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학술대회 Comparative Analysis of Machine Learning Algorithms to Urban Traffic Prediction
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저자
이용주, 민옥기
발행일
201710
출처
International Conference on Information and Communication Technology Convergence (ICTC) 2017, pp.1035-1037
DOI
https://dx.doi.org/10.1109/ICTC.2017.8190846
협약과제
17HS5200, 도시 교통 문제 개선을 위한 클라우드 기반 트래픽 예측 시뮬레이션 SW 기술 개발, 민옥기
초록
Machine learning is currently a hot research topic and applied in intelligence transportation system to discover new valuable knowledge and patterns. In this paper, we extract trajectory information from popular traffic simulator and apply it into four different machine learning methods. In the case of the Gangnam district in Seoul, the Gradient Boosting Regression has better fit with lower values of RMSE (Root Mean Square Error).
키워드
Intelligence transportation system, Machine learning, Traffic prediction
KSP 제안 키워드
Comparative analysis, Different machine learning methods, Machine Learning Algorithms, Root mean square(RMS), Traffic simulator, gradient boosting, mean square error(MSE), root mean square error, trajectory information, transportation system, urban traffic prediction