High-Quality Trajectory Generation via Domain-Knowledge Enhanced GANs

Simulating human mobility realistically and generating large-scale, high-quality trajectories are crucial for various location-based applications such as traffic management, epidemic spreading analysis, and location privacy protection. While the most popular model-free methods succeed by directly learning distribution of real-world data, they struggle to produce high-quality mobility data without leveraging the domain knowledge of human mobility. Moreover, such model-free methods primarily rely on auto-regressive paradigms, usually accompanied by error accumulation problem. To address the issues, we propose a model-free Domain-Knowledge Enhanced Generative Adversarial Network (DKE-GAN), which efficiently combines domain knowledge of urban context with model-free learning paradigm to generate high-quality mobility data. In addition, we incorporate reinforcement learning into the training process, thereby effectively alleviating the error accumulation. Furthermore, we introduce Trajectory Representation Learning (TRL) to convert noise-carrying raw trajectories into low-dimensional representation vectors for fully mining human mobility patterns. Extensive experiments conducted on two representative real-world mobility datasets demonstrate that our proposed method outperforms six state-of-the-art baselines, significantly achieving performance improvements in simulating human mobility.