Designing an algorithm for predicting plane ticket prices using feedforward neural network modeling

Abstract

PurposeThis research aims to determine the ideal fare for various aircraft itineraries by modeling prices using a neural network method. Dynamic pricing has been studied from the airline’s point of view, with a focus on demand forecasting and price differentiation. Early demand forecasting on a specific route can assist an airline in strategically planning flights and determining optimal pricing strategies.Design/methodology/approachA feedforward neural network was employed in the current study. Two hidden layers, consisting of 18 and 12 neurons, were incorporated to enhance the network’s capabilities. The activation function employed for these layers was tanh. Additionally, it was considered that the output layer’s functions were linear. The neural network inputs considered in this study were flight path, month of flight, flight date (week/day), flight time, aircraft type (Boeing, Airbus, other), and flight class (economy, business). The neural network output, on the other hand, was the ticket price. The dataset comprises 16,585 records, specifically flight data for Iranian airlines for 2022.FindingsThe findings indicate that the model achieved a high level of accuracy in approximating the actual data. Additionally, it demonstrated the ability to predict the optimal ticket price for various flight routes with minimal error.Practical implicationsBased on the significant alignment observed between the actual data and the tested data utilizing the algorithmic model, airlines can proactively anticipate ticket prices across all routes, optimizing the revenue generated by each flight. The neural network algorithm utilized in this study offers a valuable opportunity for companies to enhance their decision-making processes. By leveraging the algorithm’s features, companies can analyze past data effectively and predict future prices. This enables them to make informed and timely decisions based on reliable information.Originality/valueThe present study represents a pioneering research endeavor that investigates using a neural network algorithm to predict the most suitable pricing for various flight routes. This study aims to provide valuable insights into dynamic pricing for marketing researchers and practitioners.

Guest editorial: Marketing via smart technologies in hospitality and tourism

“…Mojoodi et al . (2024) focus on determining the ideal fare for various aircraft itineraries by modeling prices using a neural network method, as early demand forecasting on a specific route can assist an airline in strategically planning flights and determining optimal pricing strategies.…”

“…Additionally, the study uncovers new areas of digitization in the tourism sector, further enhancing its value and relevance. Mojoodi et al (2024) focus on determining the ideal fare for various aircraft itineraries by modeling prices using a neural network method, as early demand forecasting on a specific route can assist an airline in strategically planning flights and determining optimal pricing strategies. A feedforward neural network was employed in this study for a dataset consisting of 16,585 records of Iranian airlines' flight data.…”