Air passenger travel forecasting model based on both dynamical individual behavior and social influence force
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 reven...
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2019-10-01
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Series: | Journal of Algorithms & Computational Technology |
Online Access: | https://doi.org/10.1177/1748302619881392 |
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doaj-4c4d2b8220844a8fb30e8f1fa34e95f82020-11-25T03:44:47ZengSAGE PublishingJournal of Algorithms & Computational Technology1748-30262019-10-011310.1177/1748302619881392Air passenger travel forecasting model based on both dynamical individual behavior and social influence forceJialiang WangXiaoqing LiuJianli DingAir 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.https://doi.org/10.1177/1748302619881392 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jialiang Wang Xiaoqing Liu Jianli Ding |
spellingShingle |
Jialiang Wang Xiaoqing Liu Jianli Ding Air passenger travel forecasting model based on both dynamical individual behavior and social influence force Journal of Algorithms & Computational Technology |
author_facet |
Jialiang Wang Xiaoqing Liu Jianli Ding |
author_sort |
Jialiang Wang |
title |
Air passenger travel forecasting model based on both dynamical individual behavior and social influence force |
title_short |
Air passenger travel forecasting model based on both dynamical individual behavior and social influence force |
title_full |
Air passenger travel forecasting model based on both dynamical individual behavior and social influence force |
title_fullStr |
Air passenger travel forecasting model based on both dynamical individual behavior and social influence force |
title_full_unstemmed |
Air passenger travel forecasting model based on both dynamical individual behavior and social influence force |
title_sort |
air passenger travel forecasting model based on both dynamical individual behavior and social influence force |
publisher |
SAGE Publishing |
series |
Journal of Algorithms & Computational Technology |
issn |
1748-3026 |
publishDate |
2019-10-01 |
description |
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. |
url |
https://doi.org/10.1177/1748302619881392 |
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