Jul 2026· International Journal of Machine Learning and Cybernetics· Vol 17· 0 citations· 55 references
Computer Science
TL;DR
An action value weighted QMIX (AVW-QMIX) based cooperative traffic signal control method, which models a multi-intersection traffic network in a region as a multi-agent reinforcement learning system, and uses each local signal agent to sense the traffic flow information of its incoming lane.
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.
Yunxue Lu, Chang-Ze Li, Hao Yu et al.· Journal of Intelligent Trans...· 5 citations
Traffic jam is an unresolved issue in the burgeoning urban centres culminating in the excess of delays, increased fuel usage and high levels of green house gasses. The traditional traffic signal systems are run on a set timing schedule that fails to adapt to the fluctuations that occur in the traffic, thus wasting the...
Billasonti Mohan Nageswara Rao, Manukuri Likith Kumar, S. Nagadevi et al.· 2026 2nd International Confe...· 0 citations
WMFLight (Weighted Mean Field multi-agent reinforcement learning-based traffic Light control method), a method that combines dynamic clustering and multi-agent mean field reinforcement learning to adapt to the dynamic changes of traffic flow is proposed.
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...
This paper presents a comparative study of two distributed traffic signal control approaches: a cell-transmission-model-based model predictive control (MPC) method and a deep reinforcement learning (DRL) method based on latent spatio-temporal network (LST-Net). In the MPC method, each intersection predicts local traffi...
Zhen-Zhen Hao, Guo-Rong Ma, Yong-Bin Cai et al.· Applied Sciences· 0 citations
Urban traffic signal control must reduce congestion, prioritize emergency vehicles, and limit environmental impact under dynamic traffic conditions. Fixed-time control cannot adapt effectively to such variations. Existing learning-based methods have shown promising results, but most of them do not jointly address emerg...
Rajasekhar Dasari, D. C. A. Reddy, S. Nayak· IEEE International Symposium...· 0 citations
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