Broad learning for optimal short-term traffic flow prediction

Conference paper (2019)

Authors

D. Liu Southeast University

Wenwu Yu Southeast University

Simone Baldi , Southeast University

S. Baldi , Southeast University

Traffic flow prediction Broad Learning System Fast least-square methods Flat network

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Published Date

2019

Language

English

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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.

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