RHYTHM: Reasoning with Hierarchical Temporal Tokenization for Human
  Mobility
Authors: name: Haoyu He, name: Haozheng Luo, name: Yan Chen, name: Qi R. Wang
Organization: Northwestern University

Predicting human mobility is inherently challenging due to complex long-range
dependencies and multi-scale periodic behaviors. To address this, we introduce
RHYTHM (Reasoning with Hierarchical Temporal Tokenization for Human Mobility),
a unified framework that leverages large language models (LLMs) as
general-purpose spatio-temporal predictors and trajectory reasoners.
Methodologically, RHYTHM employs temporal tokenization to partition each
trajectory into daily segments and encode them as discrete tokens with
hierarchical attention that captures both daily and weekly dependencies,
thereby significantly reducing the sequence length while preserving cyclical
information. Additionally, we enrich token representations by adding
pre-computed prompt embeddings for trajectory segments and prediction targets
via a frozen LLM, and feeding these combined embeddings back into the LLM
backbone to capture complex interdependencies. Computationally, RHYTHM freezes
the pretrained LLM's backbone to reduce attention complexity and memory cost.
We evaluate our model against state-of-the-art methods using three real-world
datasets. Notably, RHYTHM achieves a 2.4% improvement in overall accuracy, a
5.0% increase on weekends, and a 24.6% reduction in training time. Code is
publicly available at https://github.com/he-h/rhythm.