Nonlinear Traffic Prediction as a Matrix Completion Problem with Ensemble Learning
This work addresses high-resolution traffic prediction for signalized operations management, offering an incremental improvement over traditional aggregated methods.
The paper tackles short-term, second-by-second traffic prediction by modeling it as a matrix completion problem, using a block-coordinate descent algorithm and ensemble learning to reduce training error, and demonstrates that the method outperforms state-of-the-art algorithms on simulated and real-world data from Abu Dhabi.
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.