Air passenger travel forecasting model based on both dynamical individual behavior and social influence force. (October 2019)

• Record Type: Journal Article
• Authors: Wang, Jialiang; Liu, Xiaoqing; Ding, Jianli
• Is Part Of: Journal of algorithms & computational technology. Volume 13(2019)
• Publication Date: 2019-10
• Subjects: Heterogeneous network -- air passenger travel forecasting model -- dynamical individual behavior -- social influence force -- feasible and effective prediction; Computer algorithms -- Periodicals; Numerical calculations -- Periodicals; Computer algorithms; Numerical calculations; Periodicals; 518.1
• DOI: 10.1177/1748302619881392
• Languages: English
• ISSNs: 1748-3018

Abstract

Air passenger travel forecasting is necessary and becomes very valuable for airline company, because accurately obtaining practical requirements of air passenger, which can not only help airline company to improve air passenger satisfaction degree and enhance user experience so as to gain huge revenue, but also can help air passengers discover suitable travel plan quickly. In order to generate the air passenger travel forecasting model, this paper aims to analyze the internal driving force and social affect factor simultaneously, which was based on dynamical personal behaviors and air passenger social relationship exactly. In particular, three aspects in terms of dynamical personal behaviors, effect of fellow air passenger, and influence of similar air passenger are all considered simultaneously, and then the data from these aspects are further trained so as to obtain weight allocation in many different scenarios. Besides, workday and non-workday are separately considered in order to make the forecasting model feasible and effective.