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Title	Intelliflow: A Machine Learning-Driven Dynamic Traffic Management In Smart Cities
ID_Doc	32252
Authors	Alla K.R.; Thangarasu G.
Year	2023
Published	2023 2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023
DOI	http://dx.doi.org/10.1109/SmartTechCon57526.2023.10391320
Abstract	Efficient traffic management in smart cities necessitates advanced technologies and intelligent systems. This research proposes IntelliFlow-an integrated approach that combines machine learning algorithms and Internet of Things (IoT) capabilities for efficient traffic management in smart cities. It employs Random Forests, LSTM Networks and SVM to optimize traffic flow, enhance mobility and improve transportation efficiency. The algorithms are used for tasks such as traffic flow prediction, incident detection, congestion classification, travel time estimation, anomaly detection and traffic signal optimization. By leveraging ensemble learning techniques, IntelliFlow achieves significant improvements in traffic flow optimization, incident detection accuracy and congestion classification compared to individual algorithms. The proposed system presents a comprehensive and effective solution for dynamic decision-making, real-time optimization and improved mobility in smart cities.. © 2023 IEEE.
Author Keywords	Ensemble; Intelligent Transportation System; Internet of Things; LSTM; Machine Learning

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