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

In modern society, people should not be identified based on their disability,
rather, it is environments that can disable people with impairments.
Improvements to automatic Sign Language Recognition (SLR) will lead to more
enabling environments via digital technology. Many state-of-the-art approaches
to SLR focus on the classification of static hand gestures, but communication
is a temporal activity, which is reflected by many of the dynamic gestures
present. Given this, temporal information during the delivery of a gesture is
not often considered within SLR. The experiments in this work consider the
problem of SL gesture recognition regarding how dynamic gestures change during
their delivery, and this study aims to explore how single types of features as
well as mixed features affect the classification ability of a machine learning
model. 18 common gestures recorded via a Leap Motion Controller sensor provide
a complex classification problem. Two sets of features are extracted from a 0.6
second time window, statistical descriptors and spatio-temporal attributes.
Features from each set are compared by their ANOVA F-Scores and p-values,
arranged into bins grown by 10 features per step to a limit of the 250
highest-ranked features. Results show that the best statistical model selected
240 features and scored 85.96% accuracy, the best spatio-temporal model
selected 230 features and scored 80.98%, and the best mixed-feature model
selected 240 features from each set leading to a classification accuracy of
86.75%. When all three sets of results are compared (146 individual machine
learning models), the overall distribution shows that the minimum results are
increased when inputs are any number of mixed features compared to any number
of either of the two single sets of features.

本研究旨在探讨单一特征和混合特征对机器学习模型分类能力的影响，通过提取统计描述符和时空属性两类特征来识别含 18 种动态手势的手语，结果表明混合特征的输入比两类单一特征的输入产生更好的分类效果，对于自动手语识别技术的提高对其实现数字化技术的残疾人群体更有益。