Reducing uncertainty of low-sampling-rate trajectories

Zheng, Kai, Zheng, Yu, Xie, Xing and Zhou, Xiaofang (2012). Reducing uncertainty of low-sampling-rate trajectories. In: ICDE 2012 IEEE 28th International Conference on Data Engineering. 28th IEEE International Conference on Data Engineering (ICDE), Washington, DC, United States, (1144-1155). 1-5 April 2012. doi:10.1109/ICDE.2012.42

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Author Zheng, Kai
Zheng, Yu
Xie, Xing
Zhou, Xiaofang
Title of paper Reducing uncertainty of low-sampling-rate trajectories
Conference name 28th IEEE International Conference on Data Engineering (ICDE)
Conference location Washington, DC, United States
Conference dates 1-5 April 2012
Proceedings title ICDE 2012 IEEE 28th International Conference on Data Engineering   Check publisher's open access policy
Journal name Proceedings - International Conference on Data Engineering   Check publisher's open access policy
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2012
Sub-type Fully published paper
DOI 10.1109/ICDE.2012.42
Open Access Status
ISBN 9780768547473
ISSN 1084-4627
Start page 1144
End page 1155
Total pages 12
Collection year 2013
Language eng
Formatted Abstract/Summary
The increasing availability of GPS-embedded mobile devices has given rise to a new spectrum of location-based services, which have accumulated a huge collection of location trajectories. In practice, a large portion of these trajectories are of low-sampling-rate. For instance, the time interval between consecutive GPS points of some trajectories can be several minutes or even hours. With such a low sampling rate, most details of their movement are lost, which makes them difficult to process effectively. In this work, we investigate how to reduce the uncertainty in such kind of trajectories. Specifically, given a low-sampling-rate trajectory, we aim to infer its possible routes. The methodology adopted in our work is to take full advantage of the rich information extracted from the historical trajectories. We propose a systematic solution, History based Route Inference System (HRIS), which covers a series of novel algorithms that can derive the travel pattern from historical data and incorporate it into the route inference process. To validate the effectiveness of the system, we apply our solution to the map-matching problem which is an important application scenario of this work, and conduct extensive experiments on a real taxi trajectory dataset. The experiment results demonstrate that HRIS can achieve higher accuracy than the existing map-matching algorithms for low-sampling-rate trajectories.
Keyword Artificial Neural Networks
Educational institutions
Global positioning systems
Trajectory
Inference algorithms
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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