MobilityGPT: Enhanced Human Mobility Modeling with a GPT model
Ammar Haydari Dongjie Chen Zhengfeng Lai Chen-Nee Chuah
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.