A Novel Traffic Flow Forecasting Method Based on RNN-GCN and BRB
Joint Authors
Zhu, Hailong
Ma, Ning
Sun, Chao
Xie, Yawen
He, Wei
Zhu, Kaili
Zhou, Guohui
Source
Journal of Advanced Transportation
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-10-19
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Zhu, Hailong& Xie, Yawen& He, Wei& Sun, Chao& Zhu, Kaili& Zhou, Guohui…[et al.]. 2020. A Novel Traffic Flow Forecasting Method Based on RNN-GCN and BRB. Journal of Advanced Transportation،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1176036
Modern Language Association (MLA)
Zhu, Hailong…[et al.]. A Novel Traffic Flow Forecasting Method Based on RNN-GCN and BRB. Journal of Advanced Transportation No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1176036
American Medical Association (AMA)
Zhu, Hailong& Xie, Yawen& He, Wei& Sun, Chao& Zhu, Kaili& Zhou, Guohui…[et al.]. A Novel Traffic Flow Forecasting Method Based on RNN-GCN and BRB. Journal of Advanced Transportation. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1176036
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1176036