Smart City Gnosys

Smart city article details

Title	Real-Time Traffic Flow Optimization Using Adaptive Iot And Data Analytics: A Novel Deepstreamnet Model
ID_Doc	44467
Authors	Saranya V.S.; Subbarao G.; Balakotaiah D.; Bhavsingh M.; Babu K.S.; Dhanikonda S.R.
Year	2024
Published	4th International Conference on Sustainable Expert Systems, ICSES 2024 - Proceedings
DOI	http://dx.doi.org/10.1109/ICSES63445.2024.10763109
Abstract	Thisstudy introduces the DeepStreamNet model, an advanced framework for enhancing real-time traffic management in urban environments using adaptive IoT and sophisticated big data analytics. Central to our approach is the integration of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) units, which synergize with IoT devices to facilitate dynamic adaptation to fluctuating traffic conditions. The model excels in operational performance, achieving a notable accuracy of 94.7% in congestion prediction with an exceptionally low latency of only 45 ms per decision cycle and the capability to process up to 50,000 data points per second. These technical achievements have marked significant improvements over traditional traffic management systems, enhancing traffic flow efficiency and reducing congestion effectively. The results underscore the transformative potential of the DeepStreamNet model in urban traffic management and public safety, offering actionable insights for intelligent transportation systems and setting the stage for more informed urban planning. Looking forward, we suggest extending the application of our model to areas such as logistics and broader smart city initiatives, highlighting its adaptability and broad utility in various urban contexts © 2024 IEEE.
Author Keywords	Adaptive IoT; Data Analytics; DeepStreamNet; Real-Time Processing; Traffic Flow Optimization; Urban Traffic Management

Similar Articles

Id	Similarity	Authors	Title	Published
51592	0.918	Pritha A.; Fathima G.	Smart Traffic Management: A Deep Learning Revolution In Traffic Prediction - A Review	IET Conference Proceedings, 2024, 23 (2024)
8075	0.917	Zheng G.; Chai W.K.; Katos V.	An Ensemble Model For Short-Term Traffic Prediction In Smart City Transportation System	Proceedings - IEEE Global Communications Conference, GLOBECOM (2019)
1395	0.916	Tripathi A.N.; Sharma B.	A Deep Review: Techniques, Findings And Limitations Of Traffic Flow Prediction Using Machine Learning	Lecture Notes in Mechanical Engineering (2023)
40920	0.913	Abdullah S.M.; Periyasamy M.; Kamaludeen N.A.; Towfek S.K.; Marappan R.; Kidambi Raju S.; Alharbi A.H.; Khafaga D.S.	Optimizing Traffic Flow In Smart Cities: Soft Gru-Based Recurrent Neural Networks For Enhanced Congestion Prediction Using Deep Learning	Sustainability (Switzerland), 15, 7 (2023)
23689	0.909	Sundari K.B.T.; Ganesan K.; Justin S.; Yamsani N.; Maranan R.; Ramya M.	Enhanced Traffic Prediction For Smart Cities Through Iot Using Optimized Continual Spatio-Temporal Graph Convolutional Network	2024 IEEE 4th International Conference on ICT in Business Industry and Government, ICTBIG 2024 (2024)
34097	0.908	Manswini Padhy K.; Chattopadhyay S.; Malik N.; Patra J.P.; Perada A.; Raghapriya N.R.	Iot-Enabled Traffic Management Systems Using Cnn-Translstm For Next-Generation Smart Cities	3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025 (2025)
58657	0.908	Selvan C.; Senthil Kumar R.; Iwin Thanakumar Joseph S.; Malin Bruntha P.; Amanullah M.; Arulkumar V.	Traffic Prediction Using Gps Based Cloud Data Through Rnn-Lstm-Cnn Models: Addressing Road Congestion, Safety, And Sustainability In Smart Cities	SN Computer Science, 6, 2 (2025)
34028	0.907	Miao Z.; Liao Q.	Iot-Based Traffic Prediction For Smart Cities	IEEE Access, 13 (2025)
58620	0.906	Almukhalfi H.; Noor A.; Noor T.H.	Traffic Management Approaches Using Machine Learning And Deep Learning Techniques: A Survey	Engineering Applications of Artificial Intelligence, 133 (2024)
7046	0.906	Rathore S.P.S.; Farhaoui Y.; Aniebonam E.E.; Nagpal T.; Thanuja M.; Kaushik P.	Ai-Driven Traffic Congestion Management: A Predictive Analytics Approach For Smart Cities	2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2025 (2025)