Using Scalable Data Mining for Predicting Flight Delays

Belcastro, Loris; Marozzo, Fabrizio; Talia, Domenico; Trunfio, Paolo

doi:10.1145/2888402

Cited by 74 publications

(46 citation statements)

References 10 publications

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“…As a result, the proposed Random Forest, which is an ensemble learning method, predicted 80.36% of arrival delay. In [8], Belcastro et al predicted the arrival flight delay due to bad weather by Random Forest in MapReduce. This study, which used weather data of NOAA, showed the result of prediction as follows: (1) with a delay threshold of 15 min, an accuracy of 74.2% and 71.8% recall on delayed flights are achieved, and (2) with a threshold of 60 min, the accuracy is 85.8% and the delay recall is 86.9%.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of a predictive model for on-time arrival flight of airliner by discovering correlation between flight and weather data

Etani

2019

J Big Data

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An important business of airlines is to get customer satisfaction. Due to bad weather, a mechanical reason, and the late arrival of the aircraft to the point of departure, flights delay and lead to customer dissatisfaction. A predictive model of on-time arrival flight is proposed with using flight data and weather data. The key research in this paper is to discover the correlation between flight data and weather data. The relation between pressure pattern and flight data of Peach Aviation, which is LCC (low-cost carrier) in Japan, are clarified, and it is found that the sea-level pressures of 3 weather observation spots, which are Wakkanai as the most northern spot, Minami-Torishima as the most eastern spot, and Yonagunijima as the most western spot, can classify the pressure patterns. As a result, on-time arrival fight is predicted at 77% of the accuracy with using Random Forest Classifier of machine learning. Furthermore, feasibility of the predictive model is evaluated by developing a tool of on-time arrival flight prediction.

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Section: Literature Reviewmentioning

confidence: 99%

Development of a predictive model for on-time arrival flight of airliner by discovering correlation between flight and weather data

Etani

2019

J Big Data

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“…The hybrid approach was compared by the most widely used models LR [9], [13] and DT [14], [30], [31], [11], [28] . In addition, it was compared with a benchmark from Kaggle.com to prove its authenticity.…”

Section: Methodsmentioning

confidence: 99%

“…The advanced machine learning techniques and associated data mining tools can help to understand and predict several complex phenomena, the approach is used in enabling businesses and research collaborations alike to make informed decisions flight delay prediction [9]. In addition, every year approximately 20% of airline flights are delayed or canceled mainly due to bad weather, carrier equipment, security or technical airport problems [9], [2]. These delays result in significant cost to both airlines and passengers [9], [2], [1].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, every year approximately 20% of airline flights are delayed or canceled mainly due to bad weather, carrier equipment, security or technical airport problems [9], [2]. These delays result in significant cost to both airlines and passengers [9], [2], [1]. The costs related to the airline company when flight connections are missed, or flight crews and aircraft need to be reallocated due to maintenance problems or crew duty time limits [1], this reallocation reduces the delay propagation effects [3].…”

Section: Introductionmentioning

confidence: 99%

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AHybrid Artificial Intelligence Approach to Predict Flight Delay

Alharbi¹,

Prince²

2020

International Journal of Engineering Research and Technology

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Commercial airlines and the passengers suffer from flight delay. Flight delay causes huge loss for the airlines and unsatisfied passengers. The researchers attempt to solve this problem through prediction extensively by machine learning approach and data mining tools. Accurate and robust performance is still to get through existing models. Our proposed hybrid approach is intended to use the power of machine learning as data mining tool and to predict the delay using classification algorithm of deep learning. An extensive evaluation of the proposed method is carried out by comparing the performance by using two data sets: one is local and the other is benchmark from Kaggle to obtain the best performing classifier. Three predictive models were applied on the datasets: logistic regression, decision tree and the proposed approach. The result shows that the proposed method performed well as comparing to the existing state-ofthe art.

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“…To get accurate prediction models and correctly evaluate them, we used balanced training sets and test sets in which half of the log events end with the purchase of a ticket and half with the abandonment of the platform. For this reason, we used the random under-sampling algorithm [25], which balances class distribution through random discarding of major class tuples as described in [26].…”

Section: Step 4: Prediction Modelmentioning

confidence: 99%

Ticket Sales Prediction and Dynamic Pricing Strategies in Public Transport

Branda

Marozzo

Talia

2020

BDCC

Self Cite

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In recent years, the demand for collective mobility services registered significant growth. In particular, the long-distance coach market underwent an important change in Europe, since FlixBus adopted a dynamic pricing strategy, providing low-cost transport services and an efficient and fast information system. This paper presents a methodology, called DA4PT (Data Analytics for Public Transport), for discovering the factors that influence travelers in booking and purchasing bus tickets. Starting from a set of 3.23 million user-generated event logs of a bus ticketing platform, the methodology shows the correlation rules between booking factors and purchase of tickets. Such rules are then used to train machine learning models for predicting whether a user will buy or not a ticket. The rules are also used to define various dynamic pricing strategies with the purpose of increasing the number of tickets sales on the platform and the related amount of revenues. The methodology reaches an accuracy of 95% in forecasting the purchase of a ticket and a low variance in results. Exploiting a dynamic pricing strategy, DA4PT is able to increase the number of purchased tickets by 6% and the total revenue by 9% by showing the effectiveness of the proposed approach.

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Using Scalable Data Mining for Predicting Flight Delays

Cited by 74 publications

References 10 publications

Development of a predictive model for on-time arrival flight of airliner by discovering correlation between flight and weather data

Development of a predictive model for on-time arrival flight of airliner by discovering correlation between flight and weather data

AHybrid Artificial Intelligence Approach to Predict Flight Delay

Ticket Sales Prediction and Dynamic Pricing Strategies in Public Transport

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