Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
6-2017
Abstract
We propose a new method for traffic state estimation applicable to large urban road networks where a significant amount of the real-time and historical data is missing. Our proposed approach involves estimating the missing historical data through low-rank matrix completion, coupled with an online estimation approach for estimating the missing real-time data. In contrast to the traditional approach, the proposed method does not require re-calibration every time new streaming data becomes available. Empirical results from two metropolitan cities show that the proposed two-step approach provides comparable accuracy to a state of the art benchmark method while achieving two orders of magnitude improvement in computational speed.
Discipline
OS and Networks | Transportation
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Digital transformation
Publication
56th IEEE Annual Conference on Decision and Control, CDC 2017
First Page
6307
Last Page
6312
ISBN
9781509028733
Identifier
10.1109/CDC.2017.8264610
Publisher
IEEE
City or Country
Piscataway, NJ
Citation
GHOSH, Soham; ASIF, Muhammad Tayyab; and WYNTER, Laura.
Denoising autoencoders for fast real-time traffic estimation on urban road networks. (2017). 56th IEEE Annual Conference on Decision and Control, CDC 2017. 6307-6312.
Available at: https://ink.library.smu.edu.sg/sis_research/10386
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/CDC.2017.8264610