PURPOSES : In this study, a model was developed to estimate the concentrations of particulate matter (PM2.5 and PM10) in expressway tunnel sections. METHODS : A statistical model was constructed by collecting data on particulate matter (PM2.5 and PM10), weather, environment, and traffic volume in the tunnel section. The model was developed after accurately analyzing the factors influencing the PM concentration. RESULTS : A machine learning-based PM concentration estimation model was developed. Three models, namely linear regression, convolutional neural network, and random forest models, were compared, and the random forest model was proposed as the best model. CONCLUSIONS : The evaluation revealed that the random forest model displayed the least error in the concentration estimation model for (PM2.5 and PM10) in all tunnel section cases. In addition, a practical application plan for the model developed in this study is proposed.

1. 서론
2. 연구방법론
3. 문헌 고찰
    3.1. 미세먼지 정의
    3.2. 기존 문헌 고찰
4. 데이터 구축 및 기초 분석
    4.1. 터널구간 교통량, 미세먼지 수집
    4.2. 터널구간 교통량, 미세먼지 수집
    4.3. 데이터 구축 및 기초통계 분석
5. 미세먼지 농도 추정 모형 개발
    5.1. 분석방법론
    5.2 초미세먼지(PM2.5) 농도 추정 모형 구축결과
    5.3. 미세먼지(PM10) 농도 추정 모형 구축결과
6. 결론
감사의 글

• 정도영(정회원 · 한국건설기술연구원 전임연구원) | Jung DoYoung 교신저자