Analysing prospects for expansion in Bangladesh's domestic aviation industry using machine learning and prediction intervals

Abstract

Bangladesh, grappling with high population density and severe traffic congestion, has witnessed a surge in air travel demand due to inadequate transportation infrastructure. This trend has propelled the growth of the domestic aviation industry, with several private companies and a government-owned entity operating domestic flights. However, monopolistic tendencies and barriers to entry pose challenges for new market entrants. In response, this paper presents a comprehensive analysis of Bangladesh's domestic aviation sector, focusing on comprehensive analysis of growth opportunities. Over a four-month period, our study constructs a comprehensive dataset from diverse sources, including flight data, operational expenditures, passenger surveys, and interviews, to evaluate the scope and growth potential within the domestic aviation market. Leveraging machine learning algorithms and novel uncertainty quantification techniques like prediction intervals, we investigate the profitability of flights on different routes based on operational costs and ticket fares. Prediction intervals perform better than point predictions in this case, as they offer a range of likely outcomes, providing more valuable insights into potential profit margins, offering valuable guidance for new entrants seeking opportunities in the market. The dataset's trustworthiness is ensured through rigorous validation, with a trustworthiness score of 83.5 %. Additionally, the dataset is available upon request to facilitate further research and collaboration.