상세정보
학술대회
Comparative Analysis of Machine Learning Algorithms to Urban Traffic Prediction
- 발행일
- 201710
- 출처
- International Conference on Information and Communication Technology Convergence (ICTC) 2017, pp.1035-1037
- 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).
- 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