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аналитика - Traffic Engineering
EnduRL: Enhancing Safety, Stability, and Efficiency of Mixed Traffic Under Real-World Perturbations Via Reinforcement Learning
混合交通の安全性、安定性、効率を向上させるためにリアルワールドの乱れを強化するEnduRLプロジェクト。
Multi-agent Reinforcement Traffic Signal Control with Interpretable Influence Mechanism and Biased ReLU Approximation
Proposing a novel multi-agent actor-critic framework with interpretable influence mechanism and biased ReLU approximation for efficient traffic signal control.
Car-Following Models: A Comprehensive Review
Car-following models encompass various disciplines and provide insights into traffic flow dynamics, aiding in the development of connected and automated transportation systems.
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