基于隐马尔可夫模型的铁路出行团体关系预测研究

doi:10.11896/jsjkx.210500001

计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 247-255.doi: 10.11896/jsjkx.210500001

基于隐马尔可夫模型的铁路出行团体关系预测研究

王欣¹, 向明月², 李思颖², 赵若成³

1 西南石油大学计算机科学学院成都 610500
2 西南交通大学经济管理学院成都 610031
3 伦敦大学伯贝克学院商业经济和信息学院伦敦 WC1E 7HX

出版日期:2022-06-10 发布日期:2022-06-08
通讯作者: 李思颖(1049810415@qq.com)
作者简介:
(xinwang@swpu.edu.cn)

Relation Prediction for Railway Travelling Group Based on Hidden Markov Model

WANG Xin¹, XIANG Ming-yue², LI Si-ying², ZHAO Ruo-cheng³

1 Southwest Petroleum University,School of Computer Science,Chengdu 610500,China
2 Southwest Jiaotong University,School of Economics and Management,Chengdu 610031,China
3 Birkbeck,University of London,School of Business,Economics and Informatics,London WC1E 7HX,UK

Online:2022-06-10 Published:2022-06-08
About author:WANG Xin,born in 1981,Ph.D,professor,Ph.D supervisor,is a member of ACM,IEEE,CCF and CAAI.His main research interests in clude knowledge discovery in database,artificial in telligence,machine learning and data mining.
LI Si-ying,born in 1996,postgraduate.Her main research interest includes data mining.

摘要/Abstract

摘要： 近年来,随着铁路交通网络和高铁技术的不断发展,铁路出行的快捷性和舒适性得到了大幅度提高,铁路出行被更多人选择,团队出行也变得更加普遍。旅客的出行行为通常会受同行旅客的影响,不同的出行团体有不同的出行偏好,如家庭团体出行时会考虑团体中的老人和小孩,更在意舒适度;年轻人组成的团体出行时会着重考虑体验感和新鲜感。因此,出行团体类型是研究该团体出行偏好的基础。基于此,文中提出了一种利用客票数据对铁路出行团体同行关系进行预测的方法。首先,基于铁路客票数据特点,提出了铁路出行团体同行次数的量化方法;然后,对隐马尔可夫模型在客票数据分析中的适用性进行了剖析,对基于隐马尔可夫模型的铁路出行团体关系预测问题进行了形式化定义。基于真实铁路购票数据,对构建的出行团体关系模型的预测准确性以及预测结果的一致性进行了验证,实验结果显示构建的模型的预测准确率高达96.38%,对于同一出行团体在不同时刻的预测结果的一致性达95%,由此认为所提方法能够高效且准确地预测铁路出行团体中的同行关系。

关键词: 铁路出行团体, 同行关系预测, 隐马尔可夫模型

Abstract: In recent years,with the continuous development of transportation network as well as technology in high-speed railway,the speed and comfort of railway travel have been greatly improved,more and more people choose to travel by railway.As a result,co-travel behaviors have become even common in rail trips.The travel behavior of passengers can be influenced by their peers,and different travel groups will present different travel preferences.For example,for a travelling group with family mem-bers,the elderly and children will be taken good care of,hence group members are more inclined to pursue comfort during the trip.When a few young people who are mutual friends form a travelling group,they care more about the sense of experience and freshness.Therefore,predicting the type of a travel group will be beneficial for learning travel preference of this group,e.g.,not only help transportation,tourism and other related industries to define their products and services that travel groups interest in,but also provide support for market decision-making in the railway transportation industry.Based onthis,this paper proposes a methodology for analyzing railway passengers'travelling behavioral using ticket booking data.Firstly,based on ticket booking data,it proposes the quantitative method of co-travel times of a travelling group.Secondly,it formalizes the prediction problem by incorporating Hidden Markov Model.Lastly,the accuracy and consistency of the model are verified with real-life data and experiment results show that the accuracy of our model can even reach 96.38%,in the meanwhile,the consistency is as high as 95%.Thus,we conclude that the proposed method can effectively and accurately predict the relationship of railway travel groups.

Key words: Co-travel relation prediction, HMM, Railway co-travel group

中图分类号:

F532.8

王欣, 向明月, 李思颖, 赵若成. 基于隐马尔可夫模型的铁路出行团体关系预测研究[J]. 计算机科学, 2022, 49(6A): 247-255. https://doi.org/10.11896/jsjkx.210500001

WANG Xin, XIANG Ming-yue, LI Si-ying, ZHAO Ruo-cheng. Relation Prediction for Railway Travelling Group Based on Hidden Markov Model[J]. Computer Science, 2022, 49(6A): 247-255. https://doi.org/10.11896/jsjkx.210500001

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基于隐马尔可夫模型的铁路出行团体关系预测研究

Relation Prediction for Railway Travelling Group Based on Hidden Markov Model

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