PURPOSES : This study aimed to predict the number of future COVID-19 confirmed cases more accurately using public and transportation big data and suggested priorities for introducing major policies by region. METHODS : Prediction analysis was performed using a long short-term memory (LSTM) model with excellent prediction accuracy for time-series data. Random forest (RF) classification analysis was used to derive regional priorities and major influencing factors. RESULTS : Based on the daily number of COVID-19 confirmed cases from January 26 to December 12, 2020, as well as the daily number of confirmed cases in Gyeonggi Province, which was expected to occur on December 24 and 25, depending on social distancing, the accuracy of the LSTM artificial neural network was approximately 95.8%. In addition, as a result of deriving the major influencing factors of COVID-19 through random forest classification analysis, according to the number of people, social distancing stages, and masks worn, Bucheon, Yongin, and Pyeongtaek were identified as regions expected to be at high risk in the future. CONCLUSIONS : The results of this study can help predict pandemics such as COVID-19.

1. 서론
2. 선행연구
    2.1. 예측 분석에 관한 선행 연구 고찰
    2.2. 분류 분석에 관한 선행 연구 고찰
    2.3. 기존 연구와의 차별성
3. 분석 방법론
    3.1. (1단계) 데이터 수집 및 가공
    3.2. (2단계) LSTM 인공신경망 기반 경기도 내 코로나-19확진자 수 학습 및 검증
    3.3. (3단계) 랜덤포레스트 기반 코로나-19 확진자 수증감에 따른 주요 영향요인 도출
    3.4. (4단계) 주요 영향요인 변화에 따른 코로나-19 확진자 수 변화 예측
    3.5. (5단계) 경기도 지자체별 코로나-19 확진자 수 분류분석을 통한 우선순위 도출
4. 분석 결과
    4.1. LSTM 기반 코로나-19 확진자 수 학습 및 검증 결과
    4.2. 랜덤포레스트 기반 코로나-19 감염 주요 영향요인도출 결과
    4.3. 주요 영향요인의 변화에 따른 향후 코로나-19확진자 수 예측 결과
    4.4.경기도 지자체별 코로나-19 확진자 수 분류 분석을통한 우선순위 도출 결과
    4.5.경기도 지자체별 코로나-19 확진자 수에 따른 도로정책 연계방안 제안
5. 결론
REFERENCES

• 정정호(한국교통연구원 교통빅데이터연구본부 연구원) | Jeong Jeongho
• 권경주(한국도로공사 디지털계획처 대리) | Kwon Kyeongju
• 박성민(한국교통안전공단 모빌리티지원센터 선임연구원) | Park Seongmin
• 강가원(국토연구원 국토인프라 연구본부 연구원) | Kang Kawon
• 박준영(한양대학교 공과대학 교통·물류공학과 부교수) | Park Juneyoung (Associate Professor Department of Transportation and Logistics Engineering, Hanyang University, Gyeonggi 15588, Korea) Corresponding author