Discovering periodic frequent travel patterns of individual metro passengers considering different time granularities and station attributes  

作　　者：Zhibin Jiang Yan Tang Jinjing Gu Zhiqing Zhang Wei Liu 

机构地区：[1]College of Transportation Engineering,Tongji University,Shanghai 201804,China [2]The Key Laboratory of Road and Traffic Engineering of the Ministry of Education,Tongji University,Shanghai 201804,China [3]Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety,Tongji University,Shanghai 201804,China [4]School of Information Science and Engineering,Yunnan University,Kunming 650500,China [5]The Key Laboratory of Internet of Things Technology and Application in Yunnan Province,Kunming 650500,China [6]Technical Center of Shanghai Shentong Metro Group Co.,Ltd.,Shanghai 201103,China

出　　处：《International Journal of Transportation Science and Technology》2024年第2期12-26,共15页交通科学与技术(英文)

基　　金：supported by the National Natural Science Foundation of China(Grant No.52102382);the Shanghai Science and Technology Committee(Grant No.20DZ1203201);the Fundamental Research Funds for the Central Universities(2022-5-YB-04);the Shanghai Shentong Metro Group Co.,Ltd.(Grant Nos.JSKY21R005-1-WT-21064 and JS-KY21R005-2).

摘　　要：Periodic frequent pattern discovery is a non-trivial task to discover frequent patterns based on user interests using a periodicity measure.Although conventional algorithms for periodic frequent pattern detection have numerous applications,there is still little research on periodic frequent pattern detection of individual passengers in the metro.The travel behavior of individual passengers has complex spatio-temporal characteristics in the metro network,which may pose new challenges in discovering periodic frequent patterns of individual metro passengers and developing mining algorithms based on real-world smart card data.This study addresses these issues by proposing a novel pattern for metro passenger travel pattern called periodic frequent passenger traffic patterns with time granularities and station attributes(PFPTS).This discovered pattern can automatically capture the features of the temporal dimension(morning and evening peak hours,week)and the spatial dimension(entering and leaving stations).The corresponding complete mining algorithm with the PFPTS-tree structure has been developed.To evaluate the performance of PFPTS-tree,several experiments are conducted on one-year real-world smart card data collected by an automatic fare collection system in a certain large metro network.The results show that PFPTS-Tree is efficient and can discover numerous interesting periodic frequent patterns of metro passengers in the real-world dataset.

关 键 词：Metro passenger travel pattern Spatio-temporal characteristics Periodic frequent pattern PFPTS-tree structure Smart card data 

分 类 号：O17[理学—数学]