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. We reformat human mobility modeling as an autoregressive generation task to address these issues, leveraging the Generative Pre-trained Transformer (GPT) architecture. 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 proposed to construct a preference dataset for fine-tuning MobilityGPT via Reinforcement Learning from Trajectory Feedback (RLTF) mechanism, which minimizes the travel distance between training and the synthetically generated trajectories. Experiments on real-world datasets demonstrate MobilityGPT's superior performance over 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有前景的结果。然而，确保生成的地理空间移动数据在语义上具有真实性（包括一致的位置序列）并反映现实世界特征（如受地理空间限制约束）仍然具有挑战性。为解决这些问题，我们将人类移动性建模重新构建为自回归生成任务，并利用生成式预训练Transformer（GPT）架构。为确保其可控生成以缓解上述挑战，我们提出了一个地理空间感知的生成模型——MobilityGPT。我们提出了一种基于引力的采样方法来训练Transformer以获取语义序列相似性。随后，我们通过道路连通性矩阵约束训练过程，该矩阵为轨迹生成提供序列连通性，从而将生成的轨迹保持在地理空间限制内。最后，我们提出通过轨迹反馈强化学习（RLTF）机制构建偏好数据集对MobilityGPT进行微调，以最小化训练轨迹与合成生成轨迹之间的出行距离。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先进方法，其在起点-终点相似性、行程长度、出行半径、链路分布和引力分布方面最接近真实数据。