Title :
Short-Term Prediction of Railway Passenger Flow Based on RBF Neural Network
Author :
Huang, Yuanchun ; Pan, Haize
Author_Institution :
Coll. of Urban Railway Transp., Shanghai Univ. of Eng. Sci., Shanghai, China
Abstract :
RBF network has a high degree of accuracy when it functions with complex issues for the functional approach. Because of the structural characteristics of the network, it can learn faster than the conventional BP algorithm. This paper propose a short-term passenger forecasting method based on RBF neural which has the characteristics of the underlying data and short-term forecast requirements.
Keywords :
backpropagation; radial basis function networks; railway engineering; transportation; RBF neural network; conventional BP algorithm; railway passenger flow; short term passenger forecasting method; short term prediction; structural characteristics; Artificial neural networks; Biological system modeling; Forecasting; Neurons; Rail transportation; Time series analysis; Training; RBF; Railway; short-term forecast;
Conference_Titel :
Computational Sciences and Optimization (CSO), 2011 Fourth International Joint Conference on
Conference_Location :
Yunnan
Print_ISBN :
978-1-4244-9712-6
Electronic_ISBN :
978-0-7695-4335-2
DOI :
10.1109/CSO.2011.240
