@inproceedings{8bf2703fe40e407a9e16596cb8d4e85b,
title = "Back to the Future: Predicting Traffic Shockwave Formation and Propagation Using a Convolutional Encoder-Decoder Network",
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.",
author = "Mohammadreza Khajeh-Hosseini and Alireza Talebpour",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 ; Conference date: 27-10-2019 Through 30-10-2019",
year = "2019",
month = oct,
doi = "10.1109/ITSC.2019.8917430",
language = "English (US)",
series = "2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1367--1372",
booktitle = "2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019",
address = "United States",
}