Generative models have shown promising results in capturing human mobility characteristics and generating synthetic trajectories. However, it remains challenging to ensure that the generated geospatial mobility data is semantically realistic, including consistent location sequences, and reflects real-world characteristics, such as constraining on geospatial limits. To address these issues, we reformat human mobility modeling as an autoregressive generation task, leveraging Generative Pre-trained Transformer (GPT). To ensure its controllable generation to alleviate the above challenges, we propose a geospatially-aware generative model, MobilityGPT. We propose a gravity-based sampling method to train a transformer for semantic sequence similarity. Then, we constrained the training process via a road connectivity matrix that provides the connectivity of sequences in trajectory generation, thereby keeping generated trajectories in geospatial limits. Lastly, we constructed a Reinforcement Learning from Trajectory Feedback (RLTF) to minimize the travel distance between training and the synthetically generated trajectories. Our experiments on real-world datasets demonstrate that MobilityGPT outperforms state-of-the-art methods in generating high-quality mobility trajectories that are closest to real data in terms of origin-destination similarity, trip length, travel radius, link, and gravity distributions.

翻译：生成模型在捕捉人类移动特征和生成合成轨迹方面已展现出潜力。然而，如何确保生成的地理空间移动数据在语义上真实可靠——包括一致的序列位置，并反映现实世界特性（如地理空间约束）——仍是挑战。为解决这些问题，我们将人类移动性建模重构为自回归生成任务，利用生成式预训练Transformer（GPT）。为通过可控生成缓解上述挑战，我们提出了一种地理空间感知生成模型MobilityGPT。我们提出基于引力采样的方法训练Transformer，以实现语义序列相似性。随后，通过道路连通性矩阵约束训练过程，该矩阵提供轨迹生成中的序列连通性，从而确保生成的轨迹不超出地理空间边界。最后，我们构建了轨迹反馈强化学习（RLTF）机制，以最小化训练数据与合成生成轨迹之间的行程距离。在真实数据集上的实验表明，MobilityGPT在生成高质量移动轨迹方面优于现有最优方法，其生成的轨迹在起讫点相似性、行程长度、出行半径、链接分布及引力分布上最接近真实数据。