This paper targets traffic signal control in Japan and proposes an implementable signal control method by combining reinforcement learning with traditional traffic engineering, demonstrating the potential of integrating traffic engineering with RL to develop effective signal control methods.
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, compar...
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.
T. Haddad· Transportation Research Reco...· 0 citations
A hybrid reinforcement learning approach for traffic signal control that combines the complementary learning mechanisms of Q-learning, SARSA, and Monte Carlo algorithms to improve both learning efficiency and control performance is proposed.
Azzeddine Ben Moussa, A. Khazari· Mathematical and Computation...· 0 citations
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...
Ishini Charindi Dewamiththa, Kasun Chamika Priyadarshana, Sajan Hirusha Gunasekara et al.· Moratuwa Engineering Researc...· 0 citations
A systematic and up-to-date review of RL-based methods for large-scale TSC in traffic simulation environments, transportation modalities, and advances in methodologies is presented.
Xiao-Cai Zhang, Zhe Xiao, Tao Liu et al.· Artificial Intelligence Revi...· 0 citations
Traffic signal control plays a critical role in mitigating congestion and improving urban mobility, particularly in multi-intersection networks where fixed-time strategies cannot adapt to fluctuating demand. Although reinforcement learning has shown strong potential for adaptive signal optimization, purely learning-bas...
Mohammed El Kaim Billah, Mohammed-Alamine El Houssaini, Abedelfettah Mabrouk et al.· Future Transportation· 0 citations
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