Low-Dimensional Models for Compressed Sensing and Prediction of Large-Scale Traffic Data
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Jaillet_Low-dimensional-Models-v2-2015.pdf
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Author(s)
Mitrovic, Nikola
Asif, Muhammad Tayyab
Dauwels, Justin
Jaillet, Patrick
Date Issued
September 2015
Journal
IEEE Transactions on Intelligent Transportation Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Mitrovic, Nikola, Muhammad Tayyab Asif, Justin Dauwels, and Patrick Jaillet. “Low-Dimensional Models for Compressed Sensing and Prediction of Large-Scale Traffic Data.” IEEE Transactions on Intelligent Transportation Systems 16, no. 5 (October 2015): 2949–2954.
Version
Author's final manuscript
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-min prediction horizons, respectively.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Operations Research Center
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/tits.2015.2411675
