Structural Properties of the Brazilian Air Transportation Network

Couto, Guilherme S.; Silva, Ana Paula Couto da; Ruiz, Linnyer B.; Benevenuto, Fabrí­cio

doi:10.1590/0001-3765201520140155

Cited by 22 publications

(20 citation statements)

References 13 publications

(14 reference statements)

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“…Trata-se de um subconjunto do sistema completo, o qual inclui passageiros e carga e é formado por cerca de 850 aeroportos. Por outro lado, quando comparada com outras redes de transporte de carga e passageiros no mundo, como a do Brasil que é formada por 120 aeroportos (Couto et al, 2015), a rede GT20 mostra-se consideravelmente superior. A Figura 1 apresenta a quantidade de aeroportos para cada rede específica.…”

Section: Análise E Resultadosunclassified

“…A seguir são apresentados os conceitos relacionados ao conjunto de métricas de rede utilizadas no presente estudo, com base em Newman (2003), Reka e Barabási (2002) e Couto et al (2015).…”

Section: Métricas De Redeunclassified

“…Quando se avalia o sistema de transporte aéreo nos Estados Unidos de forma integral, passageiros e carga, verifica-se que é formado por uma rede ampla, constituída de cerca de 850 aeroportos, dezenas de companhias aéreas e milhares de voos (Couto et al, 2015), tendo essa rede evoluído desde a desregulamentação do setor após 1978 de um sistema predominante de rotas lineares "ponto a ponto" para estruturas do tipo hub and spoke, com diversas conexões a partir da introdução de grandes aeronaves (wide body air-TRANSPORTES v. 24, n. 4 (2016), p. 1-9 craft), além da introdução de companhias aéreas que operam com baixo custo e tarifas (low-cost airlines -LCCs) (Cook e Goodwin, 2008).…”

Section: Introductionunclassified

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Análise de redes sociais aplicadas ao transporte aeroviário de cargas

2016

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Resumo: O presente artigo possui como objetivo analisar redes de transporte aéreo exclusivamente de carga nos Estados Unidos durante uma década, entre os anos de 2004 e 2014. Para isso é utilizada a Análise de Redes Sociais onde indicadores como closeness (centralidade de proximidade) e betweenness (centralidade de intermediação) são calculados. O artigo esboça comparações entre as redes e respectivas métricas das duas maiores empresas aéreas do setor e a das 18 maiores aéreas seguintes, o que permite avaliar o impacto de retrações econômicas, como a ocorrida em 2008, nessas redes assim como revelar assimetrias entre empresas de diferentes portes. Pode-se notar que, dentre outros aspectos, o grafo do transporte aéreo de carga gerado apresenta grande influência das duas maiores empresas privadas do setor, a FedEx e a UPS o que pode ser evidenciado, por exemplo, pelo número de nós que, em 2014, apresentou os valores de 178 e 106 respectivamente em comparação com 81 das demais 18 maiores aéreas.Palavras-chave: redes de transporte aéreo de carga; análise de redes sociais; redes complexas. Abstract:The present work objective is the analysis of the United States air cargo transport during a decade, from the year 2004 to 2014. The network theory is used and indicators such as closeness and betweenness are calculated. The present work compares the networks and the respective metrics of the two main airlines of the industry and the other 18 biggest companies what enables the evaluation of the impact of economic recessions, such as the one from 2008, on these networks and the detection of assymetries between companies of different sizes. It is possible to note that, among other aspects, the air cargo transport graph is heavily influenced by the two main private companies of the sector, FedEx and UPS, what can be pointed out by, e.g., the number of nodes of 178 and 106 in 2014 respectively for these networks compared to 81 from the other 18 biggest companies.

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Section: Análise E Resultadosunclassified

Section: Métricas De Redeunclassified

Section: Introductionunclassified

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Análise de redes sociais aplicadas ao transporte aeroviário de cargas

2016

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“…In spite of this, scalefreeness has been claimed for the Italian [11] (42 airports), Indian [12] (79 airports), the Brazilian [13] (120 airports), or the Chinese network [14] (128 airports). The situation is even worse in the case of road networks, in which the physical nature of the graph implies that the degree of each node is limited, as, for instance, it is difficult to plan a crossroad where more than six streets converge.…”

Section: Common Pitfalls and Misleadingmentioning

confidence: 99%

Studying the Topology of Transportation Systems through Complex Networks: Handle with Care

