Flight Delay Prediction using Hybrid Machine Learning Approach: A Case Study of Major Airlines in the United States

• URL: http://arxiv.org/abs/2409.00607v1
• Date: Sun, 1 Sep 2024 04:18:41 GMT
• Title: Flight Delay Prediction using Hybrid Machine Learning Approach: A Case Study of Major Airlines in the United States
• Authors: Rajesh Kumar Jha, Shashi Bhushan Jha, Vijay Pandey, Radu F. Babiceanu,
• Abstract summary: This research proposes a hybrid approach that combines the feature of deep learning and classic machine learning techniques. Several machine learning algorithms are applied on flight data to validate the results of proposed model. The study also includes an extensive analysis of the flight data and each model to obtain insightful results for U.S. airlines.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Airport Delay Prediction with Temporal Fusion Transformers [24.280246809961945]
  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.
  arXiv  Detail & Related papers  (2024-05-14T03:27:15Z)
• Machine Learning for Pre/Post Flight UAV Rotor Defect Detection Using Vibration Analysis [54.550658461477106]
  Unmanned Aerial Vehicles (UAVs) will be critical infrastructural components of future smart cities. In order to operate efficiently, UAV reliability must be ensured by constant monitoring for faults and failures. This paper leverages signal processing and Machine Learning methods to analyze the data of a comprehensive vibrational analysis to determine the presence of rotor blade defects.
  arXiv  Detail & Related papers  (2024-04-24T13:50:27Z)
• Airport take-off and landing optimization through genetic algorithms [55.2480439325792]
  This research addresses the crucial issue of pollution from aircraft operations, focusing on optimizing both gate allocation and runway scheduling simultaneously. The study presents an innovative genetic algorithm-based method for minimizing pollution from fuel combustion during aircraft take-off and landing at airports.
  arXiv  Detail & Related papers  (2024-02-29T14:53:55Z)
• FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather Forecasting [56.73502043159699]
  This work presents FengWu-GHR, the first data-driven global weather forecasting model running at the 0.09$circ$ horizontal resolution. It introduces a novel approach that opens the door for operating ML-based high-resolution forecasts by inheriting prior knowledge from a low-resolution model. The hindcast of weather prediction in 2022 indicates that FengWu-GHR is superior to the IFS-HRES.
  arXiv  Detail & Related papers  (2024-01-28T13:23:25Z)
• Alexa, Predict My Flight Delay [0.0]
  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.
  arXiv  Detail & Related papers  (2022-08-21T17:01:48Z)
• Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds [96.74836678572582]
  We present a learning-based approach that allows rapid online adaptation by incorporating pretrained representations through deep learning. Neural-Fly achieves precise flight control with substantially smaller tracking error than state-of-the-art nonlinear and adaptive controllers.
  arXiv  Detail & Related papers  (2022-05-13T21:55:28Z)
• Phased Flight Trajectory Prediction with Deep Learning [8.898269198985576]
  The unprecedented increase of commercial airlines and private jets over the past ten years presents a challenge for air traffic control. Precise flight trajectory prediction is of great significance in air transportation management, which contributes to the decision-making for safe and orderly flights. We propose a phased flight trajectory prediction framework that can outperform state-of-the-art methods for flight trajectory prediction for large passenger/transport airplanes.
  arXiv  Detail & Related papers  (2022-03-17T02:16:02Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• Spatio-Temporal Data Mining for Aviation Delay Prediction [15.621546618044173]
  We present a novel aviation delay prediction system based on stacked Long Short-Term Memory (LSTM) networks for commercial flights. The system learns from historical trajectories from automatic dependent surveillance-broadcast (ADS-B) messages. Compared with previous schemes, our approach is demonstrated to be more robust and accurate for large hub airports.
  arXiv  Detail & Related papers  (2021-03-20T18:37:06Z)
• T$^2$-Net: A Semi-supervised Deep Model for Turbulence Forecasting [65.498967509424]
  Air turbulence forecasting can help airlines avoid hazardous turbulence, guide routes that keep passengers safe, maximize efficiency, reduce costs. Traditional forecasting approaches rely on painstakingly customized turbulence indexes, which are less effective in dynamic and complex weather conditions. We propose a machine learning based turbulence forecasting system due to two challenges: (1) Complex-temporal correlations, and (2) scarcity, very limited turbulence labels can be obtained.
  arXiv  Detail & Related papers  (2020-10-26T21:14:15Z)
• Flight Time Prediction for Fuel Loading Decisions with a Deep Learning Approach [3.285168337194676]
  Airlines are constantly seeking new technologies and optimizing flight operations to reduce fuel consumption. Excess fuel is loaded by dispatchers and (or) pilots to handle fuel consumption uncertainties. We develop a novel spatial weighted recurrent neural network model to provide better flight time predictions.
  arXiv  Detail & Related papers  (2020-05-12T11:05:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.