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A hybrid deep and machine learning model for short-term traffic volume forecasting of adjacent intersections

Mirzahossein, H ; Sharif University of Technology | 2022

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  1. Type of Document: Article
  2. DOI: 10.1049/itr2.12224
  3. Publisher: John Wiley and Sons Inc , 2022
  4. Abstract:
  5. Despite complex fluctuations, missing data, and maintenance costs of detectors, traffic volume forecasting at intersections is still a challenge. Moreover, most existing forecasting methods consider an isolated intersection instead of multiple adjacent ones. By accurately forecasting the volume of short-term traffic, a low-cost method can be provided to solve the problems of congestion, delay, and breakdown of detectors in the road transport system. This paper outlines a novel hybrid method based on deep learning to estimate short-term traffic volume at three adjacent intersections. The gated recurrent unit (GRU) and long short-term memory (LSTM) bilayer network with wavelet transform (WL) noise reduction algorithm (WL+GRU-LSTM) are used to analyze raw traffic volume data. The WL+GRU-LSTM is constructed by comparing different machine learning and deep learning methods. A comparative study was used to choose the model’s network structure, training technique, and optimizer type. To prove the model’s accuracy and resilience, it was compared with the leading short-term traffic forecasting approaches. Experimental results confirm that the WL+GRU-LSTM model can forecast complex traffic volume fluctuations in different approaches of intersections with an accuracy of over 94%. It also shows better results compared to current methods. The proposed model could replace intermediate loop detectors. © 2022 The Authors. IET Intelligent Transport Systems published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology
  6. Keywords:
  7. Complex networks ; Forecasting ; Intelligent systems ; Learning systems ; Long short-term memory ; Traffic congestion ; Adjacent intersections ; Data costs ; Forecasting methods ; Isolated intersection ; Machine learning models ; Maintenance cost ; Missing data ; Short-term traffic volume ; Traffic volume forecasting ; Wavelets transform ; Wavelet transforms
  8. Source: IET Intelligent Transport Systems ; Volume 16, Issue 11 , 2022 , Pages 1648-1663 ; 1751956X (ISSN)
  9. URL: https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/itr2.12224