Understanding human mobility patterns is essential for various applications,
from urban planning to public safety. The individual trajectory such as mobile
phone location data, while rich in spatio-temporal information, often lacks
semantic detail, limiting its utility for in-depth mobility analysis. Existing
methods can infer basic routine activity sequences from this data, lacking
depth in understanding complex human behaviors and users' characteristics.
Additionally, they struggle with the dependency on hard-to-obtain auxiliary
datasets like travel surveys. To address these limitations, this paper defines
trajectory semantic inference through three key dimensions: user occupation
category, activity sequence, and trajectory description, and proposes the
Trajectory Semantic Inference with Large Language Models (TSI-LLM) framework to
leverage LLMs infer trajectory semantics comprehensively and deeply. We adopt
spatio-temporal attributes enhanced data formatting (STFormat) and design a
context-inclusive prompt, enabling LLMs to more effectively interpret and infer
the semantics of trajectory data. Experimental validation on real-world
trajectory datasets demonstrates the efficacy of TSI-LLM in deciphering complex
human mobility patterns. This study explores the potential of LLMs in enhancing
the semantic analysis of trajectory data, paving the way for more sophisticated
and accessible human mobility research.

通过大型语言模型（LLMs）推理轨迹语义的 Trajectory Semantic Inference with Large Language Models (TSI-LLM) 框架通过人类移动性模式的复杂分析，提高了轨迹数据的语义分析能力。

揭示人类迁移行为：利用大型语言模型推断轨迹的语义

Deciphering Human Mobility: Inferring Semantics of Trajectories with  Large Language Models

Understanding the neural implementation of complex human behaviors is one of
the major goals in neuroscience. To this end, it is crucial to find a true
representation of the neural data, which is challenging due to the high
complexity of behaviors and the low signal-to-ratio (SNR) of the signals. Here,
we propose a novel unsupervised learning framework, Neural Latent Aligner
(NLA), to find well-constrained, behaviorally relevant neural representations
of complex behaviors. The key idea is to align representations across repeated
trials to learn cross-trial consistent information. Furthermore, we propose a
novel, fully differentiable time warping model (TWM) to resolve the temporal
misalignment of trials. When applied to intracranial electrocorticography
(ECoG) of natural speaking, our model learns better representations for
decoding behaviors than the baseline models, especially in lower dimensional
space. The TWM is empirically validated by measuring behavioral coherence
between aligned trials. The proposed framework learns more cross-trial
consistent representations than the baselines, and when visualized, the
manifold reveals shared neural trajectories across trials.

神经学中理解复杂人类行为的神经实现是一个重要目标，本研究提出一种新的无监督学习框架，神经潜变压器（NLA），用于找到良好约束的、与行为相关的神经表示，通过对重复试验进行表示对齐来学习跨试验一致的信息，通过时间弯曲模型（TWM）解决试验之间的时间错位，应用于自然语言说话的脑内电皮层图图（ECoG）的实验证明了该模型在低维空间下学习更好的解码行为的表示，TWM 可通过测量对齐试验之间的行为一致性进行经验验证，所提出的框架学习到了比基线模型更加一致的、可视化后揭示了试验之间共享的神经轨迹的表示流形。