Open Access
Issue
ITM Web Conf.
Volume 70, 2025
2024 2nd International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2024)
Article Number 01005
Number of page(s) 7
Section Traffic Prediction and Analysis
DOI https://doi.org/10.1051/itmconf/20257001005
Published online 23 January 2025
  1. U. N. Desa. World Population Prospects 2019: Highlights. New York (US): United Nations Department for Economic and Social Affairs, 11(1): 125 (2019). [Google Scholar]
  2. B. Medina-Salgado, E. Sánchez-DelaCruz, P. Pozos-Parra, & J. E. Sierra. Urban traffic flow prediction techniques: A review. Sustain. Comput. Inform. Syst., 35, 100739 (2022). [Google Scholar]
  3. R. S. Sutton & A. G. Barto. Reinforcement Learning: An Introduction. MIT Press (2018). [Google Scholar]
  4. H. Wang, Y. Yuan, X. T. Yang, T. Zhao, & Y. Liu. Deep Q learning-based traffic signal control algorithms: Model development and evaluation with field data. J. Intell. Transp. Syst., 27 (3), 314-334 (2023). [CrossRef] [Google Scholar]
  5. A. Agafonov & V. Myasnikov. Traffic signal control: A double q-learning approach. In 2021 16th Conference on Computer Science and Intelligence Systems (FedCSIS) (pp. 365–369) (2021). [Google Scholar]
  6. N. T. T. Le, D. Tran-Anh, Q. B. Bui, & L. M. Pham. Double Deep Q-Network algorithm for solving traffic congestion on one-way highways. J. Water Resour. Environ. Eng., 87 (12), 39 (2023). [Google Scholar]
  7. V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, & M. Riedmiller. Playing Atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602 (2013). [Google Scholar]
  8. V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, … & D. Hassabis. Human-level control through deep reinforcement learning. Nature, 518 (7540), 529-533 (2015). [CrossRef] [PubMed] [Google Scholar]
  9. H. Hasselt. Double Q-learning. Adv. Neural Inf. Process. Syst., 23 (2010). [Google Scholar]
  10. H. Van Hasselt, A. Guez, & D. Silver. Deep reinforcement learning with double Q-learning. Proc. AAAI Conf. Artif. Intell., 30(1) (2016). [Google Scholar]
  11. H. Wei, G. Zheng, H. Yao, & Z. Li. Intellilight: A reinforcement learning approach for intelligent traffic light control. In Proc. 24th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. (pp. 2496–2505) (2018). [CrossRef] [Google Scholar]
  12. M. Karimzadeh, R. Aebi, A. M. de Souza, Z. Zhao, T. Braun, S. Sargento, & L. Villas. Reinforcement learning-designed LSTM for trajectory and traffic flow prediction. In 2021 IEEE Wireless Commun. Networking Conf. (WCNC) (pp. 1–6) (2021). [Google Scholar]
  13. Z. He, C. Y. Chow, & J. D. Zhang. STCNN: A spatio-temporal convolutional neural network for long-term traffic prediction. In 2019 20th IEEE Int. Conf. Mobile Data Manage. (MDM) (pp. 226–233) (2019). [CrossRef] [Google Scholar]
  14. Y. Liu, H. Zheng, X. Feng, & Z. Chen. Short-term traffic flow prediction with Conv- LSTM. In 2017 9th Int. Conf. Wireless Commun. Signal Process. (WCSP) (pp. 1–6) (2017). [Google Scholar]
  15. B. Du, H. Peng, S. Wang, M. Z. A. Bhuiyan, L. Wang, Q. Gong, … & J. Li. Deep irregular convolutional residual LSTM for urban traffic passenger flows prediction. IEEE Trans. Intell. Transp. Syst., 21 (3), 972-985 (2019). [Google Scholar]
  16. Z. Cui, K. Henrickson, R. Ke, & Y. Wang. Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting. IEEE Trans. Intell. Transp. Syst., 21 (11), 4883-4894 (2019). [Google Scholar]
  17. L. Zhao, Y. Song, C. Zhang, Y. Liu, P. Wang, T. Lin, … & H. Li. T-GCN: A temporal graph convolutional network for traffic prediction. IEEE Trans. Intell. Transp. Syst., 21 (9), 3848-3858 (2019). [Google Scholar]
  18. B. Yu, H. Yin, & Z. Zhu. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017). [Google Scholar]
  19. H. Peng, B. Du, M. Liu, M. Liu, S. Ji, S. Wang, … & L. He. Dynamic graph convolutional network for long-term traffic flow prediction with reinforcement learning. Inf. Sci., 578, 401-416 (2021). [CrossRef] [Google Scholar]
  20. T. Zhang & G. Guo. Graph attention LSTM: A spatiotemporal approach for traffic flow forecasting. IEEE Intell. Transp. Syst. Mag., 14 (2), 190-196 (2020). [CrossRef] [Google Scholar]
  21. L. Cai, M. Lei, S. Zhang, Y. Yu, T. Zhou, & J. Qin. A noise-immune LSTM network for short-term traffic flow forecasting. Chaos: An Interdiscip. J. Nonlinear Sci., 30(2) (2020). [Google Scholar]
  22. H. Zheng, F. Lin, X. Feng, & Y. Chen. A hybrid deep learning model with attentionbased Conv-LSTM networks for short-term traffic flow prediction. IEEE Trans. Intell. Transp. Syst., 22 (11), 6910-6920 (2020). [Google Scholar]
  23. H. Xing, A. Chen, & X. Zhang. RL-GCN: Traffic flow prediction based on graph convolution and reinforcement learning for smart cities. Displays, 80, 102513 (2023). [CrossRef] [Google Scholar]
  24. P. Ø. Kanestrøm. Traffic flow forecasting with deep learning. Master’s thesis, NTNU (2017). [Google Scholar]
  25. F. Rasheed, K. L. A. Yau, R. M. Noor, C. Wu, & Y. C. Low. Deep reinforcement learning for traffic signal control: A review. IEEE Access, 8, 208016-208044 (2020). [CrossRef] [Google Scholar]
  26. C. Wu, L. Chen, G. Wang, S. Chai, H. Jiang, J. Peng, & Z. Hong. Spatiotemporal scenario generation of traffic flow based on LSTM-GAN. IEEE Access, 8, 186191186198 (2020). [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.