Statistics on urban traffic speed flows are essential for thoughtful city planning. Recently, data-driven traffic prediction methods have become the state-of-the-art for a wide range of traffic forecasting tasks. However, many small cities have a limited amount of traffic data available for building data-driven models due to lack of data collection methods. With the acceleration of urbanization, the need for traffic construction of small and medium-sized cities is imminent. To tackle the above problems, we propose a TransfEr lEarning approach with graPh nEural nEtworks (TEEPEE) for traffic prediction that can forecast the traffic speed in data-scarce areas with massive value data from developed cities. In particular, TEEPEE uses graph clustering to divide the traffic network map into multiple sub-graphs. Graph clustering captures more spatial information in the transfer process. To evaluate the effectiveness of TEEPEE, we conduct experiments on two real-world datasets and compare them with other baseline models. The results demonstrate that TEEPEE is among the best efforts of baseline models. We provide a comprehensive analysis of the experimental results in this work.