Enhancing road traffic flow prediction with improved deep learning using wavelet transforms

Fouzi Harrou*, Abdelhafid Zeroual, Farid Kadri, Ying Sun

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

Precise traffic flow prediction is a central component of advancing intelligent transportation systems, providing essential insights for optimizing traffic management, reducing travel times, and alleviating congestion. This study introduces an efficient deep learning approach that synergistically integrates the benefits of wavelet-based denoising and Recurrent Neural Networks (RNNs). This integrated methodology is introduced to effectively capture the inherent nonlinearity and temporal dependencies in time series traffic data. Specifically, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are introduced to address the challenges associated with accurately forecasting traffic flow. To enhance prediction quality, traffic flow data is preprocessed using exponential smoothing and wavelet-based filtering as denoising filters, effectively eliminating outliers. The effectiveness of the proposed techniques is evaluated using traffic measurements collected from diverse highway locations across California, including the Old Bayshore highway, situated south of Interstate 880 (I880), and the Ashby Ave highway, positioned west of Interstate 80 (I80) in the San Francisco Bay Area. The results obtained through integrating both architectures, including LSTM and GRU, within the wavelet transform-based filter demonstrate an enhancement in forecasting performance. Specifically, LSTM with wavelet-based denoising using Symlet and Haar wavelets achieved high prediction performance with an average R2 of 0.982 and 0.9811, respectively.

Original languageEnglish (US)
Article number102342
JournalResults in Engineering
Volume23
DOIs
StatePublished - Sep 2024

Keywords

  • Deep learning
  • Denoising
  • Exponential smoothing
  • Traffic flow prediction
  • Wavelet filter

ASJC Scopus subject areas

  • General Engineering

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