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Smart city article details

Title	Traffic Prediction System Using Iot In Smart City Perspective
ID_Doc	58655
Authors	Shanthi D.L.; Prasanna K.; Desai V.; Agarwal S.; Shetty V.M.M.; Rakesh A.S.
Year	2021
Published	2021 IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2021
DOI	http://dx.doi.org/10.1109/ICMNWC52512.2021.9688355
Abstract	The development of sensors and their integration with the environment enables us to build smarter applications that suit modern city needs. A Road network is the backbone of any country's development structure. The free flow of traffic on the highways is vital for better mobility and transportation. These problems necessitate the development of smarter technologies focused on the Internet of Things, which recognize real-time data in the monitored area and analyze urban transportation flow in a smart city road. For a variety of road users, including commuters, private vehicle drivers, and the public transportation system, reliable traffic flow information is critical. This data would aid passengers in choosing the best mode of transportation, improving road flows, reducing noise, and reducing traffic congestion. The proposed system provides solutions to these problems partly. This system predicts the congestion in the road traffic and provides information ahead of time. The importance of traffic congestion prediction has grown in tandem with the rapid development of Smart Transportation Networks for convenient transportation. This paper illustrates a smart traffic management infrastructure based on Internet of Things (IoT), traffic is predicted using KNN algorithm and information is projected to smart devices through Android application. The outcome is smart predictive system with suggested alternates. The system can be further enhanced by installing display panels integrated with cloud computing at key places that shows alternate routes with time constraints. Also the performance and accuracy of application can be tested with different machine learning algorithms.
Author Keywords	Android; Cloud computing; Congestion; Infrared sensors; Internet of Things; predict; smart cities

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