Smart City Gnosys

Smart city article details

Title	Edge Computing In Smart Cities: Enhancing Real-Time Data Processing
ID_Doc	21759
Authors	Rajakumari S.; Natarajan C.; Alone V.N.; Senthilkumar P.; Gayathri R.; John G.J.
Year	2024
Published	2024 2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024
DOI	http://dx.doi.org/10.1109/ICAICCIT64383.2024.10912246
Abstract	One of the most crucial issues that smart cities have to keep addressing as they grow is urban mobility improvement. Maximizing the efficiency of transportation, so lowering congestion, and so optimizing traffic flow can help to solve the growing urbanization and increasing vehicle numbers. This research aims to develop and implement a deep learning method based on DenseNet in order to reach the target of raising the effectiveness of urban traffic management. The unique selling proposition that distinguishes this sector from others is the introduction of modern technologies into a sector that has always depended on conventional methods. The main goal of the work was to apply DenseNet architecture to analyze traffic patterns. Considering the two other strategies, the proposed option has a lot of possibilities. From evaluation criteria-which comprised recall, precision, and F1-score, the proposed solution outperformed the approaches regarded to be state-of- the-art. © 2024 IEEE.
Author Keywords	Deep Learning; DenseNet; Smart Cities; Traffic Management

Similar Articles

Id	Similarity	Authors	Title	Published
40935	0.895	Chandi Priya K.G.; Sharma S.; Kumar M.S.; Gangwar P.K.; Aarthi R.; Kumar R.S.	Optimizing Urban Mobility In Smart Cities Through Deep Learning-Based Traffic Management	2024 15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024 (2024)
21773	0.892	Parveen Banu S.; Patil Y.M.; Somasundaram R.; Santhosh C.; Singh D.P.; Manikandan G.	Edge Computing-Based Short-Term Traffic Flow Forecast For The Smart City Employing 5G Internet Vehicles	Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2024 (2024)
44467	0.892	Saranya V.S.; Subbarao G.; Balakotaiah D.; Bhavsingh M.; Babu K.S.; Dhanikonda S.R.	Real-Time Traffic Flow Optimization Using Adaptive Iot And Data Analytics: A Novel Deepstreamnet Model	4th International Conference on Sustainable Expert Systems, ICSES 2024 - Proceedings (2024)
1395	0.889	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)
51592	0.889	Pritha A.; Fathima G.	Smart Traffic Management: A Deep Learning Revolution In Traffic Prediction - A Review	IET Conference Proceedings, 2024, 23 (2024)
7788	0.888	Bahaddad A.; Almarhabi K.; Alshahrani M.; Mnzool M.; Elhassan A.A.M.; Alzughaibi A.; Alghamdi A.M.	An Efficient Algorithm For Traffic Flow Evaluation On Smart Cities Based On Deep Learning	Thermal Science, 29, 2 (2025)
58592	0.888	Cenni D.; Han Q.	Traffic Flow Prediction Using Uber Movement Data	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 594 LNICST (2024)
35879	0.887	Chaymae T.; Mhamed R.; Soumia Z.	Machine Learning And 5G Edge Computing For Intelligent Traffic Management	International Journal of Advanced Computer Science and Applications, 16, 6 (2025)
58620	0.887	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)
24040	0.886	Ismaeel A.G.; Mary J.; Chelliah A.; Logeshwaran J.; Mahmood S.N.; Alani S.; Shather A.H.	Enhancing Traffic Intelligence In Smart Cities Using Sustainable Deep Radial Function	Sustainability (Switzerland), 15, 19 (2023)