Applying Recurrent Neural Network for Passenger Traffic Forecasting-Reference-Cited by-同舟云学术

Applying Recurrent Neural Network for Passenger Traffic Forecasting

Published:2019-03-29 Issue: Volume: Page:68-77
ISSN:2194-5357
Container-title:Advances in Computer Science for Engineering and Education II
language:
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Author:

Hu Zhengbing,Dychka Ivan^ORCID,Oleshchenko Liubov^ORCID,Kukharyev Sergiy^ORCID

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-16621-2_7

Reference16 articles.

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2. Geraldine Bessie Amali, D., Dinakaran, M.: A new quantum tunneling particle swarm optimization algorithm for training feedforward neural networks. Int. J. Intell. Syst. Appl. (IJISA) 10(11), 64–75 (2018). https://doi.org/10.5815/ijisa.2018.11.07

3. Cyprich, O., Konečný, V., Kilianová, K.: Short-term passenger demand forecasting using univariate time series theory. Promet - Traffic Transp. 25(6), 533–541 (2013)

4. Graves, A., Mohamed, A.-R., Hinton, G.: Speech recognition with deep recurrent neural networks. arXiv preprint arXiv:1303.5778 (2013)

5. Greff, К., Srivastava, R.K., Koutník, J., Steunebrink, B.R., Schmidhuber, Jü.: LSTM: A Search Space Odyssey. arXiv preprint arXiv:1503.04069 (2015)

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