Xin Yao
Machine learning methodologies for airfare prediction.
Rel. Edoardo Patti, Alessandro Aliberti. Politecnico di Torino, Master of science program in Ict For Smart Societies, 2022
Abstract
With the booming tourism industry, more and more people are choosing airplanes as a means of transportation for long-distance travel. Accurate low-price forecasting of air tickets helps the aviation industry to match demand and supply flexibly and make full use of aviation resources. Airline companies use dynamic pricing strategies to determine the price of airline tickets to maximize profits when selling airline tickets. Passengers who choose airplanes as a means of transportation want to purchase tickets at the lowest selling price for the flight of their choice. However, airline tickets are a special commodity that is time-sensitive and scarce, and the price of airline tickets is affected by various factors, such as the departure time of the plane, the number of hours of advance purchase, and the airline flight, so it is difficult for consumers to know the best time to buy a ticket.
Deep learning algorithms have made great achievements in various fields in recent years, however, most prior work on airfare prediction problem is based on traditional machine learning methods, thus the performance of deep learning on this problem remains unclear
Relators
Academic year
Publication type
Number of Pages
Additional Information
Course of studies
Classe di laurea
Aziende collaboratrici
URI
 |
Modify record (reserved for operators) |
