Road traffic flow prediction based on neural Network

The key to the implementation of urban traffic flow guidance system is to forecast the road traffic flow. This thesis mainly studies the prediction of traffic flow by neural network and the logical prediction of traffic flow. When the traffic flow information is regarded as a time series, the traffic flow can be regarded as a random time series, using the correlation between the data and the internal connection between the adjacent data. BP algorithm has a strong ability to deal with nonlinear problems, imitate bionic learning and self-organization, and occupies a certain advantage in dealing with nonlinear and uncertain traffic information. This paper mainly uses BP algorithm to build a traffic flow prediction model. In this paper, the traffic flow data of Minneapolis from 2012 to 2018 are preprocessed, and the traffic flow prediction model is used to make a better prediction of the traffic flow data of Minneapolis.