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

DOI

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.

Copyright

© 2017, 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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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  - Proceedings of the 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  - 10.2991/mecs-17.2017.14
ID  - Wang2016/06
ER  -