Wei-gang Fan, Xiang Wu, Xin Yang Shi, Chong Zhang, Ip Wai Hung, Yung Kai Leung, L. Zeng
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引用次数: 1
Abstract
Flight demand forecasting is a particularly critical component for airline revenue management because of the direct influence on the booking limits that determine airline profits. The traditional flight demand forecasting models generally only take day of the week (DOW) and the current data collection point (DCP) adds up bookings as the model input and uses linear regression, exponential smoothing, pick-up as well as other models to predict the final bookings of flights. These models can be regarded as time series flight demand forecasting models based on the interval between the current date and departure date. They fail to consider the early bookings change features in the specific flight pre-sale period, and have weak generalization ability, at last, they will lead to poor adaptability to the random changes of flight bookings. The support vector regression (SVR) model, which is derived from machine learning, has strong adaptability to nonlinear random changes of data and can adaptively learn the random disturbances of flight bookings. In this paper, flight bookings are automatically divided into peak, medium, and off (PMO) according to the season attribute. The SVR model is trained by using the vector composed of historical flight bookings and adding up bookings of DCP in the early stage of the flight pre-sale period. Compared with the traditional models, the priori information of flight is increased. We collect 2 years of domestic route bookings data of an airline in China before COVID-19 as the training and testing datasets, and divide these data into three categories: tourism, business, and general, the numerical results show that the SVR model significantly improves the forecasting accuracy and reduces RMSE compared with the traditional models. Therefore, this study provides a better choice for flight demand forecasting.
期刊介绍:
The International Journal of Engineering Business Management (IJEBM) is an international, peer-reviewed, open access scientific journal that aims to promote an integrated and multidisciplinary approach to engineering, business and management. The journal focuses on issues related to the design, development and implementation of new methodologies and technologies that contribute to strategic and operational improvements of organizations within the contemporary global business environment. IJEBM encourages a systematic and holistic view in order to ensure an integrated and economically, socially and environmentally friendly approach to management of new technologies in business. It aims to be a world-class research platform for academics, managers, and professionals to publish scholarly research in the global arena. All submitted articles considered suitable for the International Journal of Engineering Business Management are subjected to rigorous peer review to ensure the highest levels of quality. The review process is carried out as quickly as possible to minimize any delays in the online publication of articles. Topics of interest include, but are not limited to: -Competitive product design and innovation -Operations and manufacturing strategy -Knowledge management and knowledge innovation -Information and decision support systems -Radio Frequency Identification -Wireless Sensor Networks -Industrial engineering for business improvement -Logistics engineering and transportation -Modeling and simulation of industrial and business systems -Quality management and Six Sigma -Automation of industrial processes and systems -Manufacturing performance and productivity measurement -Supply Chain Management and the virtual enterprise network -Environmental, legal and social aspects -Technology Capital and Financial Modelling -Engineering Economics and Investment Theory -Behavioural, Social and Political factors in Engineering