No abstract

As indicated by current literature, service at small community airports was negatively affected by the Great Recession from 2007 to 2009 and recent changes in competition structure. Existing studies have looked at the recession’s lingering impact on small community airports (e.g., hub premiums, airport dominance, connectivity) and markets (e.g., market competition structure). However, to date it has been difficult to determine which factors contribute to a market’s potential future loss of service that serves a small community. In this study, we identified characteristics that could potentially contribute to a market’s loss or gain of service by incorporating different regional- and market-specific characteristics that have evolved over the years. This study used a fixed-effects conditional logistic regression and focused on region-to-region markets serving small communities that were in service at least once between 2007 and 2013. In total, the panel data included 1,367 markets departing from a small region and arriving at a small-, medium-, or large-sized region with 453 markets adding or losing service during that time. Fixed-effects were used to identify the impact of within-market variation on service loss over the years. Results showed that, first, markets affected by a merger were indeed at a higher risk of losing service. Second, markets operated by a fuel-intensive, small-aircraft fleet had a higher chance of being discontinued. Third, an increased number of competitors greatly reduced potential market service loss.

The International Civil Aviation Organization identifies departure and arrival punctuality as on-time key performance indicators. However, these metrics assume a flight’s delay is a result of either the origin or destination airport, providing limited information on where delay should be mitigated in the U.S. National Airspace System (NAS). This study evaluates the relationship between delay propagation magnitude, delay causal factor, airport size, and charged facility (airport or Air Route Traffic Control Center), to examine if certain delays take longer to dissipate. First, using flights from July 2018, results show that most delay propagation chains originate at large-hub airports. However, these delays were the quickest to recover. Second, this study presents a regression model, predicting total propagated delay using fixed effects based on the weather region where the original delay occurred. Each additional flight affected by downstream delay adds 18.7 min on average to total arrival delay in a propagation chain. Additionally, if weather was the original causal factor, total propagated delay increased by 11.6 min compared with non-weather delays. Lastly, this study compares delay propagation in July 2018 and July 2020. Results show uneven impacts of the coronavirus disease 2019 (COVID-19) across the large-hub airports. Some of the investigated airports did not witness large improvements in average delay per delayed flight, warranting further research in the future. While delay and delay propagation have not been completely eradicated in the NAS during the COVID-19 pandemic, findings suggest that both have significantly declined on average.