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Complex networks have encouraged scholars to develop an effective method for abstracting and optimizing aviation networks. However, researchers often overlook the aviation network's temporal attribute and treat it as a static network. Aviation networks have strong temporal characteristics and the dynamic connection cannot be realistically described by a static network. It is necessary to more accurately and realistically represent these connections during the operation of an aviation network. This study explored temporal structures of the Chinese aviation temporal network (CATN) based on flight schedules and actual operational time data. Temporal networks based on time windows were represented to analyze the temporal topology features and robustness of the CATN. The results demonstrated the following: (1) based on the spatial-temporal aviation network, there is a morning departure peak (7:00–8:00) and an evening arrival peak at the airline hub (20:00–21:00); (2) examining the centrality of each airport in the CATN at various time intervals exposed fluctuations in their rankings, which could not be identified by a static network, and (3) the robustness of the CATN was found to be unaffected by time windows, but it displayed poor resilience against deliberate attacks, particularly when subjected to betweenness and closeness attacks, which target the network's shortest paths. For obtaining a greater understanding of the operating situation of civil aviation, displaying the topological features and robustness of the temporal network is of great importance. [ABSTRACT FROM AUTHOR]

In order to improve the service level of small and medium-sized airlines, enhance its flexibility to cope with extra flights, the article designs a new crew pairing solutions for extra flights. Based on the traditional model of the crew pairing, meeting the requirements of crew pairing for extra flights, we propose robust crew pairing model. Then through the example analysis, we used CPLEX software to solve the model, obtained the optimal solutions. Finally, through the comparison of different results, revealing the advantage of robust modeling method and showing that the proposed robust models provide natural options to recovery without disrupting the existing flights at a relatively small incremental cost, and providing meaningful reference for the small and medium-sized airline in the problem of coping with extra flights. [ABSTRACT FROM AUTHOR]