머신러닝을 통한 건축 도시 데이터 분석의 기초적 연구 - 딥러닝을 이용한 유동인구 모델 구축 -

• 영문명: Machine Learning Based Architecture and Urban Data Analysis - Construction of Floating Population Model Using Deep Learning -
• 발행기관: 한국BIM학회
• 저자명: 신동윤(Shin, Dong-Youn)
• 간행물 정보: 『KIBIM Magazine』9권 1호, 22~31쪽, 전체 10쪽
• 주제분류: 공학 > 건축공학
• 파일형태: PDF
• 발행일자: 2019.03.30

영문 초록

In this paper, we construct a prototype model for city data prediction by using time series data of floating population, and use machine learning to analyze urban data of complex structure. A correlation prediction model was constructed using three of the 10 data(total flow population, male flow population, and Monday flow population), and the result was compared with the actual data. The results of the accuracy were evaluated. The results of this study show that the predicted model of the floating population predicts the correlation between the predicted floating population and the current state of commerce. It is expected that it will help efficient and objective design in the planning stages of architecture, landscape, and urban areas such as tree environment design and layout of trails. Also, it is expected that the dynamic population prediction using multivariate time series data and collected location data will be able to perform integrated simulation with time series data of various fields.

목차

1. 서 론

2. 선행연구 및 사례

3. 건축, 도시정보 분석 특성 및 고려사항

4. 건축, 도시정보 예측 모델 구축

5. 결론 및 토론

6. 한계 및 향후 연구의 필요성

키워드

Machine learning Deep learning ANN Urban planning 머신러닝 딥러닝 ANN 도시 계획 건축 계획