This study aims to enhance accessibility in transportation-disadvantaged areas by utilizing Large Language Model(LLM) to analyze public transportation and advanced mobility status data (e.g., platform taxis and Demand Responsive Transport(DRT)), and proposes a methodology to support region-specific mobility activation strategies. The study was divided into three stages: first, the collection of mobility data; second, the implementation of geographic information system (GIS)-based visualization and preprocessing; and third, the application of LLM-based image interpretation and classification. A variety of mobility data were consolidated into a unified spatial entity, converted into visualization information for LLM processing, and examined using a rule-based classification system to ascertain the mobility environment types. This approach addresses the limitations of single-data analysis and enables a multi-layered interpretation of regional transportation gaps. Through the LLM interpretation of visual elements, including grid colors, patterns, bus routes, and designated DRT operation areas, transportation characteristics such as mobility supply levels, DRT operation status, and taxi dependency were identified. The LLM model demonstrated a high level of performance with a precision rate of 78.2 %, accuracy rate of 73.1 %, recall rate of 91.8 %, and F1-score of 84.5 %. Notably, the recall rate exceeded 90 %, signifying comprehensive recognition of various transportation environment types. This study proposes an LLM-based spatial data interpretation framework for analyzing regional mobility conditions in Paju City. The integration of complex spatial information into QGIS enables the LLM to automatically analyze data, thereby unveiling micro-level mobility characteristics and identifying four types of regional mobility improvements.

ABSTRACT
1. 서론
2. 문헌 고찰 및 본 연구의 의의
    2.1 문헌 고찰
    2.2. 본 연구의 의의
3. 분석 방법
    3.1. 데이터 수집
    3.2. 데이터 시각화 및 전처리
    3.3. 시각화 데이터 해석 및 분류
4. 분석 결과
    4.1. LLM 시각화 데이터 해석 결과
    4.2. LLM 시각화 데이터 해석 성능
    4.3. LLM 시각화 데이터 해석 결과 분류
5. 결론
감사의 글
REFERENCES

• 조용빈(한국교통안전공단 선임연구원) | Cho Yongbin
• 신소명(한국교통안전공단 선임연구원) | Shin So-Myoung Corresponding author