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• W1974539152 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." @default.
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• W1974539152 date "2015-10-01" @default.
• W1974539152 modified "2023-09-22" @default.
• W1974539152 title "Low-Dimensional Models for Compressed Sensing and Prediction of Large-Scale Traffic Data" @default.
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• W1974539152 doi "https://doi.org/10.1109/tits.2015.2411675" @default.
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