Correlating USA COVID-19 cases at epidemic onset days to domestic flights passenger inflows by state

Ruiz-Gayosso, J. A.; Castillo-Escribano, M. del; Hernández-Ramírez, E.; Castillo-Mussot, Marcelo del; Pérez-Riascos, A.; Hernández-Casildo, J.

doi:10.1142/s0129183121500145

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…The computational complexity of the proposed algorithm can be estimated by considering the time and space requirements of its individual components. The link prediction strategy, while applied on a graph with n nodes in k clusters, is likely to have a time complexity of 𝑂(𝑛( ℎ 𝑘 ) 2 ), where h the is average degree of nodes. This is because the strategy involves calculating pairwise similarities between nodes of each cluster.…”

Section: B Performance Of the Proposed Flight Recommendation Methodsmentioning

confidence: 99%

“…Today, airplanes are widely used as one of the main means of transportation for domestic and international travel, and the number of passengers in the aviation transportation system has been steadily increasing in recent years [1]. The number of passengers transported by the aviation industry has grown by 137% from 2004 to 2020, and this upward trend is predicted to continue in the coming years [2]. Providing satisfactory services to passengers and ensuring their satisfaction is one of the primary goals of airline companies, and appropriate flight recommendations can play a crucial role in achieving this objective [3].…”

Section: Introductionmentioning

confidence: 99%

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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: 99%

Section: Introductionmentioning

confidence: 99%

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

Kan,

Wong,

Chau

2024

IEEE Access

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Contagion Modeling and Simulation in Transport and Air Travel Networks During the COVID-19 Pandemic: A Survey

2021

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The COVID-19 pandemic has generated a huge volume of research from various disciplines, such as health sciences, social sciences, mathematical modeling, social network analysis, complex systems, decision-making processes, computer simulation, economics, among many others. One of the key problems has been to understand the diffusion processes of the virus, which quickly spread worldwide through transport networks, mainly air flights. Almost two years after start the pandemic, it is necessary to collect and synthesize the existing work on this matter. This work focuses on studies related to the COVID-19 contagion simulation through transport networks. In particular, we are specially interested in the different datasets and epidemiological models used. The search methodology consists of four exhaustive searches in Google Scholar carried out between January 2020 and January 2021. Of the 1786 findings, we chose 53 articles related to Covid-19 contagion modeling and simulation through transport networks. The results show 30 different data sources for the collection of air flights and 11 additional sources for maritime and land transport. These datasets are usually complemented with other data sources, local and international, with demographic information, economic data, and statistics of traceability of the pandemic. The findings also found 15 spread models of contagion, with the SEIR model being the most widely used, followed by mathematical-based risk models. This diversity of results validates the need for these types of compilation efforts since researchers do not have a single centralized repository to collect air flight data.

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Machine learning Models to Predict COVID-19 Cases

Ghadah

Tran

Saadati

et al. 2022

2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)

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Correlating USA COVID-19 cases at epidemic onset days to domestic flights passenger inflows by state

Cited by 5 publications

References 12 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

Contagion Modeling and Simulation in Transport and Air Travel Networks During the COVID-19 Pandemic: A Survey

Machine learning Models to Predict COVID-19 Cases

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