Low-Dimensional Models for Compressed Sensing and Prediction of Large-Scale Traffic Data
Author(s)Mitrovic, Nikola; Asif, Muhammad Tayyab; Dauwels, Justin; Jaillet, Patrick
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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.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Operations Research Center
IEEE Transactions on Intelligent Transportation Systems
Institute of Electrical and Electronics Engineers (IEEE)
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.
Author's final manuscript