A Framework for Airfare Price Prediction: A Machine Learning Approach

Tianyi Wang, Samira Pouyanfar, Haiman Tian, Yudong Tao, Miguel Alonso, Steven Luis, Shu-Ching Chen

Abstract: The price of an airline ticket is affected by a number of factors, such as flight distance, purchasing time, fuel price, etc. Each carrier has its own proprietary rules and algorithms to set the price accordingly. Recent advance in Artificial Intelligence (AI) and Machine Learning (ML) makes it possible to infer such rules and model the price variation. This paper proposes a novel application based on two public data sources in the domain of air transportation: the Airline Origin and Destination Survey (DB1B) and the Air Carrier Statistics database (T-100). The proposed framework combines the two databases, together with macroeconomic data, and uses machine learning algorithms to model the quarterly average ticket price based on different origin and destination pairs, as known as the market segment. The framework achieves a high prediction accuracy with 0.869 adjusted R squared score on the testing dataset.