Statistical learning of air passenger traffic at the Murtala Muhammed International Airport, Nigeria / Christopher Godwin Udomboso and Gabriel Olugbenga Ojo

Christopher, Godwin Udomboso and Gabriel, Olugbenga Ojo (2021) Statistical learning of air passenger traffic at the Murtala Muhammed International Airport, Nigeria / Christopher Godwin Udomboso and Gabriel Olugbenga Ojo. In: e-Proceedings of the 5th International Conference on Computing, Mathematics and Statistics (iCMS 2021), 4-5 August 2021.

Abstract

Based on previous studies, aviation affair needs reliable forecasts of air passenger traffic flow. In this research, the performance of Artificial Neural Network (ANN) and Support Vector Machine (SVM) models were investigated on predicting air passenger traffic in the Murtala International Airport
Nigeria. Past eleven years’ monthly data (2007-2018) obtained from Statistics Department of the Nigerian Airspace Management Agency (NAMA), MMIA, Lagos was used. ANN models with backpropagation steepest descent estimation techniques were compared with the SVM models with
different kernels. The comparative evaluation of these adopted models focused basically on a Root Mean Square Error (RMSE) statistical loss function. The efficiency of the ANN model was found better than that of the SVM model in predicting the domestic air passenger traffic flow, while the SVM model predicted the foreign air passenger traffic flow more efficiently than the ANN model.

Metadata

Item Type: Conference or Workshop Item (Paper)
Creators:
Creators
Email / ID Num.
Christopher, Godwin Udomboso
udomboso@gmail.com
Gabriel, Olugbenga Ojo
adeoluwie@gmail.com
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > Equations
Divisions: Universiti Teknologi MARA, Kedah > Sg Petani Campus
Journal or Publication Title: International Conference on Computing, Mathematics and Statistics (iCMS 2021)
Event Title: e-Proceedings of the 5th International Conference on Computing, Mathematics and Statistics (iCMS 2021)
Event Dates: 4-5 August 2021
Page Range: pp. 123-136
Keywords: Artificial neural network, support vector machine, ReLU activation function
Date: 2021
URI: https://ir.uitm.edu.my/id/eprint/56159
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56159

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