Semantic-Fused Multi-Granularity Cross-City Traffic Prediction

arXiv:2302.11774v2 Announce Type: replace
Abstract: Accurate traffic prediction is essential for effective urban management and the improvement of transportation efficiency. Recently, data-driven traffic prediction methods have been widely adopted, with better performance than traditional approaches. However, they often require large amounts of data for effective training, which becomes challenging given the prevalence of data scarcity in regions with inadequate sensing infrastructures. To address this issue, we propose a Semantic-Fused Multi-Granularity Transfer Learning (SFMGTL) model to achieve knowledge transfer across cities …

arxiv city cs.lg prediction semantic traffic type