Denoising autoencoders for fast real-time traffic estimation on urban road networks
| Autor: | Muhammad Tayyab Asif, Soham Ghosh, Laura Wynter |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Estimation
050210 logistics & transportation Matrix completion Computer science Noise reduction 05 social sciences Contrast (statistics) 010501 environmental sciences computer.software_genre 01 natural sciences Data modeling Matrix decomposition 0502 economics and business Data mining State (computer science) computer 0105 earth and related environmental sciences |
| Zdroj: | CDC |
| DOI: | 10.1109/cdc.2017.8264610 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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