Short-term traffic flow prediction using EMD-based recurrent Hermite neural network approach

Syuan Yi Chen, Wei Yao Chou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

An empirical mode decomposition based recurrent Hermite neural network (ERHNN) prediction model is proposed to predict short-term traffic flow in this study. First, a recurrent Hermite neural network (RHNN) prediction model with different orthonormal Hermite polynomial basis functions (OHPBFs) as activation functions is introduced. Then, to further mitigate the influence of noise and improve the accuracy of prediction, an empirical mode decomposition (EMD) method is derived to decompose the original short-term traffic flow data into several intrinsic mode functions (IMFs) and adopt them as the inputs for the RHNNs. Therefore, an ERHNN prediction model, which comprises good predictive ability for the nonlinear and non-stationary signals through the combination of the merits of OHPBFs, EMD and EHNN, is proposed to predict short-term traffic flow more effectively. The validity of the ERHNN prediction model is verified using all day short-term traffic flow data at high way I-80W in California. Simulation results demonstrate that the proposed ERHNN prediction model is with superior performance compared with the pure recurrent neural network (RNN) and RHNN prediction models.

Original languageEnglish
Title of host publication2012 15th International IEEE Conference on Intelligent Transportation Systems, ITSC 2012
Pages1821-1826
Number of pages6
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event2012 15th International IEEE Conference on Intelligent Transportation Systems, ITSC 2012 - Anchorage, AK, United States
Duration: 2012 Sep 162012 Sep 19

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

Other

Other2012 15th International IEEE Conference on Intelligent Transportation Systems, ITSC 2012
CountryUnited States
CityAnchorage, AK
Period12/9/1612/9/19

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Short-term traffic flow prediction using EMD-based recurrent Hermite neural network approach'. Together they form a unique fingerprint.

  • Cite this

    Chen, S. Y., & Chou, W. Y. (2012). Short-term traffic flow prediction using EMD-based recurrent Hermite neural network approach. In 2012 15th International IEEE Conference on Intelligent Transportation Systems, ITSC 2012 (pp. 1821-1826). [6338665] (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC). https://doi.org/10.1109/ITSC.2012.6338665