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Conference Paper Comparative Analysis of Machine Learning Algorithms to Urban Traffic Prediction
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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
DOI
https://dx.doi.org/10.1109/ICTC.2017.8190846
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