Accurate traffic forecasting plays an indispensable role in modern Intelligent Transportation Systems (ITS). Recent years have witnessed significant advancements in the modeling capabilities and inference efficiency of continuous-time generative models, which learn a dynamic system to transform a simple prior distribution into the target data distribution. Motivated by the emerging generative paradigm, we rethink the traffic forecasting task from the perspective of flow matching. Specifically, we propose to learn a velocity field that transports samples from a prior distribution to the distribution of future traffic values. We introduce two flow matching models based on different assumptions on prior distribution: the commonly used standard Gaussian distribution and the distribution of the historical traffic values. Furthermore, our approach incorporates the latest mean flow techniques to enable one-step generation, ensuring simplicity and high efficiency in both training and inference phases. In experiment on real world data, our proposed models demonstrate desirable performance.