Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (9): 95-102.doi: 10.11896/jsjkx.200700097

Special Issue: Intelligent Data Governance Technologies and Systems

• Intelligent Data Governance Technologies and Systems • Previous Articles     Next Articles

Railway Passenger Co-travel Prediction Based on Association Analysis

LI Si-ying1, XU Yang1, WANG Xin2, ZHAO Ruo-cheng3   

  1. 1 School of Economics and Management,Southwest Jiaotong University,Chengdu 610031,China
    2 School of Computer Science,Southwest Petroleum University,Chengdu 610500,China
    3 School of Business,Economics and Informatics,Birkbeck,University of London,London WC1E 7HX,UK
  • Received:2020-07-14 Revised:2020-11-04 Online:2021-09-15 Published:2021-09-10
  • About author:LI Si-ying,born in 1996,postgraduate,is a member of China Computer Federation.Her main research interest include data mining and so on.
    WANG Xin,born in 1981,Ph.D,professor,Ph.D supervisor,is a member of ACM,IEEE,CCF and CAAI.His main research interests include knowledge disco very in database,artificial in telligence,machine learning and data mining.

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Abstract: With the fast development of transportation technology,the railway has become one of the main choices for people when they travel for business,vacation or visiting.As a result,the behavior of co-travel has become more and more common.Based on this co-travel relationship,people can construct a co-travel network,where each node represents a passenger and an edge indicates co-travel frequency between two passengers this edge connects,and the link prediction on the network such that persona-lized service and product can be provided even better.In light of this,this paper proposes a novel approach to predicting potential co-travel relationship.Specifically,we first propose two types of co-travel graph pattern association rules which are extended from their traditional counterparts,and can be used to predict new co-travel relationship and co-travel frequency,respectively.We then decompose this mining problem into three sub-problems,i.e.,frequent co-travel pattern mining,rules generation and association analysis,and develop parallel and centralized algorithms for these sub-problems.Extensive experimental studies on large real-life datasets show that our approach can predict potential co-travel relationship efficiently and accurately,with accuracies higher than 50% for two types of rules,and substantially superior to the traditional method (e.g.,Jaccard with accuracy 24%).

Key words: Association analysis, Co-travel network, Co-travel pattern, Co-travel prediction, Graph pattern matching

CLC Number: 

  • F532.8
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