Map2Traj: Street Map Piloted Zero-shot Trajectory Generation with Diffusion Model

TL;DR

Map2Traj is a novel diffusion model-based method that generates realistic user trajectories in unobserved areas using only street maps, addressing privacy issues and enhancing mobility modeling for wireless network optimization.

Contribution

We introduce Map2Traj, a zero-shot trajectory generation approach leveraging street maps and diffusion models, enabling realistic mobility pattern synthesis without access to real trajectory data.

Findings

Map2Traj produces trajectories closely matching real-world patterns.

The method achieves high similarity in trajectory distribution.

Effective for wireless network optimization tasks.

Abstract

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.