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.

Applying complexity science to air traffic management

Complexity science is the multidisciplinary study of complex systems. Its marked network orientation lends itself well to transport contexts. Key features of complexity science are introduced and defined, with a specific focus on the application to air traffic management. An overview of complex network theory is presented, with examples of its corresponding metrics and multiple scales. Complexity science is starting to make important contributions to performance assessment and system design: selected, applied air traffic management case studies are explored. The important contexts of uncertainty, resilience and emergent behaviour are discussed, with future research priorities summarised.

The paradox of competition for airline passengers with reduced mobility (PRM)

Airline competition with customer service as product differentiator has forced down costs, air fares and investor returns. Two passenger markets operate in aviation: (a) able-bodied passengers for whom airlines compete and (b) passengers with reduced mobility (PRMs) – disabled by age, obesity or medical problems – for whom airlines do not compete. Government interference in the market intended to protect a minority of narrowly-defined PRMs has had unintended consequences of enabling increasing numbers of more widely-defined PRMs to access complimentary airline provisions. With growing ageing and overweight populations and long-haul travelling medical tourists such regulation could lead to even lower investors’ returns. The International Air Transport Association (IATA) (2013) examined the air transport value chain for competitiveness using Porter’s (2008) five forces but did not distinguish between able-bodied passengers and PRMs. Findings during an investigation of these two markets concurred with IATA-Porter that the markets for the bargaining powers of PRM buyers and PRM suppliers were highly competitive. However, in contrast to the IATA conclusions, intensity of competition, and threats from new entrants and substitute products for PRM travel were low. The conclusion is that airlines are strategically PRM defensive by omission. Paradoxically, the airline which delivers the best PRM customer service could become the least profitable.

Measuring the cost of resilience

Air traffic management research lacks a framework for modelling the cost of resilience during disturbance. There is no universally accepted metric for cost resilience. The design of such a framework is presented and the modelling to date is reported. The framework allows performance assessment as a function of differential stakeholder uptake of strategic mechanisms designed to mitigate disturbance. Advanced metrics, cost- and non-cost-based, disaggregated by stakeholder sub-types, are described. A new cost resilience metric is proposed and exemplified with early test data.