Public transportation is beating heart of a city. Understanding how citizens utilize public transportation can be used to optimize many applications such as traffic planning, crowd flow prediction, and location-based marketing. However, obtaining how citizens used transportation is not a trivial task. It is almost not possible to ask citizens to report their exact location and their transportation mode; moreover, there are usually various public transportation that move along the similar paths. These increase challenges to identify people's transport modes. To address these issues, this paper proposes Public Transport General Framework (PTGF) to identify people's transport modes by their cellular data in both offline and online manners. Regarding the offline phase, given historical cellular data of people and urban transportation networks, PTGF derives cellular data into trajectories, to match each trajectory to public transportation networks to find the most possible transport modes for sub-Trajectories of a trajectory. In the online phase, given streaming trajectories, PTGF identifies the transport modes of each location by an LSTM which are trained by historical trajectories with transport modes annotated in the offline phase. Extensive experiments are conducted by using both synthetic and real datasets. The experimental results show that the accuracy of PTGF in offline phase around 80% and that in online phase F1-score around 0.7, which could prove that the effectiveness of the proposed framework PTGF.