공공 및 교통 빅데이터 기반 코로나-19 확산 예측 및 도로정책연계 방안 연구Roadway Policy Linkage Based on Prediction of COVID-19 Spread Using Public and Transport Big Data
- Other Titles
- Roadway Policy Linkage Based on Prediction of COVID-19 Spread Using Public and Transport Big Data
- Authors
- 정정호; 권경주; 박성민; 강가원; 박준영
- Issue Date
- Apr-2024
- Publisher
- 한국도로학회
- Keywords
- Artificial Intelligence; Machine Learning; Traffic Indicator; Road Policy
- Citation
- 한국도로학회논문집, v.26, no.2, pp 125 - 132
- Pages
- 8
- Indexed
- KCI
- Journal Title
- 한국도로학회논문집
- Volume
- 26
- Number
- 2
- Start Page
- 125
- End Page
- 132
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/119120
- DOI
- 10.7855/IJHE.2024.26.2.125
- ISSN
- 1738-7159
2287-3678
- Abstract
- 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.
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