Flight delay is a major problem in the aviation sector. During the last two decades, the growth of the aviation sector has caused air traffic congestion, which has caused flight delays. Flight delays result not only in the loss of fortune also negatively impact the environment. Flight delays also cause significant losses for airlines operating commercial flights. Therefore, they do everything possible in the prevention or avoidance of delays and cancellations of flights by taking some measures. In this paper, using machine learning models such as Logistic Regression, Decision Tree Regression, Bayesian Ridge, Random Forest Regression, and Gradient Boosting Regression we predict whether the arrival of a particular flight will be delayed or not.
- Flight Prediction,
- Machine Learning,
- Error Calculation,
- Logistic Regression,
- Decision Tree,
- Bayesian Ridge,
- Random Forest,
- Gradient Boosting,
- U.S. Flight data
- Regression tree analysis,
- Machine learning,
- Bayes methods,
Flight delay is studied vigorously in various research in recent years. The growing demand for air travel has led to an increase in flight delays. According to the Federal Aviation Administration (FAA), the aviation industry loses more than $3 billion in a year due to flight delays  and, as per BTS , in 2016 there were 860,646 arrival delays. The reasons for the delay of commercial scheduled flights are air traffic congestion, passengers increasing per year, maintenance and safety problems, adverse weather conditions, the late arrival of plane to be used for the next flight  . In the United States, the FAA believes that a flight is delayed when the scheduled and actual arrival times differs by more than 15 minutes. Since it becomes a serious problem in the United States, analysis and prediction of flight delays are being studied to reduce large costs.
Much research has been done on studying flight delays. The prediction, analysis, and cause of flight delays have been a major problem for air traffic control, decision-making by airlines, and ground delay response programs. Studies are conducted on the delay propagation of the sequence. Also, studying the predictive model of arrival delay and departure delay with meteorological features is encouraged. In the past, researchers have tried to predict flight delays with Machine Learning. Chakrabarty et al.  used supervised automatic learning algorithms (random forest, Gradient Boosting Classifier, Support Vector Machine, and the k-nearest neighbor algorithm) to predict delays in the arrival of operated flights including the five busiest US airports. The maximum precision achieved was 79.7% with a gradient booster as a classifier with a limited data set. Choi et al.  applied machine learning algorithms like decision tree, random forest, AdaBoost, and K-nearest Neighbours to predict delays on individual flights. Flight schedule data and weather forecasts have been incorporated into the model. Sampling techniques were used to balance the data and it was observed that the accuracy of the classifier trained without sampling was more that of the trained classifier with sampling techniques. Cao et al.  used a Bayesian Network model to analyze the turnaround time of a flight and delay prediction.
Juan José Rebollo and Hamsa Balakrishnan  used a hundred pairs of origin and destination to summarise the result of various regression and classification models. The find outs reveal that among all the methods used, the random forest has the highest performance. However, predictability may additionally range because of factors such as the number of origin-destination pairs and the forecast horizon. Sruti Oza, Somya Sharma  used multiple linear regression to predict weather-induced flight delays in flight-data, as well as climatic factors and probabilities due to weather delays. The forecasts were based on some key attributes, such as carrier, departure time, arrival time, origin, and destination. Anish M. Kalliguddi and Aera K. Leboulluec  predicted both departure and arrival delays using regression models such as Decision Tree Regressor, Multiple Linear Regression, and Random Forest Regressor in flight-data. It has been observed that the longer forecast horizon is useful for increasing the accuracy with a minimum forecast error for random forests. Etani J Big Data  A supervised model of on-schedule arrival fight is used using weather data and flight data. The relationship between flight data and pressure patterns of Peach Aviation is found. On-Schedule arrival flight is predicted with 77% accuracy using Random Forest as a Classifier.
learning algorithms were applied progressively and successively to predict flight arrival & delay. We built five models out of this. We saw for each evaluation metric considered the values of the models and compared them. We found out that: – In Departure Delay, Random Forest Regressor was observed as the best model with Mean Squared Error 2261.8 and Mean Absolute Error 24.1, which are the minimum value found in these respective metrics. In Arrival Delay, Random Forest Regressor was the best model observed with Mean Squared Error 3019.3 and Mean Absolute Error 30.8, which are the minimum value found in these respective metrics. In the rest of the metrics, the value of the error of Random Forest Regressor although is not minimum but still gives a low value comparatively. In maximum metrics, we found out that Random Forest Regressor gives us the best value and thus should be the model selected. The future scope of this paper can include the application of more advanced, modern, and innovative preprocessing techniques, automated hybrid learning, and sampling algorithms, and deep learning models adjusted to achieve better performance. To evolve a predictive model, additional variables can be introduced. e.g., a model where meteorological statistics are utilized in developing error-free models for flight delays. In this paper we used data from the US only, therefore in the future, the model can be trained with data from other countries as well. With the use of models that are complex and hybrid of many other models provided with appropriate processing power and with the use of larger detailed datasets, more accurate predictive models can be developed. Additionally, the model can be configured for other airports to predict their flight delays as well, and for that data from these airports would be required to incorporate into this research.
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FULL Paper PDF file:Predicting Flight Delays with Error Calculation using Machine Learned Classifiers
P. Meel, M. Singhal, M. Tanwar and N. Saini,
Predicting Flight Delays with Error Calculation using Machine Learned Classifiers
2020 7th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, 2020, pp. 71-76
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