TY - GEN
T1 - Automatic Detection of Airline Ticket Price and Demand
T2 - 13th International Conference on Innovations in Information Technology, IIT 2018
AU - Abdella, Juhar Ahmed
AU - Zaki, Nazar
AU - Shuaib, Khaled
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/8
Y1 - 2019/1/8
N2 - Prediction of airline ticket prices and or demand is very challenging as it depends on various internal and external factors that can dynamically vary within short period of time. Researchers have proposed different types of ticket price/demand prediction models with the aim of either assisting the customer forecast ticket prices or aid the airline to predict the demand. In this paper, we present a review of customer side and airlines side prediction models. Our review analysis shows that models on both sides rely on limited set of features such as historical ticket price data, ticket purchase date and departure date. A combination of external factors such as social media data and search engine query in conjunction with advanced machine learning techniques are not considered.
AB - Prediction of airline ticket prices and or demand is very challenging as it depends on various internal and external factors that can dynamically vary within short period of time. Researchers have proposed different types of ticket price/demand prediction models with the aim of either assisting the customer forecast ticket prices or aid the airline to predict the demand. In this paper, we present a review of customer side and airlines side prediction models. Our review analysis shows that models on both sides rely on limited set of features such as historical ticket price data, ticket purchase date and departure date. A combination of external factors such as social media data and search engine query in conjunction with advanced machine learning techniques are not considered.
KW - Deep Learning
KW - Demand Prediction
KW - Price Discrimination
KW - Social Media
KW - Survey
KW - Ticket Price Prediction
UR - http://www.scopus.com/inward/record.url?scp=85062417337&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062417337&partnerID=8YFLogxK
U2 - 10.1109/INNOVATIONS.2018.8606022
DO - 10.1109/INNOVATIONS.2018.8606022
M3 - Conference contribution
AN - SCOPUS:85062417337
T3 - Proceedings of the 2018 13th International Conference on Innovations in Information Technology, IIT 2018
SP - 169
EP - 174
BT - Proceedings of the 2018 13th International Conference on Innovations in Information Technology, IIT 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 18 November 2018 through 19 November 2018
ER -