Smart Urban Traffic Monitoring, Prediction and Congestion Management System

The proposed Smart Urban Traffic Monitoring, Prediction and Congestion Management System integrates traffic monitoring, short-term prediction, congestion analysis, route management, signal optimization, emergency vehicle priority, and multimodal transportation support into a unified platform. The developed system combines traffic-related parameters, connected-road information, visual vehicle detection, external conditions, and traffic management functions to provide a more comprehensive view of urban congestion. This overall approach is consistent with research emphasizing spatial-temporal traffic relationships [1], multimodal traffic information [4], and diverse traffic data sources and forecasting methods [5].

[1] B. Yu, H. Yin, and Z. Zhu, “Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting,” in Proc. 27th Int. Joint Conf. Artif. Intell. (IJCAI), Stockholm, Sweden, 2018, pp. 3634–3640, doi: 10.24963/IJCAI.2018/505. [2] Z. Cui, K. Henrickson, R. Ke, Z. Pu, and Y. Wang, “Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting,” arXiv preprint arXiv:1802.07007, 2018. [3] A. Essien, I. Petrounias, P. Sampaio, and S. Sampaio, “A deep-learning model for urban traffic flow prediction with traffic events mined from Twitter,” World Wide Web, vol. 24, pp. 1345–1368, 2021, doi: 10.1007/s11280-020-00800-3. [4] S. Du, T. Li, X. Gong, and S.-J. Horng, “A hybrid method for traffic flow forecasting using multimodal deep learning,” Int. J. Comput. Intell. Syst., vol. 13, pp. 85–97, 2020, doi: 10.2991/ijcis.d.200120.001. [5] S. Afandizadeh, S. Abdolahi, and H. Mirzahossein, “Deep learning algorithms for traffic forecasting: A comprehensive review and comparison with classical ones,” Journal of Advanced Transportation, vol. 2024, Art. no. 9981657, 2024, doi: 10.1155/2024/9981657.