The literature on airlines presents few studies analyzing the airlines network evolution. We believe that this gap is due to the difficulty of capturing the network complexity in a simple manner. This paper proposes new simple and continuous indicators to measure the airlines' network structure. The methodology to build them is based on graph theory and principal component analysis. We apply this approach to the US domestic market for 2005-2018, and obtain three network indicators. The first one measures how close the network is to a single-center structure. The second indicator measures the airline's ability to provide alternative routes. The third indicator captures the network size. We analyze the indicators evolution across time and show their robustness under different scenarios.
We study the impact of airline network design on excess travel times for the main US carriers between 2008 and 2017 and find that network configuration affects excess travel time. Based on graph theory and a principal component analysis we build four continuous indicators to measure the airlines networks. We observe that airlines serving more destinations, organizing flights landings and take offs around banks or moving towards a point to point configuration present higher levels of excess travel time. However, there does not seem to exist a preferred network configuration between hub and spoke or point-to-point configuration to reduce excess travel time. We also find a nonlinear impact of competition measured at the citypair level over excess travel time. These results are robust when analyzing observed delays rather than excess travel time.
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