Dissertation defense (August 26, 2021): Lucas Giusti Tavares

Advisors: Jorge de Abreu Soares (advisor) and Eduardo Soares Ogasawara (CEFET/RJ) (co-advisor).

Committee: Jorge de Abreu Soares (president), Eduardo Soares Ogasawara (CEFET/RJ), Rafaelli de Carvalho Coutinho (CEFET/RJ) e Antônio Tadeu Azevedo Gomes (LNCC).

Day/Time: August 26, 2021 / 13h30.

Room: https://meet.google.com/zow-fgxq-fte

Abstract: Delay is one of the most critical indicators for flight transportation systems. Flight delays impose a challenge that impacts any flight transportation system. In this context, the prediction of delayed flights may be an essential tool for effectively addressing this problem. This dissertation investigates the prediction performance of different drift handling strategies in aviation under different scales. It considers two different scales: system-based (SB) and airport-based (AB). In (SB), all airports in the flight system are considered together. Conversely, in AB, each airport is studied separately. Specifically, this work proposed and answered two research questions: (i) How do drift handling strategies influence the prediction performance of delays?; and (ii) Do different scales change the results of drift handling strategies? It was observed that drift handling strategies are relevant. Their impact varies according to the scales used. The experimental evaluation was done using a dataset that integrates weather and flight data from the Brazilian system. Moreover, the passive and active strategies revealed better recall scores. For f1 scores, the strategies had similar results, with the passive strategy showing slightly better results. It may be related to the high prevalence of drifts. In this case, strategies that always retrain machine learning models offer better results than those that train only once. However, extensive testing is recommended. Nonetheless, choosing machine learning models may have a higher impact on f1 than drift handling strategies.

Dissertation