Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/80580
Title: Low-Dimensional Models for Compressed Sensing and Prediction of Large-Scale Traffic Data
Authors: Mitrovic, Nikola
Muhammad Tayyab Asif
Dauwels, Justin
Jaillet, Patrick
Keywords: Low-dimensional models
traffic prediction
Issue Date: 2015
Source: Mitrovic, N., Muhammad Tayyab Asif, Dauwels, J., & Jaillet, P. (2015). Low-Dimensional Models for Compressed Sensing and Prediction of Large-Scale Traffic Data. IEEE Transactions on Intelligent Transportation Systems, 16(5), 2949-2954.
Series/Report no.: IEEE Transactions on Intelligent Transportation Systems
Abstract: 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 where only a subset of road segments is explicitly monitored. Traffic information for the subset of roads is then used to estimate and predict conditions of the entire network. Numerical results show that such approach provides 10 times faster prediction at a loss of performance of 3% and 1% for 5 and 30 minutes prediction horizons, respectively.
URI: https://hdl.handle.net/10356/80580
http://hdl.handle.net/10220/40575
ISSN: 1524-9050
DOI: 10.1109/TITS.2015.2411675
Schools: School of Electrical and Electronic Engineering 
Rights: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/TITS.2015.2411675].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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