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22 December 2021 Short-term traffic flow prediction model based on wavelet denoising and BP neural network
Chunling Ding, Yunfeng Chen
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Proceedings Volume 12058, Fifth International Conference on Traffic Engineering and Transportation System (ICTETS 2021); 1205820 (2021) https://doi.org/10.1117/12.2619965
Event: 5th International Conference on Traffic Engineering and Transportation System (ICTETS 2021), 2021, Chongqing, China
Abstract
With the rapid development of intelligent traffic systems (ITS) and the gradual improvement of people's living standards, traffic planning and traffic guidance have become a hot topic in the field of traffic research. Accurately predicting traffic flow in a short time is the key to realize intelligent traffic control system. In view of the historical time correlation of traffic flow data, a short-time traffic flow prediction model combining wavelet denoising and BP neural network (WDBPNN) is proposed. The model takes the traffic flow data of the first four time points of the current intersection as input to predict the traffic flow. Mean relative error (MRE), equality coefficient (EC) and accuracy are introduced as the evaluation indexes of the model. The model is trained and tested with the actual traffic flow data of a section of British motorway, and the experiment shows that the model can get good prediction results.
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Chunling Ding and Yunfeng Chen "Short-term traffic flow prediction model based on wavelet denoising and BP neural network", Proc. SPIE 12058, Fifth International Conference on Traffic Engineering and Transportation System (ICTETS 2021), 1205820 (22 December 2021); https://doi.org/10.1117/12.2619965
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KEYWORDS
Data modeling
Wavelets
Neural networks
Denoising
Information technology
Intelligence systems
Statistical modeling
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