Smart City Gnosys

Smart city article details

Title	Spatio-Temporal Traffic Prediction In Urban Area Using Convolutional Temporal Network System
ID_Doc	52587
Authors	Priya K.; Omkar K.; Naresh R.; Venkatesh K.
Year	2025
Published	6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025 - Proceedings
DOI	http://dx.doi.org/10.1109/ICMCSI64620.2025.10883158
Abstract	Traffic prediction in urban areas is essential for smart city applications, enabling efficient transportation management, reduced congestion, and improved quality of life. Spatiotemporal traffic prediction is particularly challenging due to complex spatial and temporal dependencies in traffic data. This study introduces a Convolutional-Temporal Neural Net- work System (CTNS) model that combines Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to capture spatial and temporal features simultaneously for more accurate traffic forecasting. CNNs are employed to learn spatial correlations between different locations in the urban area, while RNNs, specifically Long Short-Term Memory (LSTM) networks, capture the sequential dependencies in temporal traffic patterns. Using real-world traffic data, the hybrid CTNS model demonstrates superior performance in accurately predicting traffic flow compared to traditional models. This approach highlights the potential of deep learning architectures for enhancing the accuracy of traffic predictions, which is critical for urban planning and intelligent transportation systems. Extensive experiments on real-world urban traffic datasets validate the model’s ability to achieve high accuracy, scalability, and robustness in handling complex spatio-temporal interactions. © 2025 IEEE.
Author Keywords	CNN; CTNS; deep learning; real-time; RNN; spatio-temporal; statistical; Traffic prediction

Similar Articles

Id	Similarity	Authors	Title	Published
60735	0.935	Kumari M.; Ulmas Z.; Suseendra R.; Ramesh J.V.N.; El-Ebiary Y.A.B., Prof.	Utilizing Federated Learning For Enhanced Real-Time Traffic Prediction In Smart Urban Environments	International Journal of Advanced Computer Science and Applications, 15, 2 (2024)
18083	0.932	Tang J.; Zhu R.; Wu F.; He X.; Huang J.; Zhou X.; Sun Y.	Deep Spatio-Temporal Dependent Convolutional Lstm Network For Traffic Flow Prediction	Scientific Reports, 15, 1 (2025)
29827	0.928	Chen B.; Hu K.; Li Y.; Miao L.	Hybrid Spatio-Temporal Graph Convolution Network For Short-Term Traffic Forecasting	IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2022-October (2022)
52608	0.927	Wu S.	Spatiotemporal Dynamic Forecasting And Analysis Of Regional Traffic Flow In Urban Road Networks Using Deep Learning Convolutional Neural Network	IEEE Transactions on Intelligent Transportation Systems, 23, 2 (2022)
4883	0.924	Cao S.; Wu L.; Wu J.; Wu D.; Li Q.	A Spatio-Temporal Sequence-To-Sequence Network For Traffic Flow Prediction	Information Sciences, 610 (2022)
51592	0.923	Pritha A.; Fathima G.	Smart Traffic Management: A Deep Learning Revolution In Traffic Prediction - A Review	IET Conference Proceedings, 2024, 23 (2024)
4890	0.919	Zhang Y.; Zhang F.	A Spatiotemporal Neural Network Model For City Traffic Prediction	Proceedings of SPIE - The International Society for Optical Engineering, 12970 (2023)
35590	0.919	Remmouche B.; Boukraa D.; Zakharova A.; Bouwmans T.; Taffar M.	Long-Term Spatio-Temporal Graph Attention Network For Traffic Forecasting	Expert Systems with Applications, 288 (2025)
32617	0.919	J J.S.; G A.K.; kumar E.; Raju K.N.; Sudha V.; Kshirsagar P.R.; Tirth V.; Rajaram A.	Intelligent Traffic Prediction System Using Hybrid Convolutional Neural Networks For Smart Cities	Multimedia Tools and Applications (2024)
52517	0.919	Huang X.; Pan Z.; Zhao G.	Spatial-Temporal Interactive Graph Convolutional Networks For Traffic Forecasting	2024 4th International Conference on Electronic Information Engineering and Computer Technology, EIECT 2024 (2024)