Avaliação de Desvios de Conceitos Reais e Virtuais nos Atrasos de Voos em São Paulo nos Períodos Pré, Intra e Pós-Pandemia
Resumo
Os atrasos nos voos representam desafios significativos para a eficiência operacional e a satisfação dos passageiros. Este estudo investiga a detecção de desvios de conceitos reais e virtuais nos atrasos de voos no principal aeroporto de São Paulo (SBSP) durante os períodos pré, intra e pós-pandemia. Utilizando o modelo Naive Bayes e integrando dados do Voo Regular Ativo (ANAC) com informações meteorológicas da NOAA, o estudo avalia o impacto de diferentes métodos de detecção de desvio de conceito no desempenho preditivo. A análise revela que técnicas de desvio virtual, como KSWIN, foram mais eficazes em contextos estáveis, enquanto métodos básicos, como Passivo e Inativo, se destacaram durante a pandemia. Esses resultados enfatizam a importância de escolher e combinar técnicas de detecção para aprimorar a precisão e a adaptabilidade dos modelos preditivos em diferentes cenários operacionais.
Palavras-chave:
Atrasos de Voos, Classificação, Desvio de Conceito, Desvio Virtual, Desvio Real
Referências
ANAC (2023). The Brazilian National Civil Aviation Agency. Technical report, [link].
Gama, J., Medas, P., Castillo, G., and Rodrigues, P. P. (2004). Learning with drift detection. In Brazilian Symposium on Artificial Intelligence (SBIA), pages 286–295. Springer.
Gama, J., Zliobaite, I., Bifet, A., Pechenizkiy, M., and Bouchachia, A. (2014). A survey on concept drift adaptation. ACM Computing Surveys, 46(4).
Giusti, L., Carvalho, L., Gomes, A. T., Coutinho, R., Soares, J., and Ogasawara, E. (2022). Analyzing flight delay prediction under concept drift. Evolving Systems.
Gui, G., Liu, F., Sun, J., Yang, J., Zhou, Z., and Zhao, D. (2020). Flight delay prediction based on aviation big data and machine learning. IEEE Transactions on Vehicular Technology, 69(1):140 – 150.
Iwashita, A. S. and Papa, J. P. (2019a). An Overview on Concept Drift Learning. IEEE Access, 7:1532 – 1547.
Iwashita, R. and Papa, J. P. (2019b). An Overview on Concept Drift Adaptation. Journal of Artificial Intelligence Research.
Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., and Zhang, G. (2019). Learning under Concept Drift: A Review. IEEE Transactions on Knowledge and Data Engineering, 31(12):2346 – 2363.
Moreira, L., Dantas, C., Oliveira, L., Soares, J., and Ogasawara, E. (2018). On Evaluating Data Preprocessing Methods for Machine Learning Models for Flight Delays. In Proceedings of the International Joint Conference on Neural Networks, volume 2018-July.
NOAA (2023). Climate at a Glance Global Time Series. Technical report, [link].
Raab, C., Heusinger, M., and Schleif, F.-M. (2020). Reactive soft prototype computing for concept drift streams. Neurocomputing.
Rani, S. S., Ali, A. I. A., Marie, A., El-Bannany, M., and Khedr, A. M. (2023). Air Traffic Data Analysis Using Recurrent Neural Network (RNN) Classifier During COVID-19. In Proceedings - 17th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2023, pages 402 – 408.
Sakthithasan, S. and Pears, R. (2016). Capturing recurring concepts using discrete Fourier transform. Concurrency and Computation: Practice and Experience, 28(15):4013 – 4035.
Teixeira, C., Giusti, L., Soares, J., dos Santos, J., Amorim, G., and Ogasawara, E. (2021). Integrated Dataset of Brazilian Flights. In Anais do Brazilian e-Science Workshop (BreSci), pages 89–96. SBC.
Webb, G. I., Hyde, R., Cao, H., Nguyen, H. L., and Petitjean, F. (2016). Characterizing concept drift. Data Mining and Knowledge Discovery, 30(4):964 – 994.
Gama, J., Medas, P., Castillo, G., and Rodrigues, P. P. (2004). Learning with drift detection. In Brazilian Symposium on Artificial Intelligence (SBIA), pages 286–295. Springer.
Gama, J., Zliobaite, I., Bifet, A., Pechenizkiy, M., and Bouchachia, A. (2014). A survey on concept drift adaptation. ACM Computing Surveys, 46(4).
Giusti, L., Carvalho, L., Gomes, A. T., Coutinho, R., Soares, J., and Ogasawara, E. (2022). Analyzing flight delay prediction under concept drift. Evolving Systems.
Gui, G., Liu, F., Sun, J., Yang, J., Zhou, Z., and Zhao, D. (2020). Flight delay prediction based on aviation big data and machine learning. IEEE Transactions on Vehicular Technology, 69(1):140 – 150.
Iwashita, A. S. and Papa, J. P. (2019a). An Overview on Concept Drift Learning. IEEE Access, 7:1532 – 1547.
Iwashita, R. and Papa, J. P. (2019b). An Overview on Concept Drift Adaptation. Journal of Artificial Intelligence Research.
Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., and Zhang, G. (2019). Learning under Concept Drift: A Review. IEEE Transactions on Knowledge and Data Engineering, 31(12):2346 – 2363.
Moreira, L., Dantas, C., Oliveira, L., Soares, J., and Ogasawara, E. (2018). On Evaluating Data Preprocessing Methods for Machine Learning Models for Flight Delays. In Proceedings of the International Joint Conference on Neural Networks, volume 2018-July.
NOAA (2023). Climate at a Glance Global Time Series. Technical report, [link].
Raab, C., Heusinger, M., and Schleif, F.-M. (2020). Reactive soft prototype computing for concept drift streams. Neurocomputing.
Rani, S. S., Ali, A. I. A., Marie, A., El-Bannany, M., and Khedr, A. M. (2023). Air Traffic Data Analysis Using Recurrent Neural Network (RNN) Classifier During COVID-19. In Proceedings - 17th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2023, pages 402 – 408.
Sakthithasan, S. and Pears, R. (2016). Capturing recurring concepts using discrete Fourier transform. Concurrency and Computation: Practice and Experience, 28(15):4013 – 4035.
Teixeira, C., Giusti, L., Soares, J., dos Santos, J., Amorim, G., and Ogasawara, E. (2021). Integrated Dataset of Brazilian Flights. In Anais do Brazilian e-Science Workshop (BreSci), pages 89–96. SBC.
Webb, G. I., Hyde, R., Cao, H., Nguyen, H. L., and Petitjean, F. (2016). Characterizing concept drift. Data Mining and Knowledge Discovery, 30(4):964 – 994.
Publicado
14/10/2024
Como Citar
SANTOS, Fabiana; GIUSTI, Lucas; CARVALHO, Diego; OGASAWARA, Eduardo; SOARES, Jorge.
Avaliação de Desvios de Conceitos Reais e Virtuais nos Atrasos de Voos em São Paulo nos Períodos Pré, Intra e Pós-Pandemia. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 39. , 2024, Florianópolis/SC.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 827-833.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2024.243111.