Semantic Trajectory Data Mining with LLM-Informed POI Classification

TL;DR

This paper presents a new trajectory mining pipeline that uses large language models to annotate POIs with activity types, significantly improving the accuracy of human travel pattern analysis.

Contribution

The paper introduces a novel LLM-based POI annotation method combined with Bayesian inference for activity detection, enhancing semantic trajectory mining accuracy.

Findings

Achieved 93.4% accuracy in POI classification

Attained 91.7% accuracy in activity inference

Demonstrated improved semantic trajectory analysis performance

Abstract

Human travel trajectory mining is crucial for transportation systems, enhancing route optimization, traffic management, and the study of human travel patterns. Previous rule-based approaches without the integration of semantic information show a limitation in both efficiency and accuracy. Semantic information, such as activity types inferred from Points of Interest (POI) data, can significantly enhance the quality of trajectory mining. However, integrating these insights is challenging, as many POIs have incomplete feature information, and current learning-based POI algorithms require the integrity of datasets to do the classification. In this paper, we introduce a novel pipeline for human travel trajectory mining. Our approach first leverages the strong inferential and comprehension capabilities of large language models (LLMs) to annotate POI with activity types and then uses a Bayesian-based algorithm to infer activity for each stay point in a trajectory. In our evaluation using the OpenStreetMap (OSM) POI dataset, our approach achieves a 93.4% accuracy and a 96.1% F-1 score in POI classification, and a 91.7% accuracy with a 92.3% F-1 score in activity inference.