Discovering popular routes from trajectories

Chen, Zaiben, Shen, Heng Tao and Zhou, Xiaofang (2011). Discovering popular routes from trajectories. In: Proceedings of the 2011 IEEE 27th International Conference on Data Engineering. 2011 IEEE 27th International Conference on Data Engineering, Hannover, Germany, (900-911). 11-16 April 2011. doi:10.1109/ICDE.2011.5767890

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Author Chen, Zaiben
Shen, Heng Tao
Zhou, Xiaofang
Title of paper Discovering popular routes from trajectories
Conference name 2011 IEEE 27th International Conference on Data Engineering
Conference location Hannover, Germany
Conference dates 11-16 April 2011
Proceedings title Proceedings of the 2011 IEEE 27th International Conference on Data Engineering   Check publisher's open access policy
Journal name Ieee 27Th International Conference On Data Engineering (Icde 2011)   Check publisher's open access policy
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2011
Year available 2011
Sub-type Fully published paper
DOI 10.1109/ICDE.2011.5767890
Open Access Status Not yet assessed
ISBN 9781424491940
ISSN 1063-6382
Start page 900
End page 911
Total pages 12
Language eng
Abstract/Summary The booming industry of location-based services has accumulated a huge collection of users' location trajectories of driving, cycling, hiking, etc. In this work, we investigate the problem of discovering the Most Popular Route (MPR) between two locations by observing the traveling behaviors of many previous users. This new query is beneficial to travelers who are asking directions or planning a trip in an unfamiliar city/area, as historical traveling experiences can reveal how people usually choose routes between locations. To achieve this goal, we firstly develop a Coherence Expanding algorithm to retrieve a transfer network from raw trajectories, for indicating all the possible movements between locations. After that, the Absorbing Markov Chain model is applied to derive a reasonable transfer probability for each transfer node in the network, which is subsequently used as the popularity indicator in the search phase. Finally, we propose a Maximum Probability Product algorithm to discover the MPR from a transfer network based on the popularity indicators in a breadth-first manner, and we illustrate the results and performance of the algorithm by extensive experiments.
Keyword Computer Science, Artificial Intelligence
Computer Science, Information Systems
Engineering, Electrical & Electronic
Computer Science
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Article number 5767890

Document type: Conference Paper
Sub-type: Fully published paper
Collections: Official 2012 Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 92 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 159 times in Scopus Article | Citations
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Created: Wed, 01 Jun 2011, 21:19:59 EST by Ms Dulcie Stewart on behalf of School of Information Technol and Elec Engineering