THESIS
2017
xv, 103 pages : illustrations ; 30 cm
Abstract
The popularity of location-acquisition devices has led to a rapid increase in the amount
of trajectory data collected. The large volume of trajectory data causes the difficulties of storing and processing the data. Various trajectory compression methods are
therefore proposed to deal with these problems. In this thesis, we study the problem
of trajectory compression under road network constraints. We summarize the existing
road-network-constrained trajectory compression methods and propose a classification
based on the features leveraged by them. We propose new methods that fill in the research blanks indicated by the classification. We conduct a thorough comparison among
the existing and new road-network-constrained trajectory compression methods. The
performances of the methods...[
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The popularity of location-acquisition devices has led to a rapid increase in the amount
of trajectory data collected. The large volume of trajectory data causes the difficulties of storing and processing the data. Various trajectory compression methods are
therefore proposed to deal with these problems. In this thesis, we study the problem
of trajectory compression under road network constraints. We summarize the existing
road-network-constrained trajectory compression methods and propose a classification
based on the features leveraged by them. We propose new methods that fill in the research blanks indicated by the classification. We conduct a thorough comparison among
the existing and new road-network-constrained trajectory compression methods. The
performances of the methods are studied via various metrics on real-world dataset.
We make new discoveries regarding the performances and the scalability of existing
methods, and provide guidelines of road-network-constrained trajectory compression
for various scenarios. We also design a new road-network-constrained trajectory compression framework composed of several coordinating methods which has an excellent
performance in both spatial and temporal compression. The framework is also able to
support location-based services by being able to answer several basic spatio-temporal
queries. We conduct extensive experiments to verify the efficiency and effectiveness of
our proposed framework using real-world trajectory data. The results strongly advocate
the performance of the proposed framework.
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