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

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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Main Authors: Mitrovic, Nikola, Muhammad Tayyab Asif, Dauwels, Justin, Jaillet, Patrick
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2016
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Online Access:https://hdl.handle.net/10356/80580
http://hdl.handle.net/10220/40575
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-805802022-09-23T01:39:48Z Low-Dimensional Models for Compressed Sensing and Prediction of Large-Scale Traffic Data Mitrovic, Nikola Muhammad Tayyab Asif Dauwels, Justin Jaillet, Patrick School of Electrical and Electronic Engineering Low-dimensional models traffic prediction 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. Accepted version 2016-05-27T07:17:37Z 2019-12-06T13:52:35Z 2016-05-27T07:17:37Z 2019-12-06T13:52:35Z 2015 Journal Article 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. 1524-9050 https://hdl.handle.net/10356/80580 http://hdl.handle.net/10220/40575 10.1109/TITS.2015.2411675 en IEEE Transactions on Intelligent Transportation Systems © 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]. 6 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Low-dimensional models
traffic prediction
spellingShingle Low-dimensional models
traffic prediction
Mitrovic, Nikola
Muhammad Tayyab Asif
Dauwels, Justin
Jaillet, Patrick
Low-Dimensional Models for Compressed Sensing and Prediction of Large-Scale Traffic Data
description 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.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Mitrovic, Nikola
Muhammad Tayyab Asif
Dauwels, Justin
Jaillet, Patrick
format Article
author Mitrovic, Nikola
Muhammad Tayyab Asif
Dauwels, Justin
Jaillet, Patrick
author_sort Mitrovic, Nikola
title Low-Dimensional Models for Compressed Sensing and Prediction of Large-Scale Traffic Data
title_short Low-Dimensional Models for Compressed Sensing and Prediction of Large-Scale Traffic Data
title_full Low-Dimensional Models for Compressed Sensing and Prediction of Large-Scale Traffic Data
title_fullStr Low-Dimensional Models for Compressed Sensing and Prediction of Large-Scale Traffic Data
title_full_unstemmed Low-Dimensional Models for Compressed Sensing and Prediction of Large-Scale Traffic Data
title_sort low-dimensional models for compressed sensing and prediction of large-scale traffic data
publishDate 2016
url https://hdl.handle.net/10356/80580
http://hdl.handle.net/10220/40575
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