The application of ARIMA-RBF model in urban rail traffic volume forecast
- DOI
- 10.2991/iccsee.2013.416How to use a DOI?
- Keywords
- Railway traffic, Passenger flow forecast, Combination Forecasting, RBF neural network, ARIMA model
- Abstract
Due to various factors, passenger flow has nonlinear characteristics. Autoregressive Integrated Moving Average Model (ARIMA model) is suitable for non-stationary time series forecasting while RBF neural network is a kind of forward neural network which has good approximation performance and is suitable for processing nonlinear problem. In this paper, we combine the ARIMA model and RBF neural network model to formulate the ARIMA - RBF model by analyzing passenger flow’ s temporal characteristics, the mechanism of ARIMA model with RBF model. We use the proposed model which used to forecast Beijing urban rail transit passenger flow and obtain a good prediction result.
- Copyright
- © 2013, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Jiuran He AU - Bingfeng Si PY - 2013/03 DA - 2013/03 TI - The application of ARIMA-RBF model in urban rail traffic volume forecast BT - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013) PB - Atlantis Press SP - 1662 EP - 1665 SN - 1951-6851 UR - https://doi.org/10.2991/iccsee.2013.416 DO - 10.2991/iccsee.2013.416 ID - He2013/03 ER -