Abstract: Traffic flow prediction is a crucial foundational aspect in the development of smart cities, holding significant implications for urban traffic management and user travel planning. The expanding dimensions of both time and space contribute to the increasing volume of traffic flow data, with real-time updates presenting a challenge in deploying cost-effective forecasting models to meet diverse traffic prediction demands. Inspired by the success of the emerging paradigm of graph-based finetuning in downstream tasks of natural language processing, we introduce, for the first time to our best knowledge, graph based finetuning to enhance the generalization capabilities of traditional traffic flow prediction models, called TPGPF (traffic flow prediction based on graph prompt-finetuning), which could enhance the generality and effectiveness of self-supervised learning. In the context of spatiotemporal multidimensional traffic flow prediction models, our approach involves pretraining the model based on historical datasets and introducing learnable prompt vectors. With the pretrained model solidified, a self-supervised learning model is guided by the introduced prompt vectors to adapt to new data prediction tasks, thereby enhancing the generality and effectiveness of traffic flow prediction models. Extensive experiments on five real-world public datasets validate the effectiveness of our work, which provides an effective method to overcome the cost challenge brought by quasi real-time training of traffic flow data.