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Flight on-time performance is one of the most important issues in the National Airspace System, a very complex and dynamic system. To avoid negative impacts to the aviation industry, the Federal Aviation Administration has set a long-term objective of understanding and mitigating flight delays. Building an effective and accurate prediction model of flight-delay incidents will help airport executives make the best decisions in delay scenarios. This article utilized two advanced prediction methods to predict the probability of a flight-delay incident--data mining using the decision tree and data mining using Bayesian inference. Prediction models were built using flight on-time performance data collected from different sources. The results indicated important airport-related factors and their effects on the flight on-time performance. [ABSTRACT FROM AUTHOR]

Bad weather, mechanical failures, air control, and crew members of the discomfort health are very likely to cause flight delays. If these events occur, decision-makers of airport operation must rediscover the flight schedules through reassigning gates to these flights, delaying flights, and canceling flights. Therefore, it is important to study the recovery strategy with the feasibility and the least cost for delayed flights and to improve the airport operation efficiency. In this paper, a mathematical model of gate reassignment based on the objectives of the loss of passengers, airport operating, and airlines, and the most important index of disturbance value of the gate reassignment for delayed flights is constructed. Then, the genetic algorithm (GA) and ant colony optimization (ACO) algorithm are combined in order to propose a two-stage hybrid(GAOTWSH) algorithm, which is used to solve the constructed mathematical model of gate reassignment for delayed flights. The test data from the operations of the one airport is used to simulate and demonstrate the performance of the constructed mathematical model of gate reassignment for irregular flights. The results show that the proposed GAOTWSH algorithm has better optimization performance and the constructed gate reassignment model is feasible and effective. The study provides a new idea and method for irregular flights. [ABSTRACT FROM AUTHOR]

The main aim of the paper was to investigate factors influencing flight delays of a European airline. Besides the identification and analysis of those factors the paper offers possible suggestions on how to eliminate the delays. The research is based on data acquired over the period of time spanning from June to September in 2008 -- 2014. Analysis of contingency tables, including Pearson's chi-squared test, has been used for data processing. The dependencies have been presented in graphical form by using correspondence maps. The proportion of delayed flights reaches approx. 50 % during nearly the entire monitored period only in September the proportion drops to 45 %. Flight delays are most frequently caused by delays of previous flights of the same plane. These previous delayed flights are the main culprit of long delays and the frequency of delay occurrence caused by this reason increases significantly during the day. Longer delays of flights appear also due to technical maintenance or aircraft defects. On the contrary other factors such as operational control and crew duty norms, air traffic control and airport limitations tend to cause rather shorter delays of flights with the air traffic control encountering more problems with coordination of flights in the early morning. The supply and service companies also manage to eliminate long delays. [ABSTRACT FROM AUTHOR]

In this paper we applied data indexing techniques combined with association rules to unveil hidden patterns of flight delays. Considering Brazilian flight data and guided by six research questions related to causes, moments, differences, and relationships between airports and airlines, we evaluated and quantified all attributes that may lead to delays, showing not only the main patterns, but also their chances of occurrence in the entire network, in each airport and airline. We observed that Brazilian flight system has difficulties to recover from previous delays and when operating under adverse meteorological conditions, delays occurrences may increase up to 216%. [ABSTRACT FROM AUTHOR]

• Causal relationship between airline's dominance at airports and delays is examined. • Data at the daily flight level for inward flights arriving at six Spanish airports is used. • Dominant network airlines at the destination have less delays than other airlines. • Dominant low-cost airlines at the destination have more delays than other airlines. • Results are not driven by external causes of delays. Airlines with a dominant position at the destination airports have little competitive pressure to reduce delays, but they might care about the negative effects that delays generate on their own flights. Using detailed daily flight data for six Spanish airports in 2017–2018 including very precise information on the external factors that generate flight delays, we find that flights operated by network airlines (i.e., airlines that operate a hub-and-spoke network) with a large presence at the destination airports have less delays than flights operated by other airlines. This finding is in line with the literature on congestion internalization, which predicts a negative relationship between airlines' dominance at the destination airports and delays. We also show that flights operated by low-cost airlines (i.e. airlines that operate a point-to-point network) with a dominant presence at destination airports are more likely to exhibit delays. This result could be explained by the route configuration of low-cost airlines and by their relative low number of connecting passengers. [ABSTRACT FROM AUTHOR]

In recent years European airspace has become increasingly congested and airlines can now observe that en-route capacity constraints are the fastest growing source of flight delays. In 2010 this source of delay accounted for 19% of all flight delays in Europe and has been increasing with an average yearly rate of 17% from 2005 to 2010. This paper suggests and evaluates an approach to how disruption management can be combined with flight planning in order to create more proactive handling of the kind of disruptions, which are caused by congested airspace. The approach is evaluated using data from a medium size European carrier and estimates a lower bound saving of several million USD. [ABSTRACT FROM AUTHOR]

Flight delays are quite common; however, their causes are quite complicated. Weather, airport scheduling, airline differences, etc. can all impact the on-time performance. In this paper, we set the JFK airport as an example to analyze flight delays first and analyzed changes of the delay rate with different models, and pointed out the influence of time factor for flight delays. At last, we applied these methods to other major airports. In this way, we can use the model to predict the delay rate of each period with a high precision, and provide the information to the passengers. [ABSTRACT FROM AUTHOR]

Between 1960 and 2011, worldwide scheduled passenger air travel grew by more than 7% per year. Most projections suggest that the demand for air travel will continue to increase strongly, by around 5% per year over the coming decades. This strong anticipated growth, however, does not account for any air transport infrastructure constraints. Airport and airspace capacity may not be expanded at a rate sufficiently high to enable the projected demand growth, which may lead to lower than anticipated growth in aviation. This paper describes and applies a recently developed model that predicts airline operational responses to airport capacity constraints. It does this by simulating a Nash best-response game between competing airlines, in which each airline maximizes its profit by adjusting flight frequencies and passenger routing, attempting to gain market share by increasing flight frequencies, but at the cost of the added flights. The model is validated for a network of airports in the United States in 2005 and applied to simulate airline operational changes through 2030 under two capacity-constrained scenarios: frozen 2005 capacity levels on (i) a system-wide scale, and (ii) only at Chicago O׳Hare International, a primary hub airport. Simulated passenger demand, air traffic, flight delays, system CO 2 emissions and Chicago O׳Hare NO x emissions are compared to a case in which airport capacity is expanded according to existing airport expansion plans. The simulation results indicate that the air transport system would adjust operations within a constrained network in such way as to avoid airports with high delays. While most system-wide implications for operations and environmental impact seem to be manageable, local impacts at congested hub-airports may be significant. [ABSTRACT FROM AUTHOR]

Highlights: [•] We compute time- and carrier-specific hubbing concentration indices for US airlines. [•] We quantify the impact of changing hubbing concentration on three delay indicators. [•] Increasing hubbing concentration contributes to longer excess travel times. [•] We find no influence of changing hubbing concentration on delays against schedule. [•] Our results indicate that airlines use buffers to mitigate passenger-perceived delays. [ABSTRACT FROM AUTHOR]