RHYTHM: Reasoning with Hierarchical Temporal Tokenization for Human
  Mobility
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.
68db5413d2bf1f4b15ec74ef
Haoyu He
68db5413d2bf1f4b15ec74f0
Haozheng Luo
claimed_verified
2025-09-30T06:00:46.112Z
63ef0af2bfe4ead22ca8f69a
https://cdn-avatars.huggingface.co/v1/production/uploads/1676610243576-noauth.jpeg
Haozheng Luo
user
robinzixuan
68db5413d2bf1f4b15ec74f1
Yan Chen
68db5413d2bf1f4b15ec74f2
Qi R. Wang
RHYTHM: Reasoning with Hierarchical Temporal Tokenization for Human
  Mobility
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.
RHYTHM uses hierarchical temporal tokenization and large language models to predict human mobility, capturing long-range dependencies and multi-scale periodic behaviors efficiently.
temporal tokenization hierarchical attention pre-computed prompt embeddings frozen LLM spatio-temporal predictors trajectory reasoners