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Imputing Missing Data via Sparse Reconstruction Techniques, Executive Summary
| 题名： | Imputing Missing Data via Sparse Reconstruction Techniques, Executive Summary |
| 作者： | Caramanis, C. |
| 关键词： | Sparse reconstruction techniques##Automated system##Traffic volumes##Traffic flow##Road network##Signal reconstruction## |
| 摘要： | The State of Texas does not currently have an automated approach for estimating volumes for links without counts. This research project proposes the development of an automated system to efficiently estimate the traffic volumes on uncounted links, in the event of rare disturbances of the typical traffic flow. The idea we plan to leverage is that the road network provides a mixing effect, whereby localized disturbances (accidents, flooding, road damage, etc.) have an impact whose effect can be measured at many places across the city. This forms the important analog to the well-known uncertainty principle, whereby a signal cannot be sparse in both the time and frequency domains—a result that is critically utilized in the signal reconstruction algorithms for fMRI. |
| 总页数： | 5 |
| 报告类型： | 科技报告 |
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