Support vector regression model for flight demand forecasting

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.
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航班需求预测的支持向量回归模型
航班需求预测是航空公司收入管理的一个特别重要的组成部分,因为它直接影响到决定航空公司利润的预订限额。传统的航班需求预测模型一般只以一周中的一天(DOW)和当前数据收集点(DCP)将预订量相加作为模型输入,并使用线性回归、指数平滑、拾取等模型预测航班的最终预订量。这些模型可以看作是基于当前日期和出发日期之间间隔的时间序列航班需求预测模型。它们没有考虑到特定航班预售期的早期预订量变化特征,泛化能力较弱,最后导致对航班预订量随机变化的适应性较差。基于机器学习的支持向量回归(SVR)模型对数据的非线性随机变化具有较强的适应性,能够自适应学习航班预订的随机干扰。本文根据季节属性自动将机票预订分为高峰、中等和淡季(PMO)。使用历史航班预订量和机票预售前期DCP预订量之和组成的向量来训练SVR模型。与传统模型相比,增加了飞行的先验信息。我们收集了某航空公司在新冠肺炎前2年的国内航线预订量数据作为训练和测试数据集,并将这些数据分为旅游、商务和一般三类,数值结果表明,与传统模型相比,SVR模型显著提高了预测精度,降低了RMSE。因此,本研究为航班需求预测提供了更好的选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.50
自引率
6.10%
发文量
17
审稿时长
15 weeks
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
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