Urban Traffic

Urban traffic research aims to optimize traffic flow and mitigate congestion, focusing on improving prediction accuracy, enhancing traffic control strategies, and understanding driver behavior. Current research employs diverse machine learning models, including deep neural networks (like LSTMs and GRUs), reinforcement learning algorithms, and Bayesian inference methods, often integrated with traffic simulation platforms and real-world datasets. These advancements are crucial for developing more efficient transportation systems, improving safety, and reducing environmental impact through better traffic management and planning. The integration of large language models and vision-language models is also emerging as a promising area for enhancing real-time decision-making in traffic control.