도시 빅데이터를 활용한 스마트시티의 교통 예측 모델

• 영문명: Big Data Based Urban Transportation Analysis for Smart Cities - Machine Learning Based Traffic Prediction by Using Urban Environment Data -
• 발행기관: 한국BIM학회
• 저자명: 장선영(Jang, Sun-Young) 신동윤(Shin, Dong-Youn)
• 간행물 정보: 『KIBIM Magazine』8권 3호, 12~19쪽, 전체 8쪽
• 주제분류: 공학 > 건축공학
• 파일형태: PDF
• 발행일자: 2018.08.30

영문 초록

The research aims to find implications of machine learning and urban big data as a way to construct the flexible transportation network system of smart city by responding the urban context changes. This research deals with a problem that existing a bus headway model is difficult to respond urban situations in real-time. Therefore, utilizing the urban big data and machine learning prototyping tool in weathers, traffics, and bus statues, this research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data is gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is implemented by the machine learning tool (RapidMiner Studio) and conducted several tests for bus delays prediction according to specific circumstances. As a result, possibilities of transportation system are discussed for promoting the urban efficiency and the citizens’ convenience by responding to urban conditions.

목차

1. 서 론

2. 문헌고찰 및 사례연구

3. 실시간 버스 데이터를 활용한 지연 예측 모델

4. 버스지연 예측에 대한 결과

5. 결론 및 논의

키워드

Big Data Bus Headway Prediction Machine Learning Public Transportation Smart City Information Architecture 빅데이터 머신러닝 버스 배차간격 예측 대중교통 스마트 시티 정보 건축