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Flight delay identification is an important way to coordinate the operation time of airport ground service providers and improve the efficiency of airport operations. By analyzing the flight turnaround operation process, considering the randomness and synchronization of the turnaround process, and using Colored Petri Nets and Python (4.0.1), we explore the correlation between various links in the flight turnaround process and the take-off delay at the next station. This paper is committed to improving the service performance of airports and airlines, dynamically predicting flight delays, and providing guidance for avoiding excessive time in the actual operation of bad combinations. The results show that there are six kinds of bad combinations in the departure slip-out link, which is the most likely to affect the transit time. The maximum lifting degree in the bad combination is 2.043, and the maximum average delay time in the bad combination is 22.5 min. When the combination of passenger boarding and departure slip-out time is too long, it has a great positive correlation with delay. When the other links are in a state of being able to pass the station on time, the departure time and baggage loading and unloading are the two links that most affect the flight delay value. [ABSTRACT FROM AUTHOR]

Over-capacity flight scheduling by commercial airlines due to the surging demand in recent years creates congestion and significant delays at major airports. This attitude towards maximizing throughput calls for tactical flight rescheduling to comply with airports' capacity limitations and distribute the peak hour demand over the course of a day. Such displacements of flights may cause significant problems and costs for airlines and some cancellations or missed connections for passengers. This paper presents an optimization model for flight rescheduling at a schedule-coordinated airport to minimize congestion and flight delays at peak hours. The optimization model is used to make better scheduling intervention decisions considering airport resource constraints and safety of operation. A simulation algorithm is also developed to replicate arrival and departure processes in such an airport. The simulation adheres to a first come first served (FCFS) discipline and enforces runway capacity constraints and minimum turnaround times. We compare the delays caused by an ad hoc FCFS operation with those of the optimization model. Computational results from a case study demonstrate that a reduction of 52.6% and 61% in total delay times for arrival and departure flights, respectively, can be achieved. The optimization model also facilitates the implementation of a collaborative decision-making system for better coordination of airport traffic flow management with commercial airlines. [ABSTRACT FROM AUTHOR]

To understand the impact of competition on organizational service reliability decisions, this study investigates whether firms in the airline industry consider competitors' actions when making their service reliability decisions. Using data from the U.S. Bureau of Transportation Statistics on flight cancellation rates and average length of flight delays, the authors use two complementary approaches, a simultaneous equation model and a discrete game framework, to examine competitive influence on firm decisions on the level of service reliability. The authors find that competitive effects are asymmetric and differ by the type of firm and its competitors—full-service versus low-cost airlines—as well by level of market concentration. The authors show that internal initiatives, such as on-time bonuses, can substantially improve service reliability but require the firm to account for competitive reactions. Ignoring competitive effects leads to an overestimation of the impact of these programs on service reliability levels. [ABSTRACT FROM AUTHOR]

Currently, flight delays are common and they propagate from an originating flight to connecting flights, leading to large disruptions in the overall schedule. These disruptions cause massive economic losses, affect airlines’ reputations, waste passengers’ time and money, and directly impact the environment. This study adopts a network science approach for solving the delay propagation problem by modeling and analyzing the flight schedules and historical operational data of an airline. We aim to determine the most disruptive airports, flights, flight-connections, and connection types in an airline network. Disruptive elements are influential or critical entities in an airline network. They are the elements that can either cause (airline schedules) or have caused (historical data) the largest disturbances in the network. An airline can improve its operations by avoiding delays caused by the most disruptive elements. The proposed network science approach for disruptive element analysis was validated using a case study of an operating airline. The analysis indicates that potential disruptive elements in a schedule of an airline are also actual disruptive elements in the historical data and they should be considered to improve operations. The airline network exhibits small-world effects and delays can propagate to any part of the network with a minimum of four delayed flights. Finally, we observed that passenger connections between flights are the most disruptive connection type. Therefore, the proposed methodology provides a tool for airlines to build robust flight schedules that reduce delays and propagation.

