The short-term traffic flow prediction based on neural network
HU Wu-sheng, Yuanlin Liu, Li Li, Shujie Xin
Abstract
HU Wu-sheng, Yuanlin Liu, Li Li, Shujie Xin
Abstract
As we all know, to predict the short-term traffic flow accurately and efficiently is the premise and key of traffic management and control. Based on these existing study, this paper selected BP neural network model in which the traffic flow difference was taken as the input parameter, applied the thought of dynamic rolling prediction to design a new short-term traffic flow prediction method, and wrote the corresponding program. Then using the actual observation data of traffic flow presented the model structure, thought and calculation steps of this new method. The results show this method is feasibility, reliability, and of some practical value.
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As we all know, to predict the short-term traffic flow accurately and efficiently is the premise and key of traffic management and control. Based on these existing study, this paper selected BP neural network model in which the traffic flow difference was taken as the input parameter, applied the thought of dynamic rolling prediction to design a new short-term traffic flow prediction method, and wrote the corresponding program. Then using the actual observation data of traffic flow presented the model structure, thought and calculation steps of this new method. The results show this method is feasibility, reliability, and of some practical value.
Key concepts: Traffic flow (computer networking), Term (time), Computer science, Artificial neural network, Reliability (semiconductor), Premise, Traffic generation model, Key (lock)