  User mobility modeling serves a crucial role in analysis and optimization of
contemporary wireless networks. Typical stochastic mobility models, e.g.,
random waypoint model and Gauss Markov model, can hardly capture the
distribution characteristics of users within real-world areas. State-of-the-art
trace-based mobility models and existing learning-based trajectory generation
methods, however, are frequently constrained by the inaccessibility of
substantial real trajectories due to privacy concerns. In this paper, we
harness the intrinsic correlation between street maps and trajectories and
develop a novel zero-shot trajectory generation method, named Map2Traj, by
exploiting the diffusion model. We incorporate street maps as a condition to
consistently pilot the denoising process and train our model on diverse sets of
real trajectories from various regions in Xi'an, China, and their corresponding
street maps. With solely the street map of an unobserved area, Map2Traj
generates synthetic trajectories that not only closely resemble the real-world
mobility pattern but also offer comparable efficacy. Extensive experiments
validate the efficacy of our proposed method on zero-shot trajectory generation
tasks in terms of both trajectory and distribution similarities. In addition, a
case study of employing Map2Traj in wireless network optimization is presented
to validate its efficacy for downstream applications.
