Denoising Autoencoders For Fast Real-Time Traffic Estimation On Urban Road Networks

2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC)(2017)

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Abstract
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.
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Key words
online estimation approach,two-step approach,real-time traffic estimation,urban road networks,traffic state estimation,missing historical data,low-rank matrix completion,autoencoder denoising
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