Dynamic traffic signal scheduling system based on adaptive quad agent Double Deep Q -network algorithm

Deep reinforcement learning-based traffic signal control in multi-intersection environments: a comparative study of DQN variants

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.

Deep reinforcement learning-based adaptive traffic signal control in urban networks

This paper introduces a traffic signal control system using deep reinforcement learning to solve the congestion problems at signalized intersections under dynamic traffic conditions. The proposed framework is simulated with the help of MATLAB-SUMO co-simulation framework, the traffic signal control is modeled as a Markov Decision Process (MDP). State space includes traffic density, the queue length, vehicles waiting time, and the actual signal phase whereas the action space comprises of the possible selections of the signal phase. A Deep Q-Network (DQN) is utilized to estimate the optimal state-action value function so that green times can be dynamically allocated based on the changing traffic demand. Multi-objective reward functionality is based on the combined minimization of vehicle delay, queue length, and waiting time and maximization of traffic throughput. Experience replay and target network updates are used to stabilize the learning process. Simulation experiments are conducted in low, medium, and high traffic demand conditions to compare the work of the suggested framework with fixed-time control, actuated control, and classical tabular Q-learning methods. Experimental results demonstrate that the proposed framework reduces average delay by up to 33.9%, decreases queue length by 47.1%, and increases throughput by 25.5% compared to fixed-time control under high-demand conditions. Finally, scalability studies involving networks of up to 16 isolated signalized intersections were conducted to assess computational feasibility and robustness even when the size of the network grows. Comprehensively, the results prove the usefulness of deep reinforcement learning in the creation of intelligent and adaptive traffic signal control services in the city.

Soft Actor-Critic based regional traffic signal control in connected environment and its application in priority signal control

Traffic Signal Dynamic Optimization Algorithm Based on Deep Reinforcement Learning

With the acceleration of urbanization, traffic congestion at multiple intersections has become a core bottleneck restricting urban operational efficiency. To address this, this paper proposes a traffic signal dynamic optimization algorithm, the Cross-Attention Mechanism and Dueling Double DQN (CAM-D3QN). This method utilizes a novel crisscross attention module to dynamically model spatial dependencies between intersections and incorporates the Dueling Double DQN architecture for robust Q-value estimation. Validated on CityFlow using Grid-4×4 and Hangzhou-real networks, CAM-D3QN significantly outperforms the state-of-the-art baseline, GPLight, achieving relative improvements of approximately 10.5% in average vehicle delay, 11.3% in average queue length, 3.3% in throughput, alongside notable reductions in stops (12.1%) and fuel consumption (7.2%). Ablation experiments further demonstrate that removing the cross-attention module increases queue length by 30.6% in sudden congestion scenarios. The proposed method achieves superior performance to the baseline across four typical traffic scenarios on the Hangzhou-real road network, demonstrating its generalization capabilities. By leveraging the coordinated optimization of dynamic spatial perception and robust value assessment, this paper provides an effective solution for efficient and robust coordinated traffic signal control. The framework can be combined with traffic states acquired from radar, roadside sensors or wireless communication units in intelligent transportation systems.

A Decentralized Traffic Signal Control Framework Using Multiagent Reinforcement Learning With Real-World Operational Constraints

Multi-Intersection Traffic Signal Control Based on Multi-agent Reinforcement Learning: A Cooperative Approach

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.