Congestion Prediction

Congestion prediction aims to accurately forecast traffic or network congestion, enabling proactive mitigation strategies in various domains, from urban transportation to VLSI circuit design. Current research emphasizes developing robust and generalizable models, employing architectures like graph neural networks (GNNs), mixture-of-experts models, and incorporating advanced loss functions to handle imbalanced datasets and improve accuracy, particularly in predicting extreme congestion events. These advancements are crucial for optimizing resource allocation, improving transportation efficiency, and accelerating the design process in fields like integrated circuit manufacturing.