Researcher:
İnan, Neslihan Gökmen

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Neslihan Gökmen

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İnan

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İnan, Neslihan Gökmen

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20222

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    The analysis of fatal aviation accidents more than 100 dead passengers: an application of machine learning
    (Springer India, 2022) Inan, Tuzun Tolga; Department of Computer Engineering; İnan, Neslihan Gökmen; Teaching Faculty; Department of Computer Engineering; College of Engineering; 285581
    Safety is the most prominent factor that affected accidents in civil aviation history. In safety concept, the primary factors are defined as human, technical, and sabotage/terrorism factors. Despite these primary causes, there have other factors that have an impact to accidents. The study aims to determine the affected factors of the 220 accidents that were ended with more than 100 dead passengers by the primary causes and the other factors such as aircraft type, total distance, the phase of flight, number of total passengers, and time period of the accident. All these factors aims to classify the rate of survivor/non-survivor passenger rate according to most fatal accidents. It is used logistic regression and discriminant analysis for multivariate statistical analyses comparing the machine learning approaches to show the algorithms' robustness. At the end of the analysis, it is seen that machine learning techniques have better performance than multivariate statistical methods in related to accuracy, false-positive rate, and false-negative rates. The managerial aim of this study is related to find the most important factors that affected the most fatal accidents. These factors are found as; the phase of flight, the primary cause, and total passenger numbers according to machine learning and multivariate statistical models for classifying the rate of survivor/non-survivor passenger numbers.
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    Forecasting the recovery period of air passenger transportation by using vector error correction model
    (John Wiley and Sons Inc, 2022) İnan, Tüzün Tolga; Department of Computer Engineering; İnan, Neslihan Gökmen; Teaching Faculty; Department of Computer Engineering; College of Engineering; 285581
    The COVID-19 pandemic has had a significantly negative impact on all transportation modules, especially air passengers. The authors aimed to forecast the air passenger load factor using time series modelling for the affecting variables. After providing general information about this pandemic, the forecasting results regarding getting back into the recovery period were presented by time series modelling. The last five years (2016–2020) and the first eight months of 2021 were examined with the following variables: available seat kilometre, revenue passenger kilometre, passenger load factor, gross domestic product, and domestic and international passenger numbers. The forecast results reveal that the recovery period started in June of 2021 and continues with a robust growth trend until September 2021 due to the vaccination process and the starting of the summer season. This trend changed between September to November 2021 slightly negative, from November 2021 to January 2022 slightly positive, and from January 2022 to March 2022 mildly negative. Correspondingly, passenger load factor (PLF) is affected by itself and domestic transportation in the short-term period. This effect seems short-term in domestic and international transport. This research reveals that minimising the economic damage by benefiting from the increasing trend of air passenger numbers increases the recovery period.