Flight delays are a significant challenge in the aviation industry, causing major financial and operational disruptions. To improve passenger experience and reduce revenue loss, flight delay prediction models must be both precise and generalizable across different networks. This paper introduces a novel approach that combines Queue-Theory with a simple attention model, referred to as the Queue-Theory SimAM (QT-SimAM). To validate our model, we used data from the US Bureau of Transportation Statistics, where our proposed QT-SimAM (Bidirectional) model outperformed existing methods with an accuracy of 0.927 and an F1 score of 0.932. To assess transferability, we tested the model on the EUROCONTROL dataset. The results demonstrated strong performance, achieving an accuracy of 0.826 and an F1 score of 0.791. Ultimately, this paper outlines an effective, end-to-end methodology for predicting flight delays. The proposed model's ability to forecast delays with high accuracy across different networks can help reduce passenger anxiety and improve operational decision-making

The aviation industry has experienced constant growth in air traffic since the deregulation of the U.S. airline industry in 1978. As a result, flight delays have become a major concern for airlines and passengers, leading to significant research on factors affecting flight delays such as departure, arrival, and total delays. Flight delays result in increased consumption of limited resources such as fuel, labor, and capital, and are expected to increase in the coming decades. To address the flight delay problem, this research proposes a hybrid approach that combines the feature of deep learning and classic machine learning techniques. In addition, several machine learning algorithms are applied on flight data to validate the results of proposed model. To measure the performance of the model, accuracy, precision, recall, and F1-score are calculated, and ROC and AUC curves are generated. The study also includes an extensive analysis of the flight data and each model to obtain insightful results for U.S. airlines.

Since flight delay hurts passengers, airlines, and airports, its prediction becomes crucial for the decision-making of all stakeholders in the aviation industry and thus has been attempted by various previous research. However, previous delay predictions are often categorical and at a highly aggregated level. To improve that, this study proposes to apply the novel Temporal Fusion Transformer model and predict numerical airport arrival delays at quarter hour level for U.S. top 30 airports. Inputs to our model include airport demand and capacity forecasts, historic airport operation efficiency information, airport wind and visibility conditions, as well as enroute weather and traffic conditions. The results show that our model achieves satisfactory performance measured by small prediction errors on the test set. In addition, the interpretability analysis of the model outputs identifies the important input factors for delay prediction.

This paper addresses aircraft delays, emphasizing their impact on safety and financial losses. To mitigate these issues, an innovative machine learning (ML)-enhanced landing scheduling methodology is proposed, aiming to improve automation and safety. Analyzing flight arrival delay scenarios reveals strong multimodal distributions and clusters in arrival flight time durations. A multi-stage conditional ML predictor enhances separation time prediction based on flight events. ML predictions are then integrated as safety constraints in a time-constrained traveling salesman problem formulation, solved using mixed-integer linear programming (MILP). Historical flight recordings and model predictions address uncertainties between successive flights, ensuring reliability. The proposed method is validated using real-world data from the Atlanta Air Route Traffic Control Center (ARTCC ZTL). Case studies demonstrate an average 17.2% reduction in total landing time compared to the First-Come-First-Served (FCFS) rule. Unlike FCFS, the proposed methodology considers uncertainties, instilling confidence in scheduling. The study concludes with remarks and outlines future research directions.

Today's consumers want a forum to voice their frustrations with air travel because they tend to feel unheard in the heat of the moment. Emerging technology and crowdsourced data could provide them a means of reporting trip outcomes that would allow other travelers to make smarter decisions about which airline to choose before they book flights. The data could also provide airlines with insights they don't already have. In short, crowdsourced data, analyzed and verified by AI could positively impact the future of air travel. Rating systems use rudimentary data: typically, one to four or five stars, which range from awful to excellent.

Artificial intelligence (AI) in aviation has diverse applications, from reducing flight delays to increasing jet fuel efficiency. Leading airline companies are already prototyping and testing AI applications to increase customer satisfaction and improve operational performance. Air travel passengers are projected to reach 4 billion in 2024, exceeding pre-COVID-19 levels, according to the International Air Transport Association. To deal with such a huge number of passengers, airlines need to innovate and integrate with emerging technologies like AI and machine learning. AI in aviation has the potential to increase urban air mobility, improve airline safety, automate flight scheduling, and enable predictive maintenance of airplanes.

Airlines are critical today for carrying people and commodities on time. Any delay in the schedule of these planes can potentially disrupt the business and trade of thousands of employees at any given time. Therefore, precise flight delay prediction is beneficial for the aviation industry and passenger travel. Recent research has focused on using artificial intelligence algorithms to predict the possibility of flight delays. Earlier prediction algorithms were designed for a specific air route or airfield. Many present flight delay prediction algorithms rely on tiny samples and are challenging to understand, allowing almost no room for machine learning implementation. This research study develops a flight delay prediction system by analyzing data from domestic flights inside the United States of America. The proposed models learn about the factors that cause flight delays and cancellations and the link between departure and arrival delays.

To model and forecast flight delays accurately, it is crucial to harness various vehicle trajectory and contextual sensor data on airport tarmac areas. These heterogeneous sensor data, if modelled correctly, can be used to generate a situational awareness map. Existing techniques apply traditional supervised learning methods onto historical data, contextual features and route information among different airports to predict flight delay are inaccurate and only predict arrival delay but not departure delay, which is essential to airlines. In this paper, we propose a vision-based solution to achieve a high forecasting accuracy, applicable to the airport. Our solution leverages a snapshot of the airport situational awareness map, which contains various trajectories of aircraft and contextual features such as weather and airline schedules. We propose an end-to-end deep learning architecture, TrajCNN, which captures both the spatial and temporal information from the situational awareness map. Additionally, we reveal that the situational awareness map of the airport has a vital impact on estimating flight departure delay. Our proposed framework obtained a good result (around 18 minutes error) for predicting flight departure delay at Los Angeles International Airport.

The use of AI (Artificial Intelligence) technology in commercial aviation has brought some significant changes in the way flights are being operated today. The world's leading airline service providers are now using AI tools and technologies to deliver a more personalized traveling experience to their customers. From building AI-powered airport kiosks to using it for automating airline operations and security checking, AI will play even more critical roles in the aviation industry. The International Air Transport Association (IATA) and Airports Council International (ACI) have noticed AI's value too. Here, a look at how airlines are currently using AI, and emerging areas that show promising results for a better travel experience.

The Big Data analytics are a logical analysis of very large scale datasets. The data analysis enhances an organization and improve the decision making process. In this article, we present Airline Delay Analysis and Prediction to analyze airline datasets with the combination of weather dataset. In this research work, we consider various attributes to analyze flight delay, for example, day-wise, airline-wise, cloud cover, temperature, etc. Moreover, we present rigorous experiments on various machine learning model to predict correctly the delay of a flight, namely, logistic regression with L2 regularization, Gaussian Naive Bayes, K-Nearest Neighbors, Decision Tree classifier and Random forest model. The accuracy of the Random Forest model is 82% with a delay threshold of 15 minutes of flight delay. The analysis is carried out using dataset from 1987 to 2008, the training is conducted with dataset from 2000 to 2007 and validated prediction result using 2008 data. Moreover, we have got recall 99% in the Random Forest model.