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Nonlinear Traffic Prediction as a Matrix Completion Problem with Ensemble Learning
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DOI：10.1287/trsc.2021.1086.png)
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摘要
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This paper addresses the problem of short-term traffic prediction for signalized traffic operations management. Specifically, we focus on predicting sensor states in highresolution (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 five 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 use a block-coordinate descent algorithm and demonstrate that the algorithm converges in sublinear 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 uses 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 data set from Abu Dhabi, United Arab Emirates. Our experimental results show that the proposed method outperforms other stateof-the-art algorithms.
Keyword:
traffic prediction
high-resolution data
signalized intersections
adaptive control
matrix completion
kernel regression
sparse approximation
ensemble learning
adaptive boosting
期刊
IF:
4.8
论文数:
1.9K
被引数:
8.4K
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