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...

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Bibliographic Details
Main Authors: Mitrovic, Nikola, Muhammad Tayyab Asif, Dauwels, Justin, Jaillet, Patrick
Other Authors: School of Electrical and Electronic Engineering
Format: Journal Article
Language:English
Published: 2016
Subjects:
Low-dimensional models
traffic prediction
Online Access:https://hdl.handle.net/10356/80580
http://hdl.handle.net/10220/40575

Internet

https://hdl.handle.net/10356/80580
http://hdl.handle.net/10220/40575

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