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Title	Optimizing Urban Mobility In Smart Cities Through Deep Learning-Based Traffic Management
ID_Doc	40935
Authors	Chandi Priya K.G.; Sharma S.; Kumar M.S.; Gangwar P.K.; Aarthi R.; Kumar R.S.
Year	2024
Published	2024 15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024
DOI	http://dx.doi.org/10.1109/ICCCNT61001.2024.10724627
Abstract	Urban mobility remains a substantial obstacle in intelligent urban areas. This research tackles the limitations in traffic control using deep learning AlexNet architecture. With urbanization and increasing vehicles, it is necessary to optimize traffic flow, enhance transportation efficiency and minimize congestion. The study shows the challenges in traffic management systems, such as incapacity and inflexibility to adapt to urban surroundings. A challenge arises on deep learning AlexNet to precisely forecast traffic patterns. The real-time traffic data utilisaiton facilitates the AlexNet training with precise forecasts and traffic control. The results indicates traffic flow efficiency, congestion reduction, and urban mobility improvement. © 2024 IEEE.
Author Keywords	AlexNet; Deep Learning; Smart Cities; Traffic Management; Urban Mobility

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