arXiv subjects

Shinya Hanaoka

Publications and source records attributed to Shinya Hanaoka.

2 recordsLinked to original sources

arXiv2024

Disruption and recovery of the US domestic airline networks during the COVID-19 pandemic

The COVID-19 pandemic has had serious impacts on the airline industry. Ensuring that aviation policies in emergent situations both guarantee network connectivity and maintain competition among airlines is crucial in these circumstances. To this end, we aimed to understand the network dynamics of individual airlines. In this study, we quantitatively reveal the day-to-day dynamics of these US domestic airline networks, comprising 17 airlines, from January 2019 to December 2021. Specifically, we applied a framework for analyzing temporal networks, in which the network structure changes over time. We found that, first, even though the number of nodes and edges returned to pre-pandemic levels around July 2021, the structure of the entire US domestic airline network remained altered. We also found that the network dynamics varied significantly from airline to airline. Full-service carriers were less flexible in changing their network structure and suffered higher revenue losses. On the contrary, most regional carriers completely shifted to a new structure, which may have contributed to reducing their revenue losses. Low-cost carriers were characterized by more pronounced differences between airlines and drastically changed their network structure immediately after the declaration of a national emergency. Finally, we also examined the recovery process and found that the flights connecting airports that are more central and share more common neighbors, and those connecting airports with larger numbers of connections tend to recover earlier for most of the airlines.

physics.soc-ph↗

arXiv2024

Delay propagation patterns in Japan's domestic air transport network

We experience air traffic delays every day, but are there any recurrent patterns in these delays? In this study, we investigate the recurrence of delay propagation patterns in Japan's domestic air transport network in 2019 by integrating delay causality networks and temporal network analysis. Additionally, we examine characteristics unique to delay propagation by comparing delay causality networks with corresponding randomized networks generated by a directed configuration model. As a result, we found that the structure of the delay propagation patterns can be classified into several groups. The identified groups exhibit statistically significant differences in total delay time and average out-degree, with different airports playing central roles in spreading delays. The results also suggest that some delay propagation patterns are particularly prominent during specific times of the year, which could be influenced by Japan's seasonal and geographical factors. Moreover, we discovered that specific network motifs appear significantly more (or less) frequently in delay causality networks than their corresponding randomized counterparts. This characteristic is particularly pronounced in groups with more significant delays. These results suggest that delays propagate following specific directional patterns, which could significantly contribute to predicting air traffic delays. We expect the present study to trigger further research on recurrent and non-recurrent natures of air traffic delay propagation.

physics.soc-ph↗