Real-time traffic flow optimization using large language models and reinforcement learning for smart urban mobility

The exponential growth of metropolitan populations causes transportation network congestion, which increases fuel usage, travel time, and environmental damage. Traditional traffic management systems (TMS) seldom handle these issues in real time. Recently developed Large Language Models (LLMs), especially those using Reinforcement Learning (RL), may enhance urban transportation systems. Traffic management technology's real-time flexibility and shifting congestion patterns provide improved potential. Traditional approaches cannot estimate traffic flow or adapt to urban settings. A strong AI-driven method is needed to improve urban mobility and traffic flow. This paper introduces the LLM-RL Traffic Optimization Framework (LLM-RL-TOF). LLMs analyze real-time traffic data and give predictive insights in this context. Due to these new insights, the RL algorithm can improve traffic flow in real time and reduce congestion via dynamic traffic management. IoT sensors and urban traffic cameras capture real-time traffic data, including traffic volume and incidents. This data helps the LLM estimate bottlenecks, accidents, and traffic congestion. An RL agent uses LLM outputs to adjust traffic signal timing and suggest alternate routes. With real-time alternatives, traffic flow and urban mobility may be optimized. The junction throughput rate rose 17.5 %, the queue length accumulation index fell 22.3 %, and the average vehicle delay fell 18.6 %. The decrease in average vehicle delay enabled all these gains.