GeoFormer: Predicting Human Mobility using Generative Pre-trained
  Transformer (GPT)
Aivin V. Solatorio
Predicting human mobility holds significant practical value, with
applications ranging from enhancing disaster risk planning to simulating
epidemic spread. In this paper, we present the GeoFormer, a decoder-only
transformer model adapted from the GPT architecture to forecast human mobility.
Our proposed model is rigorously tested in the context of the HuMob Challenge
2023 -- a competition designed to evaluate the performance of prediction models
on standardized datasets to predict human mobility. The challenge leverages two
datasets encompassing urban-scale data of 25,000 and 100,000 individuals over a
longitudinal period of 75 days. GeoFormer stands out as a top performer in the
competition, securing a place in the top-3 ranking. Its success is underscored
by performing well on both performance metrics chosen for the competition --
the GEO-BLEU and the Dynamic Time Warping (DTW) measures. The performance of
the GeoFormer on the HuMob Challenge 2023 underscores its potential to make
substantial contributions to the field of human mobility prediction, with
far-reaching implications for disaster preparedness, epidemic control, and
beyond.