  In this paper, we lay out a vision for analysing semantic trajectory traces
and generating synthetic semantic trajectory data (SSTs) using generative
language model. Leveraging the advancements in deep learning, as evident by
progress in the field of natural language processing (NLP), computer vision,
etc. we intend to create intelligent models that can study the semantic
trajectories in various contexts, predicting future trends, increasing machine
understanding of the movement of animals, humans, goods, etc. enhancing
human-computer interactions, and contributing to an array of applications
ranging from urban-planning to personalized recommendation engines and business
strategy.
