Cross City Few Shot

Cross-city few-shot traffic forecasting addresses the challenge of accurately predicting traffic flow in cities with limited sensor data by leveraging information from data-rich cities. Current research focuses on developing models that learn transferable traffic patterns across different urban environments, often employing pre-training on large datasets followed by fine-tuning on limited target city data. These models frequently utilize techniques like clustering to create "traffic pattern banks" or leverage frequency domain similarities to improve knowledge transfer. This research area is significant for improving the efficiency and effectiveness of intelligent transportation systems, particularly in developing cities with limited infrastructure.