scholarly journals Application of the relevance vector machine to canal flow prediction in the Sevier River Basin

2010 ◽  
Vol 97 (2) ◽  
pp. 208-214 ◽  
Author(s):  
John Flake ◽  
Todd K. Moon ◽  
Mac McKee ◽  
Jacob H. Gunther
Keyword(s):  
River Basin ◽  
Relevance Vector Machine ◽  
Flow Prediction

scholarly journals Short-Term Traffic Flow Local Prediction Based on Combined Kernel Function Relevance Vector Machine Model

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Qichun Bing ◽  
Bowen Gong ◽  
Zhaosheng Yang ◽  
Qiang Shang ◽  
Xiyang Zhou
Keyword(s):  
Traffic Flow ◽  
Kernel Function ◽  
Prediction Method ◽  
Relevance Vector Machine ◽  
Guidance System ◽  
Short Term ◽  
Machine Model ◽  
Traffic Flow Prediction ◽  
Local Prediction ◽  
Flow Prediction

Short-term traffic flow prediction is one of the most important issues in the field of adaptive traffic control system and dynamic traffic guidance system. In order to improve the accuracy of short-term traffic flow prediction, a short-term traffic flow local prediction method based on combined kernel function relevance vector machine (CKF-RVM) model is put forward. The C-C method is used to calculate delay time and embedding dimension. The number of neighboring points is determined by use of Hannan-Quinn criteria, and the CKF-RVM model is built based on genetic algorithm. Finally, case validation is carried out using inductive loop data measured from the north–south viaduct in Shanghai. The experimental results demonstrate that the CKF-RVM model is 31.1% and 52.7% higher than GKF-RVM model and GKF-SVM model in the aspect of MAPE. Moreover, it is also superior to the other two models in the aspect of EC.

Sign in / Sign up

Export Citation Format

Share Document