A Big Service with Network Represent Learning for Quantified Flight Delay Prediction

2020 ◽  
Author(s):  
Ziyu Guo ◽  
Guangxu Mei ◽  
Lei Bian ◽  
Hongwu Tang ◽  
Diansheng Wang ◽  
...  
Keyword(s):  
Flight Delay ◽  
Big Service ◽  
Delay Prediction

Delay Prediction of Aircrafts Based on Health Monitoring Data

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
B. A. Dattaram ◽  
N. Madhusudanan
Keyword(s):  
Machine Learning ◽  
Health Monitoring ◽  
Monitoring Data ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Flight Delay ◽  
Delay Prediction ◽  
The Relationship

Flight delay is a major issue faced by airline companies. Delay in the aircraft take off can lead to penalty and extra payment to airport authorities leading to revenue loss. The causes for delays can be weather, traffic queues or component issues. In this paper, we focus on the problem of delays due to component issues in the aircraft. In particular, this paper explores the analysis of aircraft delays based on health monitoring data from the aircraft. This paper analyzes and establishes the relationship between health monitoring data and the delay of the aircrafts using exploratory analytics, stochastic approaches and machine learning techniques.

scholarly journals Probabilistic Flight Delay Predictions Using Machine Learning and Applications to the Flight-to-Gate Assignment Problem

Aerospace ◽  
2021 ◽  
Vol 8 (6) ◽  
pp. 152
Author(s):  
Micha Zoutendijk ◽  
Mihaela Mitici
Keyword(s):  
Assignment Problem ◽  
Absolute Error ◽  
Aviation Industry ◽  
Probabilistic Forecasting ◽  
Flight Delays ◽  
Mixture Density ◽  
Assignment Model ◽  
Gate Assignment ◽  
Flight Delay ◽  
Delay Prediction

The problem of flight delay prediction is approached most often by predicting a delay class or value. However, the aviation industry can benefit greatly from probabilistic delay predictions on an individual flight basis, as these give insight into the uncertainty of the delay predictions. Therefore, in this study, two probabilistic forecasting algorithms, Mixture Density Networks and Random Forest regression, are applied to predict flight delays at a European airport. The algorithms estimate well the distribution of arrival and departure flight delays with a Mean Absolute Error of less than 15 min. To illustrate the utility of the estimated delay distributions, we integrate these probabilistic predictions into a probabilistic flight-to-gate assignment problem. The objective of this problem is to increase the robustness of flight-to-gate assignments. Considering probabilistic delay predictions, our proposed flight-to-gate assignment model reduces the number of conflicted aircraft by up to 74% when compared to a deterministic flight-to-gate assignment model. In general, the results illustrate the utility of considering probabilistic forecasting for robust airport operations’ optimization.

scholarly journals Amsterdam to Dublin Eventually Delayed? LSTM and Transfer Learning for Predicting Delays of Low Cost Airlines

2019 ◽  
Vol 33 ◽  
pp. 9541-9546
Author(s):  
Nicholas McCarthy ◽  
Mohammad Karzand ◽  
Freddy Lecue
Keyword(s):  
Transfer Learning ◽  
Short Term Memory ◽  
Low Cost ◽  
Learning Approaches ◽  
Flight Delays ◽  
Commercial Aviation ◽  
Feature Spaces ◽  
Flight Delay ◽  
Delay Prediction ◽  
Heterogeneous Feature

Flight delays impact airlines, airports and passengers. Delay prediction is crucial during the decision-making process for all players in commercial aviation, and in particular for airlines to meet their on-time performance objectives. Although many machine learning approaches have been experimented with, they fail in (i) predicting delays in minutes with low errors (less than 15 minutes), (ii) being applied to small carriers i.e., low cost companies characterized by a small amount of data. This work presents a Long Short-Term Memory (LSTM) approach to predicting flight delay, modeled as a sequence of flights across multiple airports for a particular aircraft throughout the day. We then suggest a transfer learning approach between heterogeneous feature spaces to train a prediction model for a given smaller airline using the data from another larger airline. Our approach is demonstrated to be robust and accurate for low cost airlines in Europe.

Flight Delay Prediction Based on Aviation Big Data and Machine Learning

2020 ◽  
Vol 69 (1) ◽  
pp. 140-150 ◽  
Author(s):  
Guan Gui ◽  
Fan Liu ◽  
Jinlong Sun ◽  
Jie Yang ◽  
Ziqi Zhou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Flight Delay ◽  
Delay Prediction

scholarly journals Flight Delay Prediction Using Machine Learning

2021 ◽  
pp. 473-476
Author(s):  
Prajwal Dhone ◽  
Uday Kirange ◽  
Rushabh Satarkar ◽  
Prof. Shashant Jaykar
Keyword(s):  
Machine Learning ◽  
Negative Impact ◽  
Weather Conditions ◽  
Current Data ◽  
Environmental Harm ◽  
Flight Delays ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
Flight Delay ◽  
Delay Prediction

In this fast growing world as airplanes continue flying, flight delays are the part of the experience. According to the Bureau Of Statistics(BOS), about 20% of all flights are delayed by 15 minutes or more. Flight delays causes a negative impact, mainly economical for airport authorities, commuters and airline industries as well. Furthermore, in the domain of sustainability, it can even cause environmental harm by the rise in fuel consumption and gas emissions and also some of the important factors including adverse weather conditions, preparing the aircraft, fixing of mechanical issue, getting security clearance, etc. Hence, these are the factors which indicates the necessity it has become to predict the delays of airline problems. To carry out the predictive analysis, which includes a range of statistical techniques from machine learning, this studies historical and current data to make predictions about the future delays, taking help of Regression Analysis using regularization technique used in Python.

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