Traffic Prediction

Traffic prediction aims to forecast future traffic conditions, crucial for optimizing transportation systems and resource allocation. Current research heavily utilizes deep learning models, including graph neural networks, transformers, and recurrent neural networks like LSTMs, often incorporating techniques like transfer learning, data augmentation, and multi-modal data fusion to improve accuracy and address data limitations. These advancements are impacting various sectors, from improving urban planning and traffic management to enhancing the efficiency of telecommunication networks and autonomous driving systems. Furthermore, there's a growing emphasis on model explainability and robustness to handle outliers and uncertainties in real-world traffic data.