Zanin

Sun

Wandelt

2018

Journal of Advanced Transportation

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The introduction of complex network concepts in the study of transportation systems has supposed a paradigm shift and has allowed understanding different transport phenomena as the emergent result of the interactions between the elements composing them. In spite of several notable achievements, lurking pitfalls are undermining our understanding of the topological characteristics of transportation systems. In this study, we analyse four of the most common ones, specifically related to the assessment of the scale-freeness of networks, the interpretation and comparison of topological metrics, the definition of a node ranking, and the analysis of the resilience against random failures and targeted attacks. For each topic we present the problem from both a theoretical and operational perspective, for then reviewing how it has been tackled in the literature and finally proposing a set of solutions. We further use six real-world transportation networks as case studies and discuss the implications of these four pitfalls in their analysis. We present some future lines of work that are stemming from these pitfalls and that will allow a deeper understanding of transportation systems from a complex network perspective.

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“…Cardillo et al (2013) [9] used real data from the European Air Transport Composite Network to analyze the structural characteristics of an essentially multilayer real network. Couto et al (2015) [10] analyzed the complex network characteristics of the Brazilian air transport network and confirmed that the network had small-world characteristics, while the national connection network followed a power law distribution. Sun et al (2016) [11] found that most of Dublin's important nodes and transportation hubs were located in the city center, i.e., most bus lines often intersect with the city center.…”

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confidence: 99%

Grade Setting of a Timber Logistics Center Based on a Complex Network: A Case Study of 47 Timber Trading Markets in China

Liang

Huang

et al. 2020

Information

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The location and grade setting of a timber logistics center is an important link in the optimization of the timber logistics system, the rationality of which can effectively improve the efficiency of the timber logistics supply chain. There is a long distance between the main forested areas in China, and more than 55% of the timber demand depends on imports. Research and practice of systematically planning timber logistics centers in the whole country have not been well carried out, which reduces the efficiency of timber logistics. In this paper, 47 timber trading markets with a certain scale in China are selected as the basis for logistics center selection. Based on their transportation network relationship and the number of enterprises in the market, combined with the complex network theory and data analysis method, the network characteristics of three different transportation networks are measured. After determining the transportation capacity indicator, the logistics capacity coefficient is measured based on the freight volume of each node. Then, the important nodes are identified, and each node is graded to systematically set up the timber logistics center.Information 2020, 11, 107 2 of 19 algorithm. The upper-level model is to determine the optimal location by minimizing the planners' cost, and the lower gives an equilibrium demand distribution by minimizing the customers' cost, so as to find the best location for a logistics distribution center. Marković et al. (2013) [2] considered the significance of selecting and ranking different locations. It is necessary to compare, as objectively as possible, the influences of various criteria and reduce them to a common function, i.e., present the methodology for solving complex problems associated with the ranking of alternatives. Önden et al. (2016) [3] determined that 19 logistics centers in Turkey are developing a systematic approach and integrating different transport modes to improve logistics performance. To reach suitability levels, seven decision criteria were considered with their priority levels. Zhang et al. (2017) [4] proposed a rural logistics center location model based on the theory of intuitionistic fuzzy TOPSIS. On the basis of the evaluation indicator system, the information is integrated according to the experts' score. The entropy weight method is used to determine the weight of each evaluation indicator. The results are sorted by intuitionistic fuzzy TOPSIS. Vanessa et al. (2017) [5] noted that the logistics integration center (LIC) has become an attractive option to realize efficient intermodal transport. The mixed integer linear programming model is applied to the soybean flow in Brazil, which can be used to evaluate whether the transportation cost will be reduced if there are LICs. Zhao (2018) [6] noted that when deciding medicine logistics center location with financial results in mind, market size, growth and demand creation must be the primary considerations. Mousavi et al. (2019) [7] presented a new MCGDM model that applied the conc...

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Structural Properties of the Brazilian Air Transportation Network

Cited by 22 publications

References 13 publications

Análise de redes sociais aplicadas ao transporte aeroviário de cargas

Análise de redes sociais aplicadas ao transporte aeroviário de cargas

Studying the Topology of Transportation Systems through Complex Networks: Handle with Care

Grade Setting of a Timber Logistics Center Based on a Complex Network: A Case Study of 47 Timber Trading Markets in China

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