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Title	Urban Mobility Optimization With Iot And Deep Q Networks For Traffic Density Management
ID_Doc	60077
Authors	Ranganathan C.S.; Jayanthi L.N.; Babu J.J.; Manikandan M.; Sivakamy N.
Year	2024
Published	2024 3rd International Conference for Innovation in Technology, INOCON 2024
DOI	http://dx.doi.org/10.1109/INOCON60754.2024.10511802
Abstract	Traffic density and mobility optimization in urban areas are becoming more difficult to manage to improve transportation efficiency. Urban traffic management is complicated, but this research provides a revolutionary technique using the Internet of Things (IoT) and Deep Q Networks (DQN). Traffic lights, sensors, and vehicles may be monitored in real-time using IoT technology to provide a complete picture of urban mobility. As an intelligent decision-making agent, the DQN uses reinforcement learning to adapt to changing traffic circumstances and optimize traffic density management. The model is educated on historical data and refines its tactics via environmental interaction, solving urban transportation problems. A complex data fusion mechanism processes data from different IoT devices. A reinforcement learning framework uses DQN for decision-making, and a feedback loop updates and optimizes traffic management tactics. The research uses simulations and urban case studies to prove that the technique works. The findings show improved traffic flow, congestion, and mobility efficiency. It uses IoT and DQN to develop intelligent urban transport systems that scale and respond to traffic density in complex and dynamic urban environments. © 2024 IEEE.
Author Keywords	Real-time Data; Smart Cities; Traffic Congestion; Transportation Optimization; Urban Data Analysis

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