Compression and Mining of GPS Trace Data: New Techniques and Applications

Lawson, Catherine T.; Ravi, Sekharipuram S.; Hwang, Jeong-Hyon

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Compression and Mining of GPS Trace Data: New Techniques and Applications

2011-03-01
By Lawson, Catherine T. ; Ravi, Sekharipuram S. ; Hwang, Jeong-Hyon

File Language:

English

Details

Creators:

Lawson, Catherine T. ; Ravi, Sekharipuram S. ; Hwang, Jeong-Hyon
Corporate Creators:

University Transportation Research Center ; State University of New York at Albany
Corporate Contributors:

United States. Federal Highway Administration
Subject/TRT Terms:
Resource Type:

Tech Report
Geographical Coverage:

New York ; United States
Edition:

Final Report January 1, 2010 - December 31, 2010
Corporate Publisher:

City University of New York. University Transportation Research Center
Abstract:

The massive volumes of trajectory data generated by inexpensive GPS devices have led to difficulties in processing, querying, transmitting and storing such data. To overcome these difficulties, a number of algorithms for compressing trajectory data have been proposed. These algorithms try to reduce the size of trajectory data, while preserving the quality of the information. We present results from a comprehensive empirical evaluation of many compression algorithms including Douglas-Peucker Algorithm, Bellman's Algorithm, STTrace Algorithm and Opening Window Algorithms. Our empirical study uses different types of real-world data such as pedestrian, vehicle and multimodal trajectories. The algorithms are compared using several criteria including how well they preserve the spatiotemporal information across numerous real-world datasets, execution times and various error metrics. Such comparisons are useful in identifying the most effective algorithms for various situations. We also provide recommendations for a hybrid algorithm which can leverage the strengths of various algorithms while mitigating their drawbacks.
Format:

PDF
Download URL:

https://rosap.ntl.bts.gov/view/dot/40253/dot_40253_DS1.pdf
File Type:

[PDF - 644.08 KB]
Collection(s):

University Transportation Centers ; US Transportation Collection
Main Document Checksum:

urn:sha256:c82e27675997b719a1ee15939acfffb99969cb237674f617e85a79702e37b6ff

File Language:

English

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