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

Mohammadreza Khajeh-Hosseini, Alireza Talebpour

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

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.

Original languageEnglish (US)
Title of host publication2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1367-1372
Number of pages6
ISBN (Electronic)9781538670248
DOIs
StatePublished - Oct 2019
Externally publishedYes
Event2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 - Auckland, New Zealand
Duration: Oct 27 2019Oct 30 2019

Publication series

Name2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019

Conference

Conference2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
CountryNew Zealand
CityAuckland
Period10/27/1910/30/19

ASJC Scopus subject areas

  • Artificial Intelligence
  • Management Science and Operations Research
  • Instrumentation
  • Transportation

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  • Cite this

    Khajeh-Hosseini, M., & Talebpour, A. (2019). Back to the Future: Predicting Traffic Shockwave Formation and Propagation Using a Convolutional Encoder-Decoder Network. In 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 (pp. 1367-1372). [8917430] (2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITSC.2019.8917430