An Aviation Delay Prediction and Recommendation System Using Machine Learning Techniques

Sirisati, Ranga Swamy; Prasanthi, K.; Latha, A. Gautami

doi:10.1007/978-981-33-6307-6_25

Cited by 2 publications

(6 citation statements)

References 11 publications

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“…This improvement can be attributed to the utilization of both flight information and user characteristics in providing recommendations. The results show that the proposed method, on average, increases accuracy by 13.17% and recall by 16.31% compared to the method of Sirisati et al [8]. Moreover, comparing the results of the proposed method with the LinkPred mode indicates that if the CNN model is not used to adjust the value of recommendations in the recommendation system, the average precision and recall will decrease by 8.99% and 13.21%, respectively.…”

Section: B Performance Of the Proposed Flight Recommendation Methodsmentioning

confidence: 85%

“…The evaluations were performed using the user and flight data from the database. The results of the proposed method were compared with the Sirisati et al [8] method and the LinkPred approach.…”

Section: B Performance Of the Proposed Flight Recommendation Methodsmentioning

confidence: 99%

“…All these flights, which have been ignored, are considered false negatives (FN) and added to the FN set. Using the above results, precision and recall metrics can be calculated based on equations (7) and (8). Figure 7-a shows the changes in precision for the number of recommendations in the proposed method, and Figure 7-b displays the changes in recall for this experiment.…”

Section: B Performance Of the Proposed Flight Recommendation Methodsmentioning

confidence: 99%

“…Table 5 compares the processing time of the proposed method with the method by Sirisati et al [8] and the LinkPred method. As shown in the results presented in Table 5, the proposed method performs faster both in the model building phase and the recommendation phase, providing recommendations to users in a shorter time.…”

Section: B Performance Of the Proposed Flight Recommendation Methodsmentioning

confidence: 99%

“…Many of these approaches have utilized machine learning techniques as the primary tool for providing recommendations. For instance, one study [8] presents a flight recommender system based on machine learning techniques. In this method, existing flights are processed using an improved Naive Bayes model to predict flight delays.…”

Section: Review Of Researchmentioning

confidence: 99%

See 4 more Smart Citations

A Personalized Flight Recommender System Based on Link Prediction in Aviation Data

Kan,

Wong,

Chau

2024

IEEE Access

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Providing suitable flight recommendations to passengers is one of the essential requirements for ensuring customer satisfaction and maintaining a strong relationship with them in airline companies. Determining the appropriateness of a flight for a passenger is a complex issue that can depend on various factors. Factors such as individual preferences, flight quality, and the possibility of flight cancellations or delays need to be simultaneously considered in the process of making appropriate flight recommendations. Additionally, the vast amount of flight data adds to the complexity of this issue. In this article, a personalized flight recommender system is presented to address these challenges. The proposed method utilizes a link prediction strategy to model user profiles and habits, limiting the set of feasible recommendations. Furthermore, a Convolutional Neural Network (CNN) is employed to predict the likelihood of flight cancellations or delays, enabling the system to provide passengers with recommendations that maximize their satisfaction based on this information combined with flight features. To reduce the complexity of handling large flight datasets and increase processing speed, the proposed approach utilizes clustering. In this technique, data is distributed into a set of clusters using the K-Means algorithm, and the recommendation process is based on the cluster with the least distance to the user's features. The performance of the proposed method was tested using real flight data. The experiments evaluated the model's accuracy in predicting flight delays/cancellations and the accuracy of the recommendations it provides. The results demonstrated that the CNN model employed in the proposed method achieved an average accuracy of 95.13% in predicting flight delays/cancellations, showing at least a 2.4% improvement over the compared methods. Additionally, the proposed recommender system reported an accuracy value of 72.31%, surpassing the compared works by 15.6% and indicating its favorable performance in providing accurate recommendations.

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Section: B Performance Of the Proposed Flight Recommendation Methodsmentioning

confidence: 85%

Section: B Performance Of the Proposed Flight Recommendation Methodsmentioning

confidence: 99%

Section: B Performance Of the Proposed Flight Recommendation Methodsmentioning

confidence: 99%

Section: B Performance Of the Proposed Flight Recommendation Methodsmentioning

confidence: 99%

Section: Review Of Researchmentioning

confidence: 99%

See 3 more Smart Citations

A Personalized Flight Recommender System Based on Link Prediction in Aviation Data

Kan,

Wong,

Chau

2024

IEEE Access

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show abstract

Modeling Behavior and Attribute feedback based Flight Recommendation for Dynamic Pricing

Xiao,

Shi,

Fan

et al. 2022

2022 IEEE Smartworld, Ubiquitous Intelligence &Amp; Computing, Scalable Computing &Amp; Communications, Digital Twin, Privacy C

View full text Add to dashboard Cite

An Aviation Delay Prediction and Recommendation System Using Machine Learning Techniques

Cited by 2 publications

References 11 publications

A Personalized Flight Recommender System Based on Link Prediction in Aviation Data

A Personalized Flight Recommender System Based on Link Prediction in Aviation Data

Modeling Behavior and Attribute feedback based Flight Recommendation for Dynamic Pricing

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