Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
97 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

A Multi-Channel Spatial-Temporal Transformer Model for Traffic Flow Forecasting (2405.06266v1)

Published 10 May 2024 in cs.AI

Abstract: Traffic flow forecasting is a crucial task in transportation management and planning. The main challenges for traffic flow forecasting are that (1) as the length of prediction time increases, the accuracy of prediction will decrease; (2) the predicted results greatly rely on the extraction of temporal and spatial dependencies from the road networks. To overcome the challenges mentioned above, we propose a multi-channel spatial-temporal transformer model for traffic flow forecasting, which improves the accuracy of the prediction by fusing results from different channels of traffic data. Our approach leverages graph convolutional network to extract spatial features from each channel while using a transformer-based architecture to capture temporal dependencies across channels. We introduce an adaptive adjacency matrix to overcome limitations in feature extraction from fixed topological structures. Experimental results on six real-world datasets demonstrate that introducing a multi-channel mechanism into the temporal model enhances performance and our proposed model outperforms state-of-the-art models in terms of accuracy.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (34)
  1. B. Medina-Salgado, E. Sanchez-DelaCruz, P. Pozos-Parra, and J. E.Sierra, “Urban Traffic Flow Prediction Techniques: A review,” Sustainable Computing: Informatics and Systems, vol. 35, pp. 100739, September 2022.
  2. W. Jiang and J. Luo, “Graph Neural Network for Traffic Forecasting: A Survey,” Expert Systems with Applications, vol. 207, pp. 117921, 30 November 2022.
  3. X. Ma, Z. Dai, Z. He, J. Ma, Y. Wang and Y. Wang, “Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction,” Sensors, vol. 17, no. 4, pp. 818, 10 April 2017.
  4. W. Weng, J. Fan, H. Wu, Y. Hu, H. Tian, et al, “A Decomposition Dynamic Graph Convolutional Recurrent Network for Traffic Forecasting,” Pattern Recognition, vol. 142, pp. 109670, October 2023.
  5. W. Fang, W. Zhuo, Y. Song, J. Yan, T. Zhou, et al, “Δf⁢r⁢e⁢esubscriptΔ𝑓𝑟𝑒𝑒\varDelta_{free}roman_Δ start_POSTSUBSCRIPT italic_f italic_r italic_e italic_e end_POSTSUBSCRIPT-LSTM: An Error Distribution Free Deep Learning for Short-Term Traffic Flow Forecasting,” Neurocomputing, vol. 526, pp. 180-190, March 2023.
  6. W. Zhang, R. Yao, X. Du, Y. Liu, R. Wang, et al, “Traffic Flow Prediction Under Multiple Adverse Weather Based on Self-Attention Mechanism and Deep Learning Models,” Physica A: Statistical Mechanics and its Applications, pp. 128988, 2023.
  7. N. Zhang, Y. Zhang and H. Lu, “Seasonal Autoregressive Integrated Moving Average and Support Vector Machine Models: Prediction of Short-Term Traffic Flow on Freeways,” Transportation Research Record, vol. 2215, no. 1, pp. 85-92, 2011.
  8. X. Xiao and H. Duan, “A New Grey Model for Traffic Flow Mechanics,” Engineering Applications of Artificial Intelligence, vol. 88, pp. 103350, 2020.
  9. S. V. Kumar, “Traffic Flow Prediction Using Kalman Filtering Technique,” Procedia Engineering, vol. 187, pp. 582-587, 2017.
  10. J. Ahn, E. Ko and E. Y. Kim, “Highway Traffic Flow Prediction Using Support Vector Regression and Bayesian Classifier,” 2016 International conference on big data and smart computing (BigComp), pp. 239-244, 2016.
  11. L. Zhang, Q. Liu, W. Yang, N. Wei and D. Dong, “An Improved K-nearest Neighbor Model for Short-Term Traffic Flow Prediction,” Procedia-Social and Behavioral Sciences, vol. 96, pp. 653-662, 2013.
  12. E. Castillo, J. M. Menéndez and S. Sánchez-Cambronero, “Predicting Traffic Flow Using Bayesian Networks,” Transportation Research Part B: Methodological, vol. 42, no. 5, pp. 482-509, 2008.
  13. H. Yin, S. Wong, J. Xu and C. K. Wong, “Urban Traffic Flow Prediction Using a Fuzzy-Neural Approach,” Transportation Research Part C: Emerging Technologies, vol. 10, no. 2, pp. 85-98, 2002.
  14. T. N. Kipf and M. Welling, “Semi-Supervised Classification with Graph Convolutional Networks,” arXiv preprint arXiv:1609.02907, 2016.
  15. L. Zhao, Y. Song, C. Zhang, Y. Liu, P. Wang, et al, “T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 9, pp. 3848-3858, 2019.
  16. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, K. Jones, et al, “Attention Is All You Need,” Advances in Neural Information Processing Systems, pp. 5998-6008, 2017.
  17. B. Yu, H. Yin and Z. Zhu,“Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting,” arXiv preprint arXiv:1709.04875, 2017.
  18. Y. J. Lu and C. T. Li, “AGSTN: Learning Attention-Adjusted Graph Spatio-Temporal Networks for Short-Term Urban Sensor Value Forecasting,” in IEEE International Conference on Data Mining, 2020.
  19. M. A. Ali, S. Venkatesan, V. Liang and H. Kruppa, “TEST-GCN: Topologically Enhanced Spatial-Temporal Graph Convolutional Networks for Traffic Forecasting,” in IEEE International Conference on Data Mining, pp. 982-987, 2021.
  20. B. Hui, D. Yan, H. Chen and W. S. Ku, “Trajectory WaveNet: A Trajectory-Based Model for Traffic Forecasting,” in IEEE International Conference on Data Mining, pp. 1114-1119, 2021.
  21. M. Liu, G. Liu and L. Sun, “Spatial-Temporal Dependence and Similarity aware Traffic Flow Forecasting,” Information Sciences, vol. 625, pp. 81-96, 2023.
  22. Q. Zhang, C. Yin, Y. Chen and F. Su, “IGCRRN: Improved Graph Convolution Res-Recurrent Network for Spatio-Temporal Dependence Capturing and Traffic Flow Prediction,” Engineering Applications of Artificial Intelligence, vol. 114, pp. 105179, 2022.
  23. Q. Zheng and Y. Zhang, “Dynamic Spatial-Temporal Adjacent Graph Convolutional Network for Traffic Forecasting,” IEEE Transactions on Big Data, 2022.
  24. S. Yang, J. Liu and K. Zhao “Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecasting,” in IEEE International Conference on Data Mining, pp. 817-826, 2021.
  25. X. Huang, J Wang, Y Lan, C. Jiang and X. Yuan, “MD-GCN: A Multi-Scale Temporal Dual Graph Convolution Network for Traffic Flow Prediction,” Sensors, vol. 23, no. 2, pp. 841, 2023.
  26. J. Chen, K. Li, K. Li, P. S. Yu and Z. Zeng, “Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-Sharing System Using Gated Graph Neural Network,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 12, no. 2, pp. 1-22, 2021.
  27. B. Pu, J. Liu, Y. Kang, J. Chen and S. Y. Philip, “MVSTT: A Multiview Spatial-Temporal Transformer Network for Traffic-Flow Forecasting,” IEEE transactions on cybernetics, 2022.
  28. M. Xu, W. Dai, C. Liu, X. Gao and G. J. Qi, “Spatial-Temporal Transformer Networks for Traffic Flow Forecasting,” arXiv preprint arXiv:2001.02908, 2020.
  29. W. Fang, W. Zhuo, J. Yan, Y. Song and D. Jiang, et al, “Attention Meets Long Short-Term Memory: A Deep Learning Network for Traffic Flow Forecasting,” Physica A: Statistical Mechanics and its Applications, vol. 587, pp. 126485, 2022.
  30. M. Méndez, M. G. Merayo, and M. Núñez, “Long-Term Traffic Flow Forecasting Using a Hybrid CNN-BiLSTM Model,” Engineering Applications of Artificial Intelligence, vol. 121, pp. 106041, 2023.
  31. Y. Djenouri, A. Belhadi, G. Srivastava and J. C. W. Lin, “Hybrid Graph Convolution Neural Network and Branch-and-Bound Optimization for Traffic Flow Forecasting,” Future Generation Computer Systems, vol. 139, pp. 100-108, 2023.
  32. Z. Wu, S. Pan, G Long, J. Jiang and C. Zhang, “Graph WaveNet for Deep Spatial-Temporal Graph Modeling,” in Proceedings of the 28th International Joint Conference on Artificial Intelligence, 10 August 2019.
  33. S. Guo, Y. Lin, N. Feng, C. Song and H. Wan, “Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting,” in Proceedings of the AAAI conference on artificial intelligence, vol. 33, no. 01, pp. 922-929, 2019.
  34. C. Song, Y. Lin, S. Guo and H. Wan, “Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network data forecasting,” in Proceedings of the AAAI conference on artificial intelligence, vol. 34, no. 01, pp. 914-921, 2020.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (2)
  1. Jianli Xiao (7 papers)
  2. Baichao Long (3 papers)

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets