Journal of East China Normal University(Natural Sc ›› 2017, Vol. 2017 ›› Issue (5): 201-212.doi: 10.3969/j.issn.1000-5641.2017.05.018

• Location Based Services • Previous Articles     Next Articles

Individual station estimation from smart card transactions

WANG Yi-lin, ZHANG Zhi-gang, JIN Che-qing   

  1. School of Data Science and Engineering, East China Normal University, Shanghai 200062, China
  • Received:2017-06-30 Online:2017-09-25 Published:2017-09-25

Abstract: With the fast development of public transportation network and widespread use of smart card, more and more rich semantic information about human mobility behaviors are hidden in smart card transaction data. However, a great number of current smart cards are initially designed for charging and do not record any detailed information about where and when a passenger gets on or gets off a bus, which brings out great difficulties for analyzing, mining transaction data and providing more precise location-based services. This paper presents Space-Time Adjacency algorithm (STA) and Historical Trip Based algorithm (HTB) to estimate the bus station of each card's transaction records with the aid of integral historical data including complete subway transaction data. Specifically, STA does the initial reconstruction work according to the space-time proximity of adjacent transaction records. Then HTB first cuts the collection of records to form trips that contain explicit trip purposes, then extracts taken lines and transfer lines using historical data, next generates candidate stations for each taken line, and finally uses them to recover the transaction records again. Experiments show that the proposed algorithms work well and narrow the range of candidate stations for bus lines, and have good time efficiency.

Key words: smart card, incomplete data, card mining, station estimation

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