We developed a delay propagation model for an airline network using a Gaussian network approach. By analyzing US flight data in a case study, we evaluated the effects of delay propagation within an airline network. The model has several advantages, such as accounting for non-independent and identically distributed delay profiles, providing a more accurate representation of the observed delay propagation process, and identifying weak links in an airline network. Various scenario studies of delay propagation revealed that the Gaussian network model could capture the effect of delay propagation more precisely than previous studies. Moreover, the Gaussian network model could identify weak links through a statistical approach in an airline network. These perspectives may be valuable in developing approaches for managing the delay propagation and alleviating its subsequent effect. [ABSTRACT FROM AUTHOR]

Under EU Air Passenger Rights legislation ("EC261"), carriers must provide assistance and cash compensation to passengers in case of long delay. We study whether the regulation reduces flight delay. EC261 applies uniformly to flights departing from the EU, but covers only EU carriers on EU-bound flights. Exploiting this variation, we find that regulated flights are 5% more likely to arrive on time, and mean arrival delay is reduced by almost four minutes. The effect is strongest on routes with little competition, and for legacy carriers. Thus, consumer rights can improve quality when incentives from competition are weak. • We examine the impact of EU Air Passenger Rights (EC261) on flight delay reduction. • We analyze flight data and exploit the variation in the regulation's coverage. • We show that regulated flights experience significantly shorter delays • The effect is strongest on routes with limited competition. [ABSTRACT FROM AUTHOR]

Over-capacity flight scheduling by commercial airlines due to the surging demand in recent years creates congestion and significant delays at major airports. This attitude towards maximizing throughput calls for tactical flight rescheduling to comply with airports’ capacity limitations and distribute the peak hour demand over the course of a day. Such displacements of flights may cause significant problems and costs for airlines and some cancellations or missed connections for passengers. This paper presents an optimization model for flight rescheduling at a schedule-coordinated airport to minimize congestion and flight delays at peak hours. The optimization model is used to make better scheduling intervention decisions considering airport resource constraints and safety of operation. A simulation algorithm is also developed to replicate arrival and departure processes in such an airport. The simulation adheres to a first come first served (FCFS) discipline and enforces runway capacity constraints and minimum turnaround times. We compare the delays caused by an ad hoc FCFS operation with those of the optimization model. Computational results from a case study demonstrate that a reduction of 52.6% and 61% in total delay times for arrival and departure flights, respectively, can be achieved. The optimization model also facilitates the implementation of a collaborative decision-making system for better coordination of airport traffic flow management with commercial airlines.

Flight delay identification is an important way to coordinate the operation time of airport ground service providers and improve the efficiency of airport operations. By analyzing the flight turnaround operation process, considering the randomness and synchronization of the turnaround process, and using Colored Petri Nets and Python (4.0.1), we explore the correlation between various links in the flight turnaround process and the take-off delay at the next station. This paper is committed to improving the service performance of airports and airlines, dynamically predicting flight delays, and providing guidance for avoiding excessive time in the actual operation of bad combinations. The results show that there are six kinds of bad combinations in the departure slip-out link, which is the most likely to affect the transit time. The maximum lifting degree in the bad combination is 2.043, and the maximum average delay time in the bad combination is 22.5 min. When the combination of passenger boarding and departure slip-out time is too long, it has a great positive correlation with delay. When the other links are in a state of being able to pass the station on time, the departure time and baggage loading and unloading are the two links that most affect the flight delay value.

Point-to-point airlines through hub airports constitutes the trunk network system, which plays a key role in the whole air traffic organization. Based on the panel data of 2012 and 2018, this paper constructs a series of indicators set, aiming to explain the impact of airline network on the spatio-temporal distribution of flight delays and forecast its change trend with a seven- year comparison. The results are as follows: ①Airline network is the external basis of spatiotemporal distribution of flight delays, and the cumulative effect and the peak effect of delays are caused by the two components of airlines and airports, respectively. The high delay rate of hub airports cannot be completely attributed to the poor airport construction and air traffic control, which is closely related to the airline network, including the strength and structure of network connection. ②The degree of airline connection in China is more than the degree of aviation demand, and its oversaturation state is obvious and mainly exists between the composite hub airports. The intra-day delay frequency changes from a low value and increases continuously until the end. The overlap of peak hours forms delay contagion and spread, and produces the cumulative effect of flight delay. ③The high node degree and intermediary center of hub airports lead to the low degree of agglomeration and average path length index of the overall route network. The central advantages and bridging functions of hub airports lead to an oligarchic structure of network connection, resulting in the top effect of flight delay. ④The pattern of China's airline network is relatively fixed in the past seven years, and it is still facing great challenges to reduce flight delays. Optimizing air traffic functions and implementing flow regulation are an important direction of airspace reform. [ABSTRACT FROM AUTHOR]

