LLM-based Analysis of Potential Demand for Regional Railways

Abstract

Local railways are facing a decline in users, and additionally, they are challenged by the difficulty of accurately grasping actual usage conditions and latent demand—information that is essential for formulating policies aligned with local realities. This study proposes a method to address this issue by leveraging social media (SNS) data and large language models (LLMs) to conduct large-scale analyses of users’ raw voices related to local railways. Specifically, we develop a technique to automatically extract usage purposes from SNS posts and apply it to multiple railway lines with differing demand characteristics for comparative analysis. Through this analysis, we aim to clarify differences and commonalities in demand patterns across lines, and particularly to visualize latent demand and usage characteristics in low-demand local railways. This method is expected to contribute to the formulation of effective policies that are grounded in the actual conditions of local railway systems.