Proceedings of International Conference on Applied Innovation in IT
2026/03/31, Volume 14, Issue 1, pp.1-6

An Integrated AI Approach for Real-Time Traffic Forecasting and Congestion Management in Intelligent IoT-Based Networks


Israa Mishkal, Dheyab Salman Ibrahim, Saja Salim Mohammed, Hassan Hadi Saleh, Taha Mohammed Hasan, Ahmed Latif Yasir, Ahmed Abbas Brisam and Rebeen Ali Hamad


Abstract: Traffic congestion remains an unresolved problem in today's urban transportation, particularly in smart infrastructure contexts with the requirement of accurate and timely traffic forecasting to augment realistic congestion improvement. Traditional forecast models such as Auto Regressive Integrated Moving Average (ARIMA), SVR, or shallow neural networks do not grasp the non-linear, spatiotemporal dynamics of real-time traffic and hence lead to inferior route decisions and increased congestion. In this study, a new hybrid artificial intelligence (AI) approach combining Bidirectional Encoder Representations from Transformers (BERT) and Convolutional Long Short-Term Memory (ConvLSTM) is presented to improve the accuracy of traffic prediction and enable proactive congestion control in intelligent Internet of Things (IoT) networks. The architecture has four steps: input representation via real-time sensor data, contextual embedding via BERT to capture temporal and semantic patterns, spatiotemporal modeling via ConvLSTM, and decision generation via reinforcement-based routing adaptation. The model was trained and evaluated using the METR-LA dataset, a real-world spatiotemporal traffic dataset for Los Angeles, and demonstrated improved performance compared to baseline models. Exactly, the proposed model registered a Mean Absolute Error (MAE) of 2.13, a Root Mean Square Error (RMSE) of 3.54, and an R² Score of 0.91, beating LSTM-only, CNN-LSTM, and Transformer-only models. The outcomes confirm the effectiveness of the integrated deep contextual learning and spatiotemporal memory in predicting traffic. The proposed system offers a scalable and sensible paradigm for future traffic management in smart cities.

Keywords: BERT, Traffic Prediction, IoT Networks, Congestion Control, Intelligent Transportation Systems (ITS).

DOI: Under indexing

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References:

  1. M. Li, S. Li, Z. Zhu, and H. Liu, “Traffic Flow Prediction with Spatial-Temporal Graph Diffusion Network,” Proc. AAAI Conf. Artif. Intell., vol. 34, no. 4, pp. 4189-4196, 2020.
  2. B. Yu, H. Yin, and Z. Zhu, “Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting,” in Proc. IJCAI, pp. 3634-3640, 2018.
  3. J. Wang, Y. Zhang, L. Sun, and Y. Li, “ST-MGCN: A Spatial-Temporal Multi-Graph Convolution Network for Traffic Flow Prediction,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 6, pp. 5029-5040, Jun. 2022, [Online]. Available: https://doi.org/10.1109/TITS.2021.3065653.
  4. J. Zhang, Y. Zheng, and D. Qi, “Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction,” in Proc. AAAI Conf. Artificial Intelligence, 2017.
  5. Y. Li, R. Yu, C. Shahabi, and Y. Liu, “Diffusion convolutional recurrent neural network: Data-driven traffic forecasting,” in Proc. Int. Conf. Learning Representations (ICLR), 2018.
  6. B. L. Smith, B. M. Williams, and R. K. Oswald, “Comparison of parametric and nonparametric models for traffic flow forecasting,” Transportation Research Part C: Emerging Technologies, vol. 10, no. 4, pp. 303-321, 2002.
  7. H. H. Saleh, I. A. Mish Khal, and D. S. Ibrahim, “Interference Mitigation in the Vehicular Communication Network Using MIMO Techniques,” Journal of Engineering Science and Technology, vol. 16, no. 2, pp. 1837-1850, Apr. 2021.
  8. W. Huang, G. Song, H. Hong, and K. Xie, “Deep architecture for traffic flow prediction: Deep belief networks with multitask learning,” IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 5, pp. 2191-2201, Oct. 2014, [Online]. Available: https://doi.org/10.1109/TITS.2014.2311123.
  9. X. Ma, Z. Tao, Y. Wang, H. Yu, and Y. Wang, “Long short-term memory neural network for traffic speed prediction using remote microwave sensor data,” Transportation Research Part C, vol. 54, pp. 187-197, 2015.
  10. Z. Cui, R. Ke, and Y. Wang, “Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction,” 2019, [Online]. Available: https://arxiv.org/abs/1801.02143.
  11. X. Shi, Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W.-C. Woo, “Convolutional LSTM network: A machine learning approach for precipitation nowcasting,” in Advances in Neural Information Processing Systems (NeurIPS), 2015.
  12. J. Sun and K. W. Axhausen, “Understanding spatiotemporal travel patterns with deep learning: A review,” Transportation Research Part C, vol. 117, p. 102668, 2020.
  13. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997.
  14. J. Xu, H. Zheng, J. Cao, and Z. Li, “Informer: Efficient Transformer for Long Sequence Time-Series Forecasting,” IEEE Trans. Knowl. Data Eng., vol. 34, no. 10, pp. 5029-5040, Oct. 2022.
  15. A. Vaswani et al., “Attention is all you need,” in Advances in Neural Information Processing Systems (NeurIPS), pp. 5998-6008, 2017.
  16. Z. Zhao, W. Chen, X. Wu, P. C. Chen, and J. Liu, “Traffic-BERT: A Pretrained Model to Understand Traffic Context,” IEEE Internet Things J., vol. 8, no. 12, pp. 9876-9887, Jun. 2021.
  17. Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. S. Yu, “A Comprehensive Survey on Graph Neural Networks,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 1, pp. 4-24, Jan. 2021, [Online]. Available: https://doi.org/10.1109/TNNLS.2020.2978386.
  18. H. H. Saleh and S. T. Hasson, “Improving Communication Reliability in Vehicular Networks Using Diversity Techniques,” Journal of Computational and Theoretical Nanoscience, vol. 16, no. 3, pp. 838-844, Mar. 2019, [Online]. Available: https://doi.org/10.1166/jctn.2019.7963.
  19. Y. Zhang and A. Haghani, “A gradient boosting method to improve travel time prediction,” Transportation Research Part C: Emerging Technologies, vol. 58, pp. 308-324, 2015.
  20. Y. Jiang, S. Yu, X. Wang, and Q. Chen, “Spatio-Temporal Multi-Graph Attention Networks for Urban Traffic Forecasting,” IEEE Trans. Intell. Transp. Syst., vol. 24, no. 5, pp. 5312-5324, May 2023.

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