Jonathan Muckell
University at Albany, SUNY
13 Papers
27 Citations
Jonathan Muckell is an academic researcher from University at Albany, SUNY. The author has contributed to research in topics: Trajectory & Computer science. The author has an hindex of 6, co-authored 13 publications. Previous affiliations of Jonathan Muckell include General Electric & Rensselaer Polytechnic Institute.
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Papers
Compression of trajectory data: a comprehensive evaluation and new approach
TL;DR: A new compression method called SQUISH-E (Spatial QUalIty Simplification Heuristic - Extended) that provides improved run-time performance and usability and is carried out through an empirical study across three types of real-world datasets and a variety of error metrics.
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SQUISH: an online approach for GPS trajectory compression
Jonathan Muckell,Jeong-Hyon Hwang,Vikram Patil,Catherine T. Lawson,Fan Ping,S. S. Ravi +5 more
- 23 May 2011
TL;DR: The Spatial QUalIty Simplification Heuristic (SQUISH) method is described, which demonstrates improved performance when compressing up to roughly 10% of the original data size, and preserves speed information at a much higher accuracy under aggressive compression.
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Algorithms for compressing GPS trajectory data: an empirical evaluation
Jonathan Muckell,Jeong-Hyon Hwang,Catherine T. Lawson,S. S. Ravi +3 more
- 02 Nov 2010
TL;DR: This empirical study uses different types of real-world data such as pedestrian, vehicle and multimodal trajectories and presents results from a comprehensive empirical evaluation of many compression algorithms including Douglas-Peucker Algorithm, Bellman's Al algorithm, STTrace Algorithm and Opening Window Algorithm.
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Evaluating the Feasibility of a Passive Travel Survey Collection in Complex Urban Environment: A Case Study in New York City
Cynthia Chen,Hongmian Gong,Catherine T. Lawson,Evan Bialostozky,Jonathan Muckell +4 more
- 01 Jan 2010
TL;DR: In this article, the feasibility of a passive travel survey in a complex urban environment has been evaluated using a multi-modal network, and the results showed that the survey results were promising, reporting success rates ranging from 60% to 95%.
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Toward an Intelligent Brokerage Platform: Mining Backhaul Opportunities in Telematics Data
TL;DR: An algorithm is described for identifying load-sharing and backhaul opportunities based on the detection of patterns in large-scale, event-based telematics network data.
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