TrajMetrix: a trajectory compression benchmarking framework

Authors:
Kyuseo Park;Jeremy Birnbaum;Paul Olsen, Jr.;Yuchao Ma;Jayadevan Vijayan;S. S. Ravi;Jeong-Hyon Hwang;Jonathan Muckell;Catherine T. Lawson
Affiliations:
University at Albany - State University of New York;University at Albany - State University of New York;University at Albany - State University of New York;University at Albany - State University of New York;University at Albany - State University of New York;University at Albany - State University of New York;University at Albany - State University of New York;University at Albany - State University of New York;University at Albany - State University of New York
Venue:
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Year:
2013

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Abstract

Trajectory compression algorithms enable efficient transmission, storage, and processing of trajectory data by eliminating redundant information. While a large number of compression algorithms have been developed, there is no comprehensive and convenient benchmarking system for evaluating these algorithms. We will demonstrate TrajMetrix, our system that meets the above need. We will show how TrajMetrix can be used to gain insights into the benefits and drawbacks of various compression algorithms given different compression requirements. From the knowledge attained by using TrajMetrix, we developed SQUISH-E (Spatial QUalIty Simplification Heuristic - Extended). This algorithm uses a priority queue to preferentially remove points based on the error introduced by their removal. Through live demonstrations that use both synthetic and real data sets, we will show the ability of SQUISH-E to effectively bound compression error with low computational overhead.