Abstract
In this work, we explore the use of a Broad Learning System (BLS) as a way to replace deep learning architectures for traffic flow prediction. BLS is shown to not only outperforms standard learning algorithms (Least absolute shrinkage and selection operator (LASSO), shallow and deep neural networks, stacked autoencoders) in terms of training time, but also in terms of testing accuracy.
This work was supported by the National Natural Science Foundation of China under Grant No. 61673107, the National Ten Thousand Talent Program for Young Top-notch Talents under Grant No. W2070082, the General joint fund of the equipment advance research program of Ministry of Education under Grant No. 6141A020223, and the Jiangsu Provincial Key Laboratory of Networked Collective Intelligence under Grant No. BM2017002.
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Notes
- 1.
According to the documentation in https://www.mathworks.com/help/deeplearning/ref/train.html.
- 2.
Implemented in Matlab according to the documentation in https://www.mathworks.com/help/stats/lasso.html.
- 3.
The documentation for training a stacked autoencoder can be found in https://www.mathworks.com/help/deeplearning/examples/train-stacked-autoencoders-for-image-classification.html.
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Liu, D., Yu, W., Baldi, S. (2019). Broad Learning for Optimal Short-Term Traffic Flow Prediction. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11554. Springer, Cham. https://doi.org/10.1007/978-3-030-22796-8_25
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