Robustness of the air transport network

This paper presents a methodology for the detection of critical airports (those whose isolation would cause the largest losses in network connectivity) in the global air transport network (ATN), based on simulating an attack on selected ATN airports using different adaptive selection criteria. The performances of several node selection criteria are compared, together with a new criterion based on Bonacich power centrality. The results show that most critical airports can be detected with an adaptive strategy based on betweenness centrality. The detection of such airports may help the development of contingency plans to develop an appropriate response to any airport closure.     

Exploring the network structure and nodal centrality of China’s air transport network: A complex network approach

This paper uses a complex network approach to examine the network structure and nodal centrality of individual cities in the air transport network of China (ATNC). Measures for overall network structure include degree distribution, average path length and clustering coefficient. Centrality metrics for individual cities are degree, closeness and betweenness, representing a node’s location advantage as being directly connected to others, being accessible to others, and being the intermediary between others, respectively. Results indicate that the ATNC has a cumulative degree distribution captured by an exponential function, and displays some small-world (SW) network properties with an average path length of 2.23 and a clustering coefficient of 0.69. All three centrality indices are highly correlated with socio-economic indicators of cities such as air passenger volume, population, and gross regional domestic product (GRDP). This confirms that centrality captures a crucial aspect of location advantage in the ATNC and has important implications in shaping the spatial pattern of economic activities. Most small and low-degree airports are directly connected to the largest cities with the best centrality and bypass their regional centers, and therefore sub-networks in the ATNC are less developed except for Kunming in the southwest and Urumchi in the northwest because of their strategic locations for geographic and political reasons. The ANTC is relatively young, and not as efficient and well-developed as that of the US.     

On the multi-dimensionality and sampling of air transport networks

Complex network theory is a framework increasingly used in the study of air transport networks, thanks to its ability to describe the structures created by networks of flights, and their influence in dynamical processes such as delay propagation. While many works consider only a fraction of the network, created by major airports or airlines, for example, it is not clear if and how such sampling process bias the observed structures and processes. In this contribution, we tackle this problem by studying how some observed topological metrics depend on the way the network is reconstructed, i.e. on the rules used to sample nodes and connections. Both structural and simple dynamical properties are considered, for eight major air networks and different source datasets. Results indicate that using a subset of airports strongly distorts our perception of the network, even when just small ones are discarded; at the same time, considering a subset of airlines yields a better and more stable representation. This allows us to provide some general guidelines on the way airports and connections should be sampled.     

Increasing the resilience of air traffic networks using a network graph theory approach

Air traffic networks are essential to today’s global society. They are the fastest means of transporting physical goods and people and are a major contributor to the globalisation of the world’s economy. This increasing reliance requires these networks to have high resilience; however, previous events show that they can be susceptible to natural hazards. We assess two strategies to improve the resilience of air traffic networks and show an adaptive reconfiguration strategy is superior to a permanent re-routing solution. We find that, if traffic networks have fixed air routes, the geographical location of airports leaves them vulnerable to spatial hazard.     

Spatial structure and network behaviour of strategic airline groups: A comparison between Europe and the United States

The Gini-index has gained in legitimacy when measuring traffic distributions of air traffic as compared to other more established measures, such as Herfindahl's. In order to render the index more meaningful for policy-makers, a partial decomposition into strategic groups of airlines along distinct geopolitical scopes of air traffic is suggested. Air traffic across airports is considered an aggregate of complex networks that are subject to multiple constraints, such as geopolitics or technology. A multi-layered analytic approach accounts for network operators as economic agents that behave in strategic ways within these constraints. Our approach allows for comparing traffic distributions in Europe with that of the US and, in particular, introduces a normative component by isolating patterns in airlines’ strategies that are likely to induce different degrees of spatial concentration and balanced traffic distributions within these common markets.     

A complex network approach towards modeling and analysis of the Australian Airport Network

An airport network forms the backbone of any air transportation system. The relationships among the origins and destinations of flights result in a complex network of routes which can be complemented with information associated with the routes themselves, for instance, traffic load and distance. In this paper, we modeled the Australia's civil domestic airport infrastructure as a network and analyzed the resulting network structure and its features using complex network tool. This case study identifies and investigates complex network measures, such as the degree distribution, characteristics path length, clustering coefficient and centrality measure as well as the correlations among them to understand the topology of an airport network. This analysis of the Australian Airport Network (AAN) indicates that it has a cumulative degree distribution described by the power-law function. As it has an average path length of 2.90, it is considered to have small-world properties. It is also found that it has a clustering coefficient of 0.50 which is higher than that of a random network of the same size which indicates that the transitivity and cohesiveness of AAN is different from a random network. In contrast to the World-wide Airport Network (WAN), the AAN is found to have disassortative mixing similar to the airport networks of China and India.     

Price and marginal cost effects of on-time performance: Evidence from the US airline industry

We examine the impact of flight delay on market power in the U.S. airline industry. Consistent with the literature, our estimation results confirm that flight delays are associated with negative consumer welfare effects. Most importantly, we find that poor on-time performance negatively impacts product markup—proxy for market power. Furthermore, we are able to decompose these markup effects into price and marginal cost effects and evidence suggests that the source of these negative markup effects is primarily driven by negative price responses.     

Mobility biographies: A review of achievements and challenges of the mobility biographies approach and a framework for further research

In transport research, important changes in an individual’s life, so-called key events, are of particular interest because changes in one’s environment and surrounding context weaken routines, and a ‘window of opportunity’ opens up for behavioural change. In the framework of the mobility biographies approach, several studies have been undertaken to improve understanding of travel behaviour change in the last decade. This paper critically and systematically reviews emerging results from mobility biographies research, focusing on key events which potentially induce contextual and travel behaviour changes. The first step was to identify 25 of the most important studies for this approach and their related concepts. Next, notions other than key events in the literature are discussed and a theoretical framework for the longitudinal interaction of four dimensions of key events and other factors for travel behaviour change are identified: (i) life events in private and professional careers, (ii) adaptation of long-term mobility decisions, (iii) exogenous interventions, and (iv) other long-term processes which are not key events (e.g. socialisation). Furthermore, the understanding of key events, methodological challenges and further research gaps are critically discussed in this review.