BROWSE
Detail
Conference Paper
Comparative Analysis of Machine Learning Algorithms to Urban Traffic Prediction
Cited 7 time in 
Download 4 time
Share 
Download 4 time
Share
- Authors
- Yong-Ju Lee, Okgee Min
- Issue Date
- 2017-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2017, pp.1035-1037
- Language
- English
- Type
- Conference Paper
- Project Code
- 17HS5200, Development of a Traffic Predictive Simulation SW for Improving the Urban Traffic Congestion, Ok Gee Min
- Abstract
- 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 Keywords
- 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