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...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/80580 http://hdl.handle.net/10220/40575 |
| _version_ | 1811696993588740096 |
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| author | Mitrovic, Nikola Muhammad Tayyab Asif Dauwels, Justin Jaillet, Patrick |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Mitrovic, Nikola Muhammad Tayyab Asif Dauwels, Justin Jaillet, Patrick |
| author_sort | Mitrovic, Nikola |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T07:48:11Z |
| format | Journal Article |
| id | ntu-10356/80580 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:48:11Z |
| publishDate | 2016 |
| record_format | dspace |
| spelling | ntu-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 |
| 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 |
| title | 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_short | 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 |
| topic | Low-dimensional models traffic prediction |
| url | https://hdl.handle.net/10356/80580 http://hdl.handle.net/10220/40575 |
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