Proceedings of the 2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017)

Traffic Flow Prediction Based on Combined Model of ARIMA and RBF Neural Network

Authors
Yuqiong Wang
Corresponding Author
Yuqiong Wang
Available Online June 2016.
DOI
https://doi.org/10.2991/mecs-17.2017.14How to use a DOI?
Keywords
traffic engineering, traffic flow prediction, Combined Model, ARIMA model, RBF neural network model
Abstract
In this paper, a combined model of ARIMA and RBF neural network is proposed by combined the good linear fit ability of ARIMA and the strong dynamic nonlinear mapping ability of RBF neural network. The velocity of microwave is predicted in real time with the consideration of the temporal characteristics of traffic flow by the models.The results indicate that the Mean Absolute Percentage Error of combined model is lower, and the goodness of fit of combined model is higher.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017)
Part of series
Advances in Engineering Research
Publication Date
June 2016
ISBN
978-94-6252-352-4
ISSN
2352-5401
DOI
https://doi.org/10.2991/mecs-17.2017.14How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yuqiong Wang
PY  - 2016/06
DA  - 2016/06
TI  - Traffic Flow Prediction Based on Combined Model of ARIMA and RBF Neural Network
BT  - 2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/mecs-17.2017.14
DO  - https://doi.org/10.2991/mecs-17.2017.14
ID  - Wang2016/06
ER  -