In modern business, Artificial Intelligence (AI) and Machine Learning (ML) have affected strategy and decision-making positively in the form of predictive modeling. This study aims to use ML and AI to predict arrival flight delays in the United States airport network. Flight delays carry severe social, environmental, and economic impacts. Deploying ML models during the process of operational decision-making can help to reduce the impact of these delays. A literature review and critical appraisal were carried out on previous studies and research relating to flight delay prediction. In the literature review, the datasets used, selected features, selected algorithms, and evaluation tools used in previous studies were analyzed and influenced the decisions made in the methodology for this study. Data for this study comes from two public sets of domestic flight and weather data from 2017. Data are processed and split into training, validation, and testing data. Subsequently, these ML models are evaluated and compared based on performance metrics obtained using the testing data. The predictive model with the best performance (in choosing between logistic regression, random forest, the gradient boosting machine, and feed-forward neural networks) is the gradient boosting machine. [ABSTRACT FROM AUTHOR]

Flight delays impose challenges that impact any flight transportation system-predicting when they will occur in a meaningful way to mitigate this issue. However, the distribution of the flight delay system variables changes over time. This phenomenon is known in predictive analytics as concept drift. This paper investigates the prediction performance of different drift handling strategies in aviation under different scales (models trained from flights related to a single airport or the entire flight system). Specifically, two research questions were proposed and answered: (1) how do drift handling strategies influence the prediction performance of delays? (2) Do different scales change the results of drift handling strategies? In our analysis, drift handling strategies are relevant, and their impacts vary according to scale and machine learning models. [ABSTRACT FROM AUTHOR]

Flight delays negatively impact costs, customer satisfaction, and revenue in the aviation industry. As a result, it is critical to identify the factors that cause flight delays for each airport, as they can vary depending on various attributes associated with their operations. This study proposes an explainable artificial intelligence (xAI) methodology for identifying the features that affect airport delays by integrating data from multiple sources and implementing explainable artificial intelligence. The methodology incorporates operational data, airport information, geographic data, and weather data combined and used to train a series of machine learning models. Furthermore, the SHAP and Sobol techniques are used to thoroughly analyze the features that influence flight delays for the specific case of the airport in Santiago, Chile. The results show that a linear discriminant analysis model is best suited for predicting flight delays in this specific case study, and the features that have the most significant impact on delays are the international flight status, average temperature at the destination airport, wind speed, and average temperature at Santiago airport. The proposed methodology could be applied by airlines that can collect data from multiple sources and conduct similar investigations, leading to the development of a decision support system to make better-informed decisions and reduce the impact of flight delays. • Explainable artificial intelligence (xAI) provides a better understanding of data. • Features that affect airport delays are identified by xAI algorithms. • Data from multiple sources is used for predicting flight delays. • Logistic regression model is best suited for predicting flight delays. • xAI can guide decision-making processes by utilizing SHAP and Sobol techniques. [ABSTRACT FROM AUTHOR]

In modern business, Artificial Intelligence (AI) and Machine Learning (ML) have affected strategy and decision-making positively in the form of predictive modeling. This study aims to use ML and AI to predict arrival flight delays in the United States airport network. Flight delays carry severe social, environmental, and economic impacts. Deploying ML models during the process of operational decision-making can help to reduce the impact of these delays. A literature review and critical appraisal were carried out on previous studies and research relating to flight delay prediction. In the literature review, the datasets used, selected features, selected algorithms, and evaluation tools used in previous studies were analyzed and influenced the decisions made in the methodology for this study. Data for this study comes from two public sets of domestic flight and weather data from 2017. Data are processed and split into training, validation, and testing data. Subsequently, these ML models are evaluated and compared based on performance metrics obtained using the testing data. The predictive model with the best performance (in choosing between logistic regression, random forest, the gradient boosting machine, and feed-forward neural networks) is the gradient boosting machine.