2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 8 , Issue 2 , PP: 08-19, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Reem Atassi 1 * , Aditi Sharma 2
Doi: https://doi.org/10.54216/JISIoT.080201
The continuous improvements in the Internet of Things (IoTs) and machine learning (ML) make them the key enabling technologies for intelligent traffic management (ITM).The ability to accurately predict network traffic has been demonstrated as crucial for effective network management and strategic planning. Proactive management of future congestion incidents requires access to reliable long-term forecasting models. Conventional prediction methods often fail to completely capture the spatiotemporal features of the traffic flows because of the complexity of the interdependence between the flows. To this end, we proposed to improve the management of traffic with a novel framework for the predictive modeling of traffic flows. The proposed formwork introduces an improved graph network to capture the positional information in traffic follows. It is also capable of precisely capturing temporal dynamics using an improved bidirectional learning module. An attention mechanism is presented to capture the interactions among spatial and temporal patterns to further empower the predictive power of the model. Proof-of-concept experimentations are conducted on the PeMSD7 dataset, and the results (MAE: 0.197, MSE: 0.13, RMSE: 0.36, ) demonstrate the efficiency of our model over the state-of-the-art.
Intelligent Traffic management systems , IoT, Intelligent Systems , Machine learning
[1] Modi, Y., Teli, R., Mehta, A., Shah, K. and Shah, M., 2022. A comprehensive review on intelligent traffic management using machine learning algorithms. Innovative infrastructure solutions, 7(1), p.128.
[2] J. Jin, H. Guo, J. Xu, X. Wang and F. -Y. Wang, "An End-to-End Recommendation System for Urban Traffic Controls and Management Under a Parallel Learning Framework," in IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 3, pp. 1616-1626, March 2021, doi: 10.1109/TITS.2020.2973736
[3] J. Jin et al., "An Agent-Based Traffic Recommendation System: Revisiting and Revising Urban Traffic Management Strategies," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 11, pp. 7289-7301, Nov. 2022, doi: 10.1109/TSMC.2022.3177027.
[4] J. Liu and O. W. W. Yang, "Using Fuzzy Logic Control to Provide Intelligent Traffic Management Service for High-Speed Networks," in IEEE Transactions on Network and Service Management, vol. 10, no. 2, pp. 148-161, June 2013, doi: 10.1109/TNSM.2013.043013.120264.
[5] F. Zhu, Y. Lv, Y. Chen, X. Wang, G. Xiong and F. -Y. Wang, "Parallel Transportation Systems: Toward IoT-Enabled Smart Urban Traffic Control and Management," in IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 10, pp. 4063-4071, Oct. 2020, doi: 10.1109/TITS.2019.2934991.
[6] D. Nallaperuma et al., "Online Incremental Machine Learning Platform for Big Data-Driven Smart Traffic Management," in IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 12, pp. 4679-4690, Dec. 2019, doi: 10.1109/TITS.2019.2924883.
[7] Y. Liu, C. Yang and Q. Sun, "Thresholds Based Image Extraction Schemes in Big Data Environment in Intelligent Traffic Management," in IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 7, pp. 3952-3960, July 2021, doi: 10.1109/TITS.2020.2994386.
[8] Lee, S., Kim, Y., Kahng, H., Lee, S.K., Chung, S., Cheong, T., Shin, K., Park, J. and Kim, S.B., 2020. Intelligent traffic control for autonomous vehicle systems based on machine learning. Expert Systems with Applications, 144, p.113074.
[9] L. Zhao et al., "T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction," in IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 9, pp. 3848-3858, Sept. 2020, doi: 10.1109/TITS.2019.2935152.
[10] X. Xu, T. Zhang, C. Xu, Z. Cui and J. Yang, "Spatial–Temporal Tensor Graph Convolutional Network for Traffic Speed Prediction," in IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 1, pp. 92-103, Jan. 2023, doi: 10.1109/TITS.2022.3215613.
[11] A. Salamanis, D. D. Kehagias, C. K. Filelis-Papadopoulos, D. Tzovaras and G. A. Gravvanis, "Managing Spatial Graph Dependencies in Large Volumes of Traffic Data for Travel-Time Prediction," in IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 6, pp. 1678-1687, June 2016, doi: 10.1109/TITS.2015.2488593.
[12] Z. Wei et al., "STGSA: A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction," in IEEE/CAA Journal of Automatica Sinica, vol. 10, no. 1, pp. 226-238, January 2023, doi: 10.1109/JAS.2023.123033.
[13] H. Wang, R. Zhang, X. Cheng and L. Yang, "Hierarchical Traffic Flow Prediction Based on Spatial-Temporal Graph Convolutional Network," in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 9, pp. 16137-16147, Sept. 2022, doi: 10.1109/TITS.2022.3148105.
[14] Z. Li et al., "A Multi-Stream Feature Fusion Approach for Traffic Prediction," in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 2, pp. 1456-1466, Feb. 2022, doi: 10.1109/TITS.2020.3026836.
[15] J. J. Q. Yu, C. Markos and S. Zhang, "Long-Term Urban Traffic Speed Prediction With Deep Learning on Graphs," in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 7359-7370, July 2022, doi: 10.1109/TITS.2021.3069234.
[16] Moftah, H. M., & Mohamed, T. M. 2020. A Novel Fuzzy Bat Based Ambulance Detection and Traffic Counting Approach. Journal of Cybersecurity and Information Management, 1(2), 41-54. https://doi.org/10.54216/JCIM.010203.
[17] Abualkishik, A.Z., Almajed, R., & Thompson, W. 2021. Multi-attribute decision-making method for prioritizing autonomous vehicles in real-time traffic management: towards active sustainable transport. International Journal of Wireless and Ad Hoc Communication, 3(2), 91-101. https://doi.org/10.54216/IJWAC.030204
[18] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł. and Polosukhin, I., 2017. Attention is all you need. Advances in neural information processing systems, 30.
