BEYOND TRAJECTORY IMITATION: BEHAVIORALLY ALIGNED SYNTHETIC HUMAN MOBILITY GENERATION

Existing synthetic mobility generators primarily optimize day-level trajectory realism, but realistic trajectories can still misrepresent how an individual moves. We study this missing objective as behaviorally aligned synthetic mobility generation: generating trajectories that realize an explicit multi-scale behavioral specification. We introduce CTGTrace, which represents individuals through 19 persistent characteristics and 16 daily intentions and realizes them through structured spatial generation, multi-day planning, and closed-loop trajectory decoding. Across five heterogeneous mobility populations and ten published generators, CTGTrace achieves a mean normalized request-realization error of 0.175 with correlation r = 0.925. We further show that the framework supports partial specifications, scenario transfer, and generation without an underlying real individual, and release the resulting synthetic dataset and code.