Low-Dimensional Models for Compressed Sensing and Prediction of Large-Scale Traffic Data

Low-Dimensional Models for Compressed Sensing and Prediction of Large-Scale Traffic Data

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Advanced sensing and surveillance technologies often collect traffic information with high temporal and spatial resolutions. The volume of the collected data severely limits the scalability of online traffic operations. To overcome this issue, we propose a low-dimensional network representation wher...

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Bibliographic Details
Main Authors: Mitrovic, Nikola, Asif, Muhammad Tayyab, Dauwels, Justin, Jaillet, Patrick
Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Format: Article
Language:en_US
Published: Institute of Electrical and Electronics Engineers (IEEE) 2016
Online Access:http://hdl.handle.net/1721.1/100719
https://orcid.org/0000-0002-8585-6566

Internet

http://hdl.handle.net/1721.1/100719
https://orcid.org/0000-0002-8585-6566

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