  This paper addresses the problem of short-term traffic prediction for
signalized traffic operations management. Specifically, we focus on predicting
sensor states in high-resolution (second-by-second). This contrasts with
traditional traffic forecasting problems, which have focused on predicting
aggregated traffic variables, typically over intervals that are no shorter than
5 minutes. Our contributions can be summarized as offering three insights:
first, we show how the prediction problem can be modeled as a matrix completion
problem. Second, we employ a block-coordinate descent algorithm and demonstrate
that the algorithm converges in sub-linear time to a block coordinate-wise
optimizer. This allows us to capitalize on the "bigness" of high-resolution
data in a computationally feasible way. Third, we develop an ensemble learning
(or adaptive boosting) approach to reduce the training error to within any
arbitrary error threshold. The latter utilizes past days so that the boosting
can be interpreted as capturing periodic patterns in the data. The performance
of the proposed method is analyzed theoretically and tested empirically using
both simulated data and a real-world high-resolution traffic dataset from Abu
Dhabi, UAE. Our experimental results show that the proposed method outperforms
other state-of-the-art algorithms.
