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Smart city article details

Title	Nano Scale Machine Learning Intelligence-Based User Behaviour Analysis (Ni-Ubaa) For Smart Urban Traffic Planning
ID_Doc	38780
Authors	Nandy M.; Dubey A.
Year	2024
Published	Nanotechnology Perceptions, 20, S1
DOI	http://dx.doi.org/10.62441/nano-ntp.v20iS1.78
Abstract	In the fast-growing world, smart cities' rapid and inevitable development significantly affects urban planning and development policies. One of the most important aspects of smart city management is monitoring, analyzing, and forecasting urban user behavior (hottest spots, trajectories, flows, etc.).In urban planning, the traffic pattern is an extended term that requires firm refinement of transportation policies, posing significant challenges in a smart city environment. Developing methodologies and tools for analyzing people's behavior in cities is essential in this environment. Hence, this study, the In-Depth Learning-based Integrated Urban Planning and Development Framework (DLI-UPDF), has been proposed to support policy-making in smart cities to improve traffic patterns for modern public transport. The Smart Urban Traffic Planning (SUTP) method uses the Internet of Things (IoT) to optimize red and green signals for both vehicle and pedestrian flow control. The authors address smart vehicles and social networks' possible use to identify and mitigate traffic congestions quickly and accurately evaluate the latest innovations in the different processes involved in a Parallel Transportation Scheme (PTS). Using Wi-Fi Access Points to monitor and analyze city user behavior is discussed in this article, which provides a high level of accuracy. This technique is shown using heat maps, origin-destination matrices, and estimates of user density. The Experimental results show that the proposed DLI-UPDF method and IoT optimize the traffic flow to enhance accuracy, prediction ratio, Flexibility, Efficiency, and performance ratio compared to other existing methods. © 2024, Collegium Basilea. All rights reserved.
Author Keywords	deep learning; integrated framework; Nano Scale Machine learning intelligence; policy-making; Smart city; urban planning; User Behaviour Analysis

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