국내 교통사고 뉴스 분석을 통한 교통안전 정책 키워드 토픽 모델링 연구

초록

Although fatalities and injuries from traffic accidents have consistently declined each year, Korea's traffic safety standards still lag behind those of major developed countries. To address this gap, this study proposes a policy model using text mining and Latent Dirichlet Allocation(LDA) topic modeling to analyze traffic accident-related news articles collected from major Korean news media. The analysis identified six key policy issues: strengthening penalties for drunk driving, ensuring child safety in school zones, responding to personal mobility device accidents, preventing motorcycle accidents, managing elderly drivers, and improving traffic infrastructure. The findings of this study are expected to serve as valuable foundational data for national and local governments in developing effective future policies for preventing and responding to traffic accidents.

키워드