Traffic Forecasting

Traffic forecasting estimates how movement and demand will change across road networks, transit systems, airspace, and communications networks, supporting planning, routing, and resource allocation. Common approaches combine graph neural networks, temporal convolutions, attention-based Transformers, and newer state-space models; other work uses signal decomposition, physical or agent-based simulation, and contextual data such as incidents, weather, or mobility patterns. Researchers are extending these models with probabilistic predictions, foundation models, and online, continual, or federated adaptation to handle uncertainty, privacy, and changing conditions. A central challenge is making forecasts accurate and efficient while ensuring they transfer across places, sensor configurations, and traffic regimes—and that improvements in forecast metrics actually help downstream decisions.