Back to the Future: Predicting Traffic Shockwave Formation and Propagation Using a Convolutional Encoder-Decoder Network

Mohammadreza Khajeh-Hosseini; Alireza Talebpour

Back to the Future: Predicting Traffic Shockwave Formation and Propagation Using a Convolutional Encoder-Decoder Network

Machine Learning 2019-05-08 v1 Computer Vision and Pattern Recognition Signal Processing

Authors: Mohammadreza Khajeh-Hosseini , Alireza Talebpour

Abstract

This study proposes a deep learning methodology to predict the propagation of traffic shockwaves. The input to the deep neural network is time-space diagram of the study segment, and the output of the network is the predicted (future) propagation of the shockwave on the study segment in the form of time-space diagram. The main feature of the proposed methodology is the ability to extract the features embedded in the time-space diagram to predict the propagation of traffic shockwaves.

Keywords

traffic forecasting encoder-decoder architecture deep learning

Cite

@article{arxiv.1905.02197,
  title  = {Back to the Future: Predicting Traffic Shockwave Formation and Propagation Using a Convolutional Encoder-Decoder Network},
  author = {Mohammadreza Khajeh-Hosseini and Alireza Talebpour},
  journal= {arXiv preprint arXiv:1905.02197},
  year   = {2019}
}

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