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Deep Reinforcement Learning for Vehicle Swarm Navigation and Urban Traffic Optimization

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The proposed model reduces average travel time by 7.54% and halting vehicles by 14.35% in high-congestion scenarios, indicating effectiveness against traffic congestion.
Simulations on both simplified and real-life road networks underline the model's performance compared to traditional methods and dynamic user equilibrium.
A parallel simulation training strategy accelerates the convergence of deep reinforcement learning in dynamic traffic environments.
The method maintains stability across various traffic conditions, indicating robustness in addressing complex urban mobility challenges.

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Deep reinforcement learning reduces travel time by 7.54% in urban traffic, suggesting effective vehicle routing optimization.

Zhang et al. (2025) studied this question.

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