Future Traffic

Predicting future traffic flow is crucial for optimizing transportation systems and improving urban mobility. Current research focuses on developing accurate and robust models, often employing deep learning architectures like convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and graph neural networks (GNNs), to analyze diverse data sources including sensor readings, GPS trajectories, and even social media data. These models aim to address challenges like data sparsity and the need for real-time adaptability, particularly in mixed human-autonomous vehicle environments. Advances in this field have significant implications for traffic management, autonomous driving, and urban planning.