The importance of mobile vehicle GPS trajectory data is widely recognized across many fields including transportation, urban planning, yet the use of real data is often hindered by privacy concerns, limited accessibility, and high acquisition costs. As a result, generating pseudo–GPS trajectory data has become an active area of research. Recent Diffusion-based approaches have achieved strong fidelity but remain limited in (1) they do not adequately quantify the influence of geographic information and social behaviors on vehicle mobility generation, (2) they typically sample synthesized trajectories from learned mobility distributions, resulting in limited generalization across regions and cities and reduced diversity of generated trajectories, (3) due to the multi-step noise addition and denoising process, diffusion models suffer from slow training and generation speeds. The project aims to address the above challenges by proposing a new paradigm for trajectory generation based on Large Language and Generative AI models. More specifically, the project will firstly quantify the influence of and select geographic information and social behaviors for trajectory and then integrate it to propose a Language(i.e. text prompt)-Trajectory Flow-Matching Pre-Training Generative Model. Language naturally offers strong diversity and creativity, making it a powerful modality for conditioning mobility generation. A flow-based generative model directly learns the continuous probability flow between a simple prior and the target mobility distribution, avoiding the multi-step noise addition and denoising required by diffusion models. Once pretrained, the model will support direct generation of trajectories from text prompts, enabling zero-shot capabilities analogous to those demonstrated by ChatGPT. To the best of our knowledge, it is the first project that aims to develop language-trajectory pre-training generative models for vehicle trajectory generation like ChatGPT. The proposed paradigm and pre-trained model are expected to be ground breaking for academic research and provide valuable guidance for transportation and urban planning sectors.