Efficient retrieval of top-K most similar users from Travel Smart Card Data

Zheng, Bolong, Zheng, Kai, Sharaf, Mohamed A., Zhou, Xiaofang and Sadiq, Shazia (2014). Efficient retrieval of top-K most similar users from Travel Smart Card Data. In: 2014 IEEE 15th International Conference on Mobile Data Management (MDM). 2014 IEEE 15th International Conference on Mobile Data Management, Brisbane, QLD, (259-268). 14-18 July 2014. doi:10.1109/MDM.2014.38

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Author Zheng, Bolong
Zheng, Kai
Sharaf, Mohamed A.
Zhou, Xiaofang
Sadiq, Shazia
Title of paper Efficient retrieval of top-K most similar users from Travel Smart Card Data
Conference name 2014 IEEE 15th International Conference on Mobile Data Management
Conference location Brisbane, QLD
Conference dates 14-18 July 2014
Convener IEEE
Proceedings title 2014 IEEE 15th International Conference on Mobile Data Management (MDM)
Journal name 2014 Ieee 15Th International Conference On Mobile Data Management (Mdm), Vol 1
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2014
Sub-type Fully published paper
DOI 10.1109/MDM.2014.38
Open Access Status
ISBN 9781479957057
ISSN 1551-6245
Volume 1
Start page 259
End page 268
Total pages 10
Collection year 2015
Language eng
Abstract/Summary Understanding the dynamics of human daily mobility patterns is essential for the management and planning of urban facilities and services. Travel smart cards, which record users' public transporting histories, capture rich information of users' mobility pattern. This provides the opportunity to discover valuable knowledge from these transaction records. In recent years, research on measuring user similarity for behavior analysis has attracted a lot of attention in applications such as recommendation systems, crowd behavior analysis applications, and numerous data mining tasks. In this paper, our goal is to estimate the similarity between users' travel patterns according to their travel smart card data. The core of our proposal is a novel user similarity measurement, namely, Travel Spatial-Temporal Similarity (TST), which measures the spatial range and temporal similarity between users. Moreover, we also propose a hybrid index structure, which integrates inverted files and cluster-based partitioning, to allow for efficient retrieval of the top-K most similar users. Through experimental evaluation, our proposed approach is shown to deliver scalable performance.
Keyword Data mining
Information retrieval
Smart cards
Travel industry
Cities and towns
Data mining
Histograms
Indexing
Time-frequency analysis
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6916929

 
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Created: Wed, 11 Mar 2015, 12:12:53 EST by Naomi Epstein on behalf of School of Information Technol and Elec Engineering