The field of traffic management is witnessing significant advancements with the integration of machine learning and artificial intelligence. Researchers are exploring novel approaches to optimize traffic signal control, estimate queue lengths, and predict vehicle delays. A key trend is the use of transfer learning and domain adaptation to improve the accuracy and robustness of models in real-world applications. This enables the development of more efficient and scalable traffic management systems. Noteworthy papers include:

• A study that proposes a reinforcement learning-based framework for traffic signal control, achieving a 29% reduction in average queue lengths.
• A paper that introduces Q-Net, a data-efficient and interpretable framework for queue length estimation, which outperforms baseline methods by over 60% in Root Mean Square Error.
• A research work that demonstrates the effectiveness of transfer learning in bridging the gap between simulated and real network data, reducing the Mean Absolute Percentage Error in packet delay prediction by up to 88%.
• A study that presents a domain adaptation framework for estimating vehicle delays at heterogeneous signalized intersections, providing more accurate and robust delay estimates.