Abstract: Traffic flow prediction,as a core technology for dynamic decision-making in smart cities,plays a crucial role in traffic signal control,route planning,and emergency management.With the expansion of urban road networks and the rapid growth of traffic data,traditional methods face challenges in accurately modeling the complex spatio-temporal interactions among road network nodes.Although pre-trained models can transfer knowledge across domains,they still encounter limitations when applied to traffic flow prediction,primarily due to coupled spatio-temporal features and the mismatch between pre-trained representations and traffic-specific characteristics.To address these issues,this paper proposes the pre-trained spatio-temporal decoupling-based traffic flow prediction model(PT-STD).The method employs a spatio-temporal decoupling module to disentangle the deep feature learning of spatial topological relationships and multi-granularity temporal patterns.Furthermore,it designs a hierarchical adaptive fine-tuning strategy that progressively unfreezes the normalization layers and attention parameters of the pre-trained model,gradually transferring the general knowledge learned in the pre-trained model to spatio-temporal feature modeling.Experimental results demonstrate that PT-STD achieves significant improvements on standard benchmark datasets,with a 3.89% reduction in mean absolute error(MAE) under data-scarce scenarios.

Key words: Traffic flow prediction, Spatio-temporal decomposition, Hierarchical fine-tuning, Pretrained model, Urban computing