Recurrence in the evolution of air transport networks

Changes in air transport networks over time may be induced by competition among carriers, changes in regulations on airline industry, and socioeconomic events such as terrorist attacks and epidemic outbreaks. Such network changes may reflect corporate strategies of each carrier. In the present study, we propose a framework for analyzing evolution patterns in temporal networks in discrete time from the viewpoint of recurrence. Recurrence implies that the network structure returns to one relatively close to that in the past. We applied the proposed methods to four major carriers in the US from 1987 to 2019. We found that the carriers were different in terms of the autocorrelation, strength of periodicity, and changes in these quantities across decades. We also found that the network structure of the individual carriers abruptly changes from time to time. Such a network change reflects changes in their operation at their hub airports rather than famous socioeconomic events that look closely related to airline industry. The proposed methods are expected to be useful for revealing, for example, evolution of airline alliances and responses to natural disasters or infectious diseases, as well as characterizing evolution of social, biological, and other networks over time.

Evolution of Air Transport Networks under Different Airline Business Models: The Case of Three Chinese Airlines

This study was conducted to compare the evolution process of air transport networks (ATNs) under different airline business models, and empirically analyzed the causes of evolution differences combining with airline market behaviors. Three representative Chinese airlines (China Southern Airlines, Spring Airlines, and Lucky Air) that belong to three business models (full-service, low-cost, and regional) were examined. Based on mathematical statistics and complex network theory, from spatial pattern, topological feature and central city, the result showed that the airlines have their own unique evolution characteristics of ATNs driven by different business models. China southern airlines as state-owned full-service network carrier, its ATN is easily affected by national policy. Spring Airlines has more flexible and diverse choice in choosing target markets and the ATN had a transformation from a mono-centric divergence network to a two-centric regional one. Lucky Air as a regional feeder carrier, whether navigable airports or navigable routes increasingly concentrated within the southwest of China. This information is important for government to improve the national ATN by developing targeted airlines.

A New Approach for Measuring the Resilience of Transport Infrastructure Networks

Resilience is an important property of transport infrastructure networks. Resilient transport infrastructure networks can retain the performance in case of disturbances and quickly recover to the original performance level after the disturbances. This study proposes a new approach that can measure the resilience of transport infrastructure networks. The new approach gives a unified conceptual framework for measuring the resilience of transport infrastructure networks. A network simulation-based method is used to analyze the influential factors of transport infrastructure network resilience. A new measuring method is developed based on network diversity characteristic to quantitatively measure the system resilience and node resilience of transport infrastructure networks. China railway and air transport networks are selected as a case study to applicability of new approach. This new approach provides strong supports for academic and industrial fields to measure, analyze, enhance, and optimize the resilience of transport infrastructure networks.

A Network Modelling Approach to Flight Delay Propagation: Some Empirical Evidence from China

This paper examines flight delay propagation in air transport networks. Delays add to additional costs, inefficiencies, and unsustainable development. An integrated flight-based susceptible-infected-susceptible (FSIS) model was developed to analyse the flight delay process from a network-based perspective. The probability of flight delay propagation was determined using a translog model. The model was applied to an airline network consisting of thirty-three routes involving three airlines. The results show that the propagation probability is network-related and varies across different routes. The variation is related to the flight frequencies at airports, route distances, scheduled buffer times, and the propagated delay time. Whereas buffer time has a greater impact on smaller airports, flight movement has a greater impact on larger airports. Having a better understanding of how delays happen can help the development of strategies to avoid them. This will lead to less costs, higher efficiencies, and more sustainable airport and airline development.