Urban Traffic Optimization Through Digital Twin Simulations and Deep Reinforcement Learning
DOI:
https://doi.org/10.70088/zgxbpx06Keywords:
Urban Traffic Optimization, Digital Twin Simulations, Deep Reinforcement Learning, Intelligent Traffic Management, Urban MobilityAbstract
Requiring innovative approaches to reduce congestion and improve efficiency, urban traffic optimization is a decisive challenge in modern metropolis. To address these challenge, this research article explores the integrating of digital pretense and deep reinforcement learning (DRL). Enable precise model and psychoanalysis, Digital twins offer -metre, dynamical models of urban traffic systems. DRL, a subset of machine learning, is employ to optimise traffic flow by take adaptive strategies through feedback. The study demonstrate a comprehensive methodology combining these technologies, measure their functioning in simulated surround, and discusses their significance for real-world implementation. Resolution manifest significant advance in traffic flow efficiency, concentrate congestion, thereby and enhance adaptability to urban term. To advancing traffic management systems and lays the groundwork for succeeding enquiry in mobility optimization, this work lend.References
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Copyright (c) 2025 Liwei Zhang (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
