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Latest revision as of 12:49, 12 February 2021
Abstract
As an important part of a smart city, intelligent transport can effectively reduce energy consumption and environmental pollution. Traffic flow forecasting provides a reliable traffic dispatch basis for intelligent transport, and most of the existing prediction methods only predict a single saturation or speed and do not use the saturation and speed in a unified way. This paper proposes a new traffic flow prediction method based on RNN-GCN and BRB. First, the belief rule base (BRB) is used for data fusion to obtain new traffic flow data, then the recurrent neural network (RNN) and graph convolution neural network (GCN) model is used to obtain the time correlation of the traffic data, and finally, the traffic flow is predicted by the topology graph. The experimental results show that the method has a better performance than ARIMA, LSTM, and GCN.
Document type: Article
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The different versions of the original document can be found in:
- http://dx.doi.org/10.1155/2020/7586154 under the license https://creativecommons.org/licenses/by/4.0/
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Published on 01/01/2020
Volume 2020, 2020
DOI: 10.1155/2020/7586154
Licence: Other
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