Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)

The application of ARIMA-RBF model in urban rail traffic volume forecast

Authors
Jiuran He, Bingfeng Si
Corresponding Author
Jiuran He
Available Online March 2013.
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/).

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Volume Title
Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
Series
Advances in Intelligent Systems Research
Publication Date
March 2013
ISBN
978-90-78677-61-1
ISSN
1951-6851
DOI
10.2991/iccsee.2013.416How to use a DOI?
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  -