A Review on Flight Delay Prediction

Transport Reviews, 2020

Flight delays hurt airlines, airports, and passengers. Their prediction is crucial during the decisionmaking process for all players of commercial aviation. Moreover, the development of accurate prediction models for flight delays became cumbersome due to the complexity of air transportation system, the number of methods for prediction, and the deluge of flight data. In this context, this paper presents a thorough literature review of approaches used to build flight delay prediction models from the Data Science perspective. We propose a taxonomy and summarize the initiatives used to address the flight delay prediction problem, according to scope, data, and computational methods, giving particular attention to an increased usage of machine learning methods. Besides, we also present a timeline of significant works that depicts relationships between flight delay prediction problems and research trends to address them.

Flight delay is a significant problem that negatively impacts the aviation industry and costs billions of dollars each year. Most existing studies investigated this issue using various methods based on applying machine learning methods to predict the flight delay. However, due to the highly dynamic environments of the aviation industry, relying only on single route of airport may not be sufficient and applicable to forecast the future of flights. The purpose of this project is to analyze a broader scope of factors which may potentially influence the flight delay it compares several machine learning-based models in designed generalized flight delay prediction tasks. In this project we have used flight delay dataset from US Department of Transportation (DOT) to predict flight delays. We have used supervised learning algorithms to predict flight departure delay and then model evaluation is done to get best model and our model can identify which features were more important when predicting flight delays.

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2022

Predicting flight delays accurately is essential for building a more effective airline industry. Increasing client happiness is a key component of the airline company. All participants in commercial aviation must consider their prediction while making decisions. Flights are delayed and cause consumer displeasure due to inclement weather, a mechanical issue, and the delayed arrival of the aircraft at the place of departure. With the aid of weather and flight data, a predictive model for flights arriving on time is put forth. In this study, we forecast whether a specific flight's arrival will be delayed or not using machine learning models such Decision Tree Regression, Bayesian Ridge, Random Forest Regression, and Gradient Boosting Regression.

Sakarya University Journal of Science

Delays in flights and other airline operations have significant consequences in quality of service, operational costs, and customer satisfaction. Therefore, it is important to predict the occurrence of delays and take necessary actions accordingly. In this study, we addressed the flight delay prediction problem from a supervised machine learning perspective. Using a realworld airline operations dataset provided by a leading airline company, we identified optimum dataset features for optimum prediction accuracy. In addition, we trained and tested 11 machine learning models on the datasets that we created from the original dataset via feature selection and transformation. CART and KNN showed consistently good performance in almost all cases achieving 0.816 and 0.807 F-Scores respectively. Similarly, GBM, XGB, and LGBM showed very good performance in most of the cases, achieving F-Scores around 0.810.

2021

Flight delay is a major issue in the aviation industry. In commercial aviation, if a flight reaches its destination 15 minutes later than the scheduled arrival, it is said to be delayed. Flight delays cause a great deal of bother to travelers. It could make them late to their booked occasions or miss a corresponding flight, accordingly prompting outrage and dissatisfaction. Likewise, travelers may not generally be entitled for a refund when a postponement happens. Carriers report that couple of the numerous reasons prompting most flight delays are carrier glitches, climate conditions, support issues with the airplane and congestion in air traffic. Rapid development in airline industry has led to an increased number of aircrafts in the skies, this has brought about air-gridlock causing flight delays. Flight delays are not only extremely undesirable financially but also have adverse environmental effects. Air-traffic management is becoming increasingly challenging. The aim of our rese...

2012

Abstract Flight delays are quite frequent (19% of the US domestic flights arrive more than 15 minutes late), and are a major source of frustration and cost for the passengers. As we will see, some flights are more frequently delayed than others, and there is an interest in providing this information to travelers. As delays are a stochastic phenomenon, it is interesting to study their entire probability distributions, instead of looking for an average value.

IRJET, 2021

Flight delay is a significant problem that negatively impacts the aviation industry and costs billions of dollars each year. Most existing studies investigated this issue using various methods based on applying machine learning methods to predict the flight delay. However, due to the highly dynamic environments of the aviation industry, relying only on single route of airport may not be sufficient and applicable to forecast the future of flights. The purpose of this project is to analyze a broader scope of factors which may potentially influence the flight delay it compares several machine learning-based models in designed generalized flight delay prediction tasks. In this project we have used flight delay dataset from US Department of Transportation (DOT) to predict flight delays. We have used supervised learning algorithms to predict flight departure delay and then model evaluation is done to get best model and our model can identify which features were more important when predicting flight delays.

ITM Web of Conferences

The excessive growth of air traffic, with the limited airspace and airports capacity, results in a flight demand-capacity imbalance leading to air traffic delays. This paper explores the factors associated with delay in both microscopic and macroscopic ways. The aim is to develop a model which analyzes and predicts the occurrence of flight arrival delays using US domestic flight data for the year 2018. It will provide passengers, airlines and airport managers with reliable flight arrival schedules, and consequently reduce economic losses and enhance passengers trust. Beside database features, the proposed model is to the best of our knowledge the first attempt to predict flight arrival delays using three new features which are contributive factors to delays: Departure Time and Arrival Time of the day in which the flight was performed (Early morning, late morning, noon, afternoon, evening or night) and model of aircraft. Four Machine Learning classifiers namely Random Forest, Decisio...

IEEE Access

Over the past years, flight delay has been a critical concern in the aviation sector due to the increased air traffic congestion worldwide. Moreover, it also prolongs the other flights, which can discourage users from traveling with the particular airline. As a result, we proposed a model to predict the overall flight delay using a random forest and path-finding algorithm. The proposed model focuses on searching flights (can be nonstop or connecting) between the source and destination at the earliest. The proposed model identifies the fastest flights between source and destination based on the input by the user using some open source/public Application Programming Interface (APIs), which are further inserted into Neo4j to convert it into a JavaScript Object Notation (JSON) format. Finally, the experimental results on the real-time data set show the proposed model's effectiveness compared to the state-of-the-art models. The results and analysis yield an accuracy of 98.2% for delay prediction on historical data using the random forest algorithm. INDEX TERMS Flight delay, flight search, Neo4j, python, random forest.

IRJET, 2022

Many companies rely on different airlines to link them with other regions of the world nowadays since the aviation industry is so important to the global transportation sector. Extreme weather, however, can have a direct impact on the airline. In order to address this problem, reliable forecasting of these flight delays helps travelers to prepare for the disruption to their travel plans and enables airlines to address the causes of the flight delays in advance to lessen the impact. This project's goal is to examine the methods for creating forecasting models for aircraft delays brought on by adverse weather. In the present research, a machine learning flight delay prediction model is established with the help of machine learning ensemble models. Three different machine learning techniques such as Naive Bayes, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) applied to the Airline dataset. To validate the performance and efficiency of the proposed method, a comparative analysis is performed.