Optimized Traffic Flow in Smart Cities Using Precision manufacturing empowered Reinforcement Learning for Urban Sustainable Mobility

Smart cities require efficient traffic management to enhance urban mobility and sustainability. Precision manufacturing empowered Reinforcement Learning (RL) has emerged as a promising approach to optimize traffic flow by leveraging real-time data and intelligent decision-making. However, traditional traffic control methods suffer from inefficiencies such as static rule-based systems, congestion mismanagement, and slow adaptability to dynamic traffic conditions. To address these issues, here propose an Optimized Traffic Flow in Smart Cities using Precision manufacturing empowered Reinforcement Learning (OTF-SC-RL) framework, which dynamically adjusts traffic signals and routing based on real-time data and predictive analytics. The proposed method integrates an Adaptive Traffic Flow Optimization (RL-ATFO) with a hybrid Artificial Neural Network–Recurrent Neural Network (ANN–RNN) model to enhance decision-making capabilities, improve traffic adaptability, and minimize congestion. Experimental results demonstrate that the OTF-SC-RL framework significantly reduces travel time, improves vehicle throughput, and enhances overall urban mobility compared to existing approaches. The findings highlight the effectiveness of the proposed framework in achieving higher efficiency, sustainability, and adaptability in smart city transportation networks.