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

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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Bibliographic Details
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
Subjects:
Low-dimensional models
traffic prediction
Online Access:https://hdl.handle.net/10356/80580
http://hdl.handle.net/10220/40575
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Institution: Nanyang Technological University
Language: English
Description
Summary: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.

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