Open Access
Issue
E3S Web Conf.
Volume 253, 2021
2021 International Conference on Environmental and Engineering Management (EEM 2021)
Article Number 01025
Number of page(s) 4
Section Intelligent Environmental Monitoring and Quality Technology Assessment
DOI https://doi.org/10.1051/e3sconf/202125301025
Published online 06 May 2021
  1. Vlahogianni, E. I., Karlaftis, M. G., Golias, J. (2014). Short-term traffic forecasting: Where we are and where we’re going. Transportation Research Part C-emerging Technologies,, 3–19. [Google Scholar]
  2. Liu, M. Y., Wu, J. P., Wang, Y. B. (2018). Traffic flow prediction based on deep learning. Journal of System Simulation, 11:4100–4105+4114. [Google Scholar]
  3. Luo, W.H., Dong, B.T., Wang, Z.S. (2017). Short-term traffic flow prediction based on CNN-SVR hybrid deep learning model. Journal of Transportation Systems Engineering and Information Technology, 17(05):68–74. [Google Scholar]
  4. He, Y.X., Yin, F., Yuan, P. (2020). Survey of short-term traffic flow prediction models in intelligent transportation system. Modern Computer. [Google Scholar]
  5. Pascanu, R., Mikolov, T., Bengio, Y. (2013). On the difficulty of training recurrent neural networks. international conference on machine learning. [Google Scholar]
  6. Tian, Y., Pan, L. (2015). Predicting Short-Term Traffic Flow by Long Short-Term Memory Recurrent Neural Network. ieee international conference on smart city socialcom sustaincom. [Google Scholar]
  7. Zeiler, M. D., Fergus, R. (2014). Visualizing and Understanding Convolutional Networks. european conference on computer vision. [Google Scholar]
  8. Salman, A. G., Heryadi, Y., Abdurahman, E.,Suparta, W. (2018). Single Layer & Multi-layer Long Short-Term Memory (LSTM) Model with Intermediate Variables for Weather Forecasting. Procedia Computer Science,, 89–98. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.