Work

An artificial neural network for predicting air traffic demand based on socio-economic parameters

Year: 2023

Type: article

Abstract: Over the past five years, emerging economies have consistently progressed toward achieving greater economic independence. The aviation industry has played a significant role in driving this progress, particularly over the past decade. Consequently, accurately forecasting the future demand for air travel is crucial for making long-term commitments to these economies. The findings of this study provide valuable insights into the provision of low-cost passenger carrier demand and can offer valuable assistance to low-cost carriers, airports, airport consultants, and government agencies in their strategic planning and development processes. The primary objective of this study was to identify and evaluate artificial intelligence-based solutions, specifically Artificial Neural Networks (ANN), to determine the most effective model for projecting domestic demand for low-cost carriers. The study analyzed nine variables that impact the accuracy of predicting domestic air passenger demand in emerging countries using a Multilayer Feed-Forward Backpropagation Neural (MFFBPN) network. The performance of the developed ANN model for predicting air passenger demand was assessed through training, validation, and testing using 240 data sets. The evaluation metrics employed, including mean square error (MSE), root mean square error (RMSE), accuracy, precision, sensitivity, and specificity, indicate the effectiveness of the developed ANN model, with results of 0.0019 for MSE, 0.0440 for RMSE, 0.92 for accuracy, 0.92 for precision, 0.93 for sensitivity, and 0.95 for specificity. The key finding of this study is that modeling based on artificial intelligence approaches, such as ANN, is significantly more effective than standard linear regression models.

Source: Decision Analytics Journal

DOI: 10.1016/j.dajour.2023.100382

Authors: Md Shafiqul Alam, Jayanta Bhusan Deb, Abdullah Al Amin, Shilpa Chowdhury

Institutions: University of Regina, Western Carolina University, Bangladesh University of Professionals, Lamar University

Language: English

Topic: Aviation Industry Analysis and Trends

Subfield: General Economics, Econometrics and Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,500