TL;DR
This study proposes a distributed traffic signal control framework built upon a Machine Learning (ML) paradigm utilizing Reinforcement Learning (RL), and demonstrates the effectiveness of the proposed approach, with vehicle queue lengths and average waiting times reduced by 35% on roads leading to the junctions, compared to conventional rule-based systems.
Traffic congestion in urban areas has become a significant challenge, particularly in developing countries such as Sri Lanka, where conventional fixed-time traffic signal systems are unable to adapt to dynamic traffic conditions. This research aimed to develop an adaptive traffic signal control system using reinforceme...
A distributed TSC model based on the Soft Actor-Critic (SAC) reinforcement learning algorithm that demonstrates the model’s effectiveness, adaptability, and potential for deployment in intelligent traffic management systems is proposed.
The findings demonstrate the potential of DRL-based traffic signal control in controlled simulation conditions and highlight that algorithm performance is strongly influenced by traffic policy design and environmental complexity.
A novel cooperative MARL-based approach for adaptive traffic signal control in multi-intersection networks that significantly outperforms existing methods in relation to average pheromone intensity, average noise emission, and average waiting time is proposed.
Results indicate that explicit rule-based projection can improve the operational feasibility and extensibility of RL-based traffic signal control, although further validation with additional seeds and longer real-network simulations is required.
This study introduces an AI-based speed control mechanism leveraging deep reinforcement learning to enhance freeway traffic conditions under varying levels of autonomous vehicle integration to provide a foundation for future adaptive traffic management strategies in evolving transportation ecosystems.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
