| Title |
Development of a Mobile LiDAR-Based Pavement Marking Retroreflectivity Visualizing Platform with MCP-Driven Conversational Analysis |
| Authors |
송민혁(Song, Minhyuk);조윤범(Jho, Youn-beom);김종우(Kim, Jongwoo) |
| DOI |
https://doi.org/10.12652/Ksce.2026.46.1.0087 |
| Keywords |
재귀반사도; 시각화 플랫폼; MCP; 대화형 분석 Retroreflectivity; Visualization platform; Model Context Protocol (MCP); Conversational analysis |
| Abstract |
Conventional pavement marking maintenance has relied on localized measurements of retroreflectivity and fragmented data management structures organized by individual devices or operational units. As a result, comprehensive analysis of long-term and segment-level measurement data has been limited, leading to persistent challenges in achieving efficient and consistent maintenance decision-making. To address these limitations, this study proposes a pavement marking maintenance framework that integrates a visualization platform and conversational analysis enabled by the Model Context Protocol (MCP), based on the analysis of mobile LiDAR-derived retroreflectivity data to support proactive and preventive maintenance decisions at the roadway segment level. First, domestic and international guidelines and institutional trends related to the observation and management of pavement marking retroreflectivity were reviewed to identify limitations in existing maintenance practices. Subsequently, a data-driven platform was designed to compensate for the blind spots of conventional maintenance systems by enabling the systematic storage, analysis, and
visualization of pavement marking retroreflectivity data. The proposed visualization platform integrates data storage, analytical processing, and visualization functions within a unified environment. In addition, through MCP, Large Language Models (LLMs) are designed to utilize pavement marking retroreflectivity databases, maintenance manuals, and relevant regulations as contextual information, thereby providing maintenance-related insights through conversational analysis. The proposed approach is expected to substantially enhance the practical utilization of pavement marking retroreflectivity data and to improve the operational efficiency and proactive response capability of road maintenance by advancing automation and decision-support functions. |