section: Efficiency Decompositionmentioning

confidence: 99%

“…As an important component of the logistics infrastructure for cities and air transportation, airports play an important role in the integrated regional cooperative network economy. Moreover, as major airports lead the demand-driven economy of these regional networks, many of the world's major countries have been actively developing regional hub airports, with airport expansions and modernization of facilities, seeking to attract passengers and cargo with better airport services [1]. To become a regional hub for this network economy, however, a large segment of the airport industry urgently needs to enhance its effective investment in infrastructure, as well as advanced airport management, to improve its own operating efficiency and effectively compete with other airports in neighboring areas.…”

Section: Introductionmentioning

confidence: 99%

Measuring Operational Performance of Major Chinese Airports Based on SBM-DEA

et al. 2020

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This study analyzes the sustainable feasibility of major airports in China in terms of airport operational efficiency (AOE). As AOE should be measured by economic performance as well as qualitative service management such as delay rate abatement, our study uses a multi-input/output slack-based data envelopment analysis model. We find that the 37 major airports in China have very low AOE levels, with an average of 48.2% during the study period of 2016–2019, implying great potential to enhance their efficiency. Even though the AOE trend is increasing upwards, it is still very much behind in terms of global standards. Moreover, this upward trend may come from external factors in the commercially driven eastern region airports and politically supported western region airports, and the AOE gap with airports in the central region is becoming larger. This implies that most airports in China are not yet self-sustaining. There are two ways for these airports to enhance AOE: more investment in infrastructure, such as airport facilities, and management upgrades from peer-learning efforts. We examined the feasibility of these two optimal paths and found that there is no need for decreasing returns to scale, implying that most of the airports can improve their AOE through additional investment, except for the eight airports with constant returns to scale, such as Beijing and Guangzhou. Moreover, each of the individual airports should learn from the top benchmarking airports on the production frontier. This study emphasizes the role of qualitative service performance and concludes that customized, self-sustaining innovation is required for all of the 37 major airports in China.

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“…Recently, the development of complex network theory has led to many studies being published in the traffic field [1], including research on the network structure of high-speed passenger transport systems, such as high-speed rail [2,3] and air transport [4][5][6][7][8][9][10]. As a complex man-made network, the air transport network plays an important role in human social and economic development.…”

Section: Introductionmentioning

confidence: 99%

Evolution and Determinants of an Air Transport Network: A Case Study of the Chinese Main Air Transport Network

Min

Luan

et al. 2019

Sustainability

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The Chinese main air transport network (CMATN) is the framework for air passenger transport in the country. This study uses complex networks and an econometric model to analyze CMATN’s evolution and determinants. In terms of overall network structure, the network has always shown small-world properties, with smaller average path lengths (2.06–2.15) and larger clustering coefficients (0.68–0.77), while its cumulative degree distribution follows an exponential function. City passenger volumes conform to the degree power law function, which means that the more destinations a city connects to, the higher its passenger traffic will be. In major hub cities, such as Beijing, Shanghai, and Guangzhou, control power decreases, while Chengdu, Kunming, Chongqing, Xi’an, Urumqi, and other cities play an increasingly important role in CMATN. In terms of main route passenger volumes and formation, increases in GDP and tourism have had a promoting effect, while high-speed rail (HSR) poses a threat to overlapping routes. CMATN is primarily located in the central and eastern regions, focusing on China’s economy, tourism, and efficient HSR development. Although the competition from HSR affects the overall network structure of CMATN based on its influence on specific routes, we believe that the impact is limited due to the different transport attributes of the two networks. The research results of this study can become an information source for decision makers and provide a reference for air transport to seek sustainable development.

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Analysis of the Air Transport Network Characteristics of Major Airports

Cited by 48 publications

References 20 publications

An SNA-DEA Prioritization Framework to Identify Critical Nodes of Gas Networks: The Case of the US Interstate Gas Infrastructure

An SNA-DEA Prioritization Framework to Identify Critical Nodes of Gas Networks: The Case of the US Interstate Gas Infrastructure

Measuring Operational Performance of Major Chinese Airports Based on SBM-DEA

Evolution and Determinants of an Air Transport Network: A Case Study of the Chinese Main Air Transport Network

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