PURPOSES : To prevent an increasing number of drowsiness-related accidents, considering driver fatigue is necessary, which is the main cause of drowsiness accidents. The purpose of this study is to propose a methodology for selecting drowsiness hotspots using continuous driving time, a variable that quantifies driver fatigue. METHODS : An analysis was conducted by dividing driver fatigue, which changes according to time and space, into temporal and spatiotemporal scenarios. The analysis technique derived four evaluation indicators (precision, recall, accuracy, and F1 score) using a random forest classification model that is effective for processing large amounts of data. RESULTS : Both the temporal and spatiotemporal scenarios performed better in models that reflected the characteristics of road sections with changes in time and space. Comparing the two scenarios, it was found that the spatiotemporal scenario showed a difference in precision of approximately 10% compared with the temporal scenarios. In addition, [Model 2-2] of the spatiotemporal scenario showed the best predictive power by assessing the model’s accuracy via a comparison of (1-recall) and precision. This shows better performance in predicting drowsy accidents by considering changes in time and space together rather than constructing only temporal changes. CONCLUSIONS : To classify hotspots of drowsiness, spatiotemporal factors must be considered. However, it is possible to develop a methodology with better performance if data on individuals driving vehicles can be collected.

1. 서론
2. 기존 문헌고찰
    2.1. 관련 이론 고찰
    2.2. 관련 연구 고찰
3. 데이터 구축
    3.1. 개요
    3.2. 종속변수 설정
    3.3. 독립변수 설정
    3.4. 데이터 구축 결과
4. 고속도로 내 졸음사고 위험 구간 선정방법론모형 개발
    4.1. 시나리오 구성
    4.2. 모형 개발 절차 및 방법
5. 분석결과
    5.1. 시간적 시나리오 분석 검증 결과
    5.2. 시공간적 분석 검증 결과
    5.3. 시나리오별 졸음사고 위험 구간 선정방법론 비교
6. 결론

• 강지혜(정회원 · 서울시립대학교 교통공학과 & 도시빅데이터 융합전공 석사) | Kang jihye
• 김도경(정회원 ·서울시립대학교 교통공학과 & 도시빅데이터 융합학과 교수) | Kim Do-Gyeong 교신저자