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Broad Learning for Optimal Short-Term Traffic Flow Prediction

  • Di Liu
  • Wenwu YuEmail author
  • Simone Baldi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11554)

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.

Keywords

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

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Cyber Science and EngineeringSoutheast UniversityNanjingChina
  2. 2.School of MathematicsSoutheast UniversityNanjingChina
  3. 3.Delft Center for Systems and ControlDelft University of TechnologyDelftThe Netherlands

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