Urban Traffic Flow Prediction Using LSTM and GRU-Reference-Cited by-同舟云学术

Urban Traffic Flow Prediction Using LSTM and GRU

Published:2024-01-02 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 5th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability
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Author:

Jang Hung-Chin¹^ORCID,Chen Che-An¹

Affiliation:

1. Department of Computer Science, National Chengchi University, Taipei City 116302, Taiwan

Publisher

MDPI

Link

https://www.mdpi.com/2673-4591/55/1/86/pdf

Reference8 articles.

1. Jang, H.C., and Lin, T.K. (2018, January 27–30). Traffic-aware traffic signal control framework based on SDN and cloud-fog computing. Proceedings of the 2018 IEEE 88th Vehicular Technology Conference (VTC 2018-Fall), Chicago, IL, USA.

2. Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3–6). ImageNet classification with deep convolutional neural networks. Proceedings of the NIPS’12, 25th International Conference on Neural Information Processing Systems, Red Hook, NY, USA.

3. Jang, H.C., and Chang, Y.H. (2019, January 6–9). Traffic flow forecast for traffic with forecastable sporadic events. Proceedings of the 12th International Conference on Ubi-Media Computing (Ubi-Media 2019), Bali, Indonesia.

4. Du, X., Zhang, H., Nguyen, H.V., and Han, Z. (2017, January 24–27). Stacked LSTM deep learning model for traffic prediction in vehicle-to-vehicle communication. Proceedings of the 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall), Toronto, ON, Canada.

5. Chen, Y.Y., Lv, Y., Li, Z., and Wang, F.Y. (2016, January 1–4). Long short-term memory model for traffic congestion prediction with online open data. Proceedings of the IEEE 19th International Conference on Intelligent Transportation System, Rio de Janeiro, Brazil.