Efficient Congestion Management Through IoT - Driven Road User Charging Systems with Reinforcement Learning

Abstract

This research aims to find out how to manage congestion more effectively in cities by combining reinforcement learning (RL) with Internet of Things (IoT)-driven road user charging systems. Unlike typical static pricing models, this approach aims to improve traffic flow and reduce congestion by dynamically adjusting prices depending on real-time traffic circumstances. The system gathers and analyzes data on road use trends using IoT infrastructure, enabling adaptive pricing schemes. RL algorithms are then used to adjust billing policies in real time based on user preferences, congestion levels, and traffic volume. The evaluation of proposed approach is based on simulation studies performed in a genuine city setting. Compared to more traditional methods, the results show that it significantly improves commuters' journey times and reduces congestion. The results of this study highlight the promise of RL approaches in combination with IoT road user charging systems to alleviate traffic in cities and improve transportation efficiency generally.