Intelligent Road Network Management Supported by 6G and Deep Reinforcement Learning

The high bandwidth, low latency, and extensive coverage of Sixth Generation (6G) communication technology provide robust data support and communication guarantees for intelligent road network management. This study aims to explore the application of 6G communication technology and deep reinforcement learning (DRL) algorithms in smart road network management, with the goal of enhancing the intelligence of traffic management systems. DRL algorithms are capable of handling complex traffic environments and, through self-learning and optimization, can achieve intelligent decision-making and route planning. This study proposes a DRL-based traffic signal control method leveraging 6G communication technology. The core of this method lies in its capability to manage complex and dynamically changing traffic flows, adjusting traffic signal plans based on real-time data to adapt to various traffic conditions. By learning traffic distribution patterns, the model generates appropriate traffic signals for each intersection, thereby optimizing the traffic signal plans. Simulation experiments found that, compared to the Convolutional Neural Network (CNN) algorithm, DRL not only reduced the average travel time by 28.2% but also increased the average travel speed by 26.3%, and significantly reduced the average queue length by 42.9%. These results demonstrate that the proposed DRL algorithm shows significant advantages in alleviating traffic congestion and optimizing traffic signal control. This study offers a novel solution for intelligent road network management and validates the potential of 6G communication technology and DRL algorithms in